SLRSurvey References

# A Survey on Green Computing in Video Games: The Dawn of Green Video Games

Carlos Pérez<EMAIL_ADDRESS>0009-0008-9786-656X Universidad San JorgeCampus
Universitario, Autovía Mudéjar, km. 299Villanueva de GállegoZaragozaSpain50830
Universitat Politècnica de ValènciaCamí de Vera, s/nValenciaSpain46022 ,  Ana
C. Marcén<EMAIL_ADDRESS>0000-0002-5054-4618 Universidad San JorgeCampus
Universitario, Autovía Mudéjar, km. 299Villanueva de GállegoZaragozaSpain50830
,  Javier Verón<EMAIL_ADDRESS>0000-0003-2516-2105 Universidad San JorgeCampus
Universitario, Autovía Mudéjar, km. 299Villanueva de GállegoZaragozaSpain50830
Universitat Politècnica de ValènciaCamí de Vera, s/nValenciaSpain46022 and
Carlos Cetina<EMAIL_ADDRESS>0000-0001-8542-5515 Universidad San JorgeCampus
Universitario, Autovía Mudéjar, km. 299Villanueva de GállegoZaragozaSpain50830

(2018)

###### Abstract.

Today, the large number of players and the high computational requirements of
video games have motivated research on Green Video Games. We present a survey
that provides an overview of this recent research area. A total of 2,637
papers were reviewed, selecting 69 papers as primary studies for further
analysis. Through a detailed analysis of the results, we propose a new way to
define the Green Video Game issues based on motivation, device, and layer of
the primary studies. Then, we analyze the different applied techniques, the
limitations and levels of evidence, and specific aspects of video games.

Green Computing, Green Software, Video Games, Energy-Efficiency, Green Video
Games

††copyright: acmcopyright††journalyear: 2018††doi: XXXXXXX.XXXXXXX††journal:
POMACS††journalvolume: 37††journalnumber: 4††article: 111††publicationmonth:
8††ccs: General and reference Surveys and overviews††ccs: Hardware Impact on
the environment††ccs: Applied computing Computer games

##  1\. Introduction

Video games are no longer a niche hobby but a mass phenomenon. Today, the
number of video game players is estimated to be approximately three billion
worldwide (ExplodingTopics2023, ) (Statista2023, ). This figure is expected to
continue to grow considerably in the coming years.

Video game players use everything from game consoles that are created
specifically for playing video games to computers or even mobiles. These
computers or mobiles are usually high-performance devices and are known as
gaming computers or mobiles. In addition, it is very common for video games to
also need a network infrastructure and servers to get from the popular online
games to the latest cloud gaming platforms such as Xbox Cloud Gaming (xbox, )
or Geforce Now (geforce, ).

The combination of the large number of players (i.e., users) with the high
computational requirements of modern video games has motivated recent research
work in the field of what we call Green Video Games. We consider Green Video
Games to be the specialization of Green Computing that addresses the
peculiarities of video games. Therefore, Green Video Games are video games
that are designed, implemented, and made available to players in a way that
limits the harmful impact on the environment.

This idea of Green Video Games has also recently gained prominence in the
industry. In early 2023, Microsoft released an update to its Xbox game console
that, in Microsoft’s own words, made it ”the first Carbon Aware Console”
(xboxFirst, ). At the moment, carbon awareness capabilities are limited to
scheduling game, app, and operating system updates based on local carbon
intensity data. However, carbon intensity data is potentially beneficial for
game developers to develop Green Video Games.

To date, surveys have not addressed the sustainability of video games per se.
The work in (gutierrez2023green, ) highlights that there are two perspectives
for analyzing energy consumption: Green-IN and Green-BY. The first one deals
with improving the energy consumption of a field per se, e.g., video games,
whereas the second one is focused on the application of a field (video games)
to improve sustainability in any context. It is possible to find some surveys
on GREEN-BY video games. For example, (johnson2017gamification, ) conducted a
systematic review to assess the effectiveness of gamification and serious
games in impacting domestic energy consumption. However, we did not find any
surveys on GREEN-IN video games, which is the perspective of this work.

Our paper is the first to study the current state of Green Video Games. Based
on the analysis of 69 papers published between 1995 and 2022, this paper
reports on the work done in Green Video Games. In the first part of our
review, we propose a way to classify current and future work on Green Video
Games. Then, the survey results provide an up-to-date, holistic review of the
field of Green Video Games. This article also aims to serve to identify
recommendations for practitioners, outstanding research challenges, and
several potential avenues of future research for Green Video Games.

Specifically, our survey reveals that this first wave of Green Video Game work
includes work that leverages energy-efficient techniques from the hardware and
networking communities for video games, e.g., dynamically distributing
computational tasks between mobile devices and servers (resource allocation)
or adapting CPU or GPU frequency to the tasks being performed (frequency
scaling). Much of this work is motivated by cost, user satisfaction, and user
experience. For example, users may not be satisfied if the battery of their
mobiles is rapidly consumed by a video game. However, it is an opportunity for
the community to further develop video games to have a minimal environmental
impact.

There are also efforts on the software side to improve efficiency in basic
algorithms for video games such as pathfinding, tree search, or image anti-
aliasing. We have not found any explanation of why there is a lack of effort
on essential artifacts in video game development such as engines in the
literature. Very few papers address physics engines, and only two works
address a proprietary engine. No paper addresses either the most popular game
engines such as Unreal and Unity or even sets of essential physics, graphics,
or sound libraries (e.g., ogre3d or bullet). We can only theorize (from the
trajectories of the researchers) that, at this early stage, the Green Video
Game community is probably made up of ”green researchers” who have approached
video games, rather than video game researchers who have approached the green
aspect.

Instead of exploring a top-down strategy where techniques focus on the art and
design layers of video games, most studies explore a bottom-up strategy where
techniques focus on the hardware, network, or software layers. However, design
artifacts (e.g., of corridors or the location of key elements) can be useful
to answer questions such as what the influence of the game engine on energy
consumption is. Even the influence of the engine per content type can be
studied, since in the field of video games the content types (NPC, textures,
maps, sounds, etc.) are almost like subfields. In all cases, artifacts in the
top layers are essential to video games and are underexplored from a green
perspective, which is an opportunity.

Finally, as far as video game genres are concerned, the genre that has
received the most attention is Massively Multiplayer Online Games (MMOG). We
have found no reason for other genres to be neglected, so it is an opportunity
for the community to explore other popular genres such as First-Person Shooter
(FPS) or Real-Time Strategy (RTS), especially when genres like these are also
among the most played nowadays.

The remainder of this paper is structured as follows: Section 2 describes some
concepts and related works related to the survey. Section 2.3 describes the
research method. Section 3 presents the results of the survey performed
according to the research questions. Section 4 and Section 5 discuss the
results and the threats to validity, respectively. Finally, Section 6
summarizes the main conclusions.

##  2\. Background

We consider Green Video Games as the specialization of Green Computing that
addresses the peculiarities of video games. Therefore, to better understand
Green Video Games, this section briefly describes some concepts of Green
Computing. This section also serves to describe the existing surveys that are
related to the current research field.

###  2.1. Green Computing

Green Computing is responsible for designing, manufacturing, using, and
disposing of computers, servers, and hardware such as monitors, printers,
storage devices, and networking and communications systems in order to
efficiently and effectively consume energy with minimal or no impact on the
environment (chandrakant2013green, ). There are five main areas in Green
Computing (dhaini2021green, ):

  * •

Green Cloud Computing refers to the environmental benefits that cloud-based
services can offer, for example, saving energy by using cloud computational
resources dynamically on demand, as virtual machines.

  * •

Mobile Computing refers to the energy-efficiently of mobile devices that allow
access to information from anywhere, at any time.

  * •

Green Software refers to software that is developed to limit energy
consumption and have minimal environmental impact.

  * •

Green Data Centers refers to the design, protocols, equipment, infrastructure,
and algorithms that make data centers energy-aware and efficient, and minimize
carbon emissions.

  * •

Educational Sector refers to the importance of introducing the principles of
green computing, and making both students and practitioners aware of its
importance.

There is no specific area for video games studies. The works on video game are
distributed in these five areas. For example, Nery201997 seeks to reduce the
energy consumption of pathfinding algorithms, whi ch corresponds to the Green
Software area. The work in han2020virtual proposes a distributed algorithm to
optimize virtual machine placement through resource competition, which
corresponds to the Green Cloud Computing area. The work in Muhuri20182311
focuses on increasing the battery life of mobile devices to improve user
satisfaction, which corresponds to the Mobile Computing area. The work in
Behiya202084 evaluates the influence of controlling the virtual machine
central process unit (vCPU) on the energy consumption of data centers, which
corresponds to the Green Data Centers area. The work in (casals2017promoting,
) develops a serious game to make people aware of energy consumption and
carbon emission reduction, which corresponds to the Educational Sector.

Therefore, if researchers wanted to obtain an overview of video games in green
computing, they would have to study all of the previously mentioned areas.
However, this work can help researchers obtain a deeper perspective on Green
Video Games by considering Green Video Games as a new area of green computing
that has emerged and grown rapidly in recent years.

###  2.2. Related Work

After a careful review of the related literature, we were unable to find other
surveys that put the focus on Green Video Games. The closest works related to
this one can be found in surveys studying how to increase awareness towards a
more sustainable future through serious games.

Serious games are video games whose primary objective is to teach rather than
to entertain. For this reason, there are many works that take advantage of
serious games to raise energy awareness in an attractive and relaxed way. In
fact, it is possible to find several surveys that review the related
literature. In (johnson2017gamification, ), a systematic review was conducted
to assess empirical support for the effectiveness of gamification and serious
games in impacting domestic energy consumption. The authors provide varying
degrees of evidence of positive influence found for behaviour, cognitions,
knowledge and learning, and the user experience. The work in
(katsaliaki2012survey, ) conducts a review of serious games on sustainable
development. Among its objectives are to improve the feature-set of these
games and to increase knowledge around sustainable development strategies. The
work in (wu2020serious, ) reviews serious games as an engaging medium for
building energy consumption. The investigation specifically focuses on the
potential impact that serious games can have on changing the domestic
practices of householders, in a safe, fun, and interactive environment. The
review conducted in (morganti2017gaming, ) shows that serious games and
gamification have been used in three different areas related to energy
efficiency: environmental education, consumption awareness, and pro-
environmental behaviours.

Therefore, some surveys focus on providing an overview of how green education
benefits from serious games. However, our survey focuses on the opposite
perspective: how video games benefit from green approaches or techniques.

In addition, some surveys mention energy consumption in video games. The work
in (chalal2022visualisation, ) conducted a systematic review of a wide range
of energy eco-feedback visualization techniques. One of the categories used to
report the techniques is game-based visualization. In
(sorrell2020digitalisation, ), the review links the specific factors that
determine the environmental impacts of information and communication
technologies to the magnitude of the impacts on energy consumption and carbon
emissions. One of the reviewed categories is e-videos and games. However, the
studies in this category present a rather mixed picture and suggest less
potential for energy saving than the other categories.

Neither of these two articles delves into the ecological perspective of video
games or offers a global vision of Green Video Games. Nevertheless, these two
articles that were published in the last four years consider video games as a
specific category. This may be indicative of the growth of video games in
Green Computing.

###  2.3. Research Method

This survey was conducted using formal and reproducible steps to identify,
evaluate, and interpret the scientific studies that are related to Green
Computing in the development of video games. Specifically, the research method
used follows the best practices and guidelines for systematic literature
review research (kitchenham2007guidelines, ; kitchenham2013systematic, ;
kitchenham2015evidence, ). This section describes the review method and how it
was applied to address the research questions raised.

###  2.4. Research questions

Research questions (RQs) are motivated by the need to provide an overview of
the Green Video Game area. This survey aims to answer the following RQs:

  1. RQ1:

What issues are addressed in the field of Green Video Games?

In Green Computing, some works have focused on identifying relevant green
issues and evaluating different approaches to these problems (li2011survey, ;
jain2020novel, ). The main goal of RQ1 is to identify which of these issues
are addressed in the Green Video Game area. In addition, RQ1 also serves to
adapt existing Green Computing taxonomies to cover video game issues or to
create a new taxonomy for Green Video Games.

  2. RQ2:

Which green techniques are applied for video game development?

The main goal of RQ2 is to identify which green techniques are used to design,
develop, implement, and make available video games. This will provide an
overview of the issues that are addressed in Green Video Games and the
techniques that are used to solve them. RQ2 also serves to identify which
techniques can potentially be used for solving other types of issues in Green
Video Games.

  3. RQ3:

What are the limitations of the current Green Video Game studies?

RQ3 is motivated by the interest in identifying future lines of work. Green
Video Games have been a rapidly growing research area in recent years.
Therefore, researchers and practitioners would benefit greatly from an
overview, especially regarding current problems and future research
directions. The main goal of RQ3 is to identify open challenges and possible
future research directions for improvement. RQ3 also serves to learn about the
maturity of studies published taking into account the level of evidence
provided for the current works in the research area.

  4. RQ4:

What other video game aspects should be considered in developing Green Video
Games? Finally, there are specific aspects of video games that are largely
neglected in Green Computing, for example, the genre of video games. A single-
player offline game does not require the same infrastructure and energy as a
MMOG. For this reason, the main goal of RQ4 is to evaluate other video game
aspects that should be considered in order to improve the sustainability of
video games. RQ4 also serves to open future discussion lines about Green Video
Games.

###  2.5. Search Strategy

A database search was adopted to collect the available published literature
that is relevant to the RQs. Specifically, we formulated our search strategy
based on the guidelines provided in (kitchenham2007guidelines, ;
kitchenham2013systematic, ; kitchenham2015evidence, ). The strategy was
composed of the following five steps:

  1. (1)

A search string was used to collect the primary studies present in the digital
sources. We found 2,637 articles through the search string.

  2. (2)

The entire list of retrieved articles was filtered to exclude non-relevant
articles. This step was conducted by one author of this paper, who applied the
exclusion criteria considering the title, keywords, and abstract of the
retrieved articles. As a result, 1,020 articles were discarded because they
met at least one of the exclusion criteria. The remaining 1,617 articles were
selected for the next step.

  3. (3)

The articles that were selected in the previous step were filtered to include
only the relevant articles to answer the RQs. This step was conducted by two
authors of this paper, who applied the inclusion criteria considering the
abstract and full text of the selected articles. As a result, 47 articles were
included because they met all of the inclusion criteria, which was about 3% of
the papers found in the first step.

  4. (4)

To extend the scope of research and to minimize the risk of missing relevant
studies, we applied snowballing to identify additional sources
(wohlin2014guidelines, ). Specifically, we conducted backward snowballing of
all of the studies included in the previous step. Then, two authors reapplied
the exclusion/inclusion criteria based on the title, abstract, and keywords of
all of the new articles found (i.e., 913 articles). As a result, 22 articles
were selected for further analysis.

  5. (5)

In the last step, we performed the data collection from the primary studies.
This step was not used for filtering purposes, so none of the primary studies
were included or excluded in this step. Thus, our survey is based on 69
papers, 47 from the initial query and 22 stemming from snowballing. These 69
papers constitute the primary studies of our survey.

To ensure accuracy, the selection process was double-checked by another author
of this paper. In case of disagreement, the authors held a discussion to reach
a consensus. As a result, the 69 papers that were selected as final studies
were confirmed as such. Fig. 1 shows the search strategy and the number of
papers resulting from each of its steps. Moreover, the following sections
provide more details about the search string, the digital sources, the
selection criteria (i.e., exclusion and inclusion), and the data collection.

Figure 1. Overview of the search strategy

####  2.5.1. Search String

The terms for the search strings were extracted following the steps suggested
by Kitchenham and Charters (kitchenham2007guidelines, ). First, we used PICO
(Population, Intervention, Comparison, and Outcomes) criteria to derive the
major terms from the research questions.

  * •

Population: In software engineering, the population may refer to a specific
software engineering role, category of software engineer, an application area,
or an industry group (kitchenham2007guidelines, ). In our work, the population
consists of terms in the video game area.

  * •

Intervention: In software engineering, intervention refers to a software
methodology, tool, technology, or procedure that addresses a specific issue
(kitchenham2007guidelines, ). In our work, the intervention corresponds to
green computing terms.

  * •

Comparison: In software engineering, the comparison is the software
engineering methodology, tool, technology, or procedure against which the
intervention is being compared (kitchenham2007guidelines, ). In our work, the
comparison is not applied. The goal of this survey is to characterize the
articles that tackle Green Computing in video games.

  * •

Outcomes: In software engineering, the outcomes should relate to factors of
importance to practitioners (kitchenham2007guidelines, ). In our work, the
outcomes are terms related to practices, solutions, techniques, or methods
considering Green Video Games.

Taking into account the RQs, the identified search terms were video games (RQ1
and RQ2), Green Computing (RQ3), and techniques (RQ4). These terms were
grouped into three sets:

  * •

Video game terms: those search terms directly related to the population (i.e.,
video game*).

  * •

Green terms: those search terms directly related to the intervention (i.e.,
Green Computing).

  * •

Approach terms: those search terms directly related to the outcomes (i.e.,
techniques).

Then, we found alternative spellings and/or synonyms. In the case of video
game terms, we included computer game* and videogame*. In the case of Green
terms, we included green software, energy efficiency, and energy consumption.
In the case of Approach terms, we included practices.

We then verified the search terms that other relevant reviews used in their
search strings. Video game terms were extended based on the search terms that
were used in review articles related to video games ((hall2012health, ;
connolly2012systematic, ; boyle2013systematic, ; stanitsas2019facilitating,
)). Green terms and Approach terms were extended based on the search terms
that were used in review articles related to Green Computing
((salam2016identification, ; salam2016developing, ; rashid2015green, ;
stanitsas2019facilitating, )). We did not discard any term found in the search
strings. Therefore, all of the new terms that were related to this work were
included as terms of our search string.

Table 1. Search terms Video game terms |  Green terms |  Approach terms
---|---|---
Video game*, Computer game*, Videogame*, Digital games, Digital video games, Virtual games, Serious gam*, Simulation gam*, Game based learning, MMOG, MMORPG, MUD, Online games, Gamification, Green games, Sustainable Development games, Environmental games |  Green computing, Green software, Energy efficiency, Energy consumption, Sustainable software, Green software development, Green IT, Greener software, Green software engineering, Sustainability, Triple bottom line, Education for sustainability, Sustainable management, Sustainable development |  Technique*, Practice*, Solution*, Method*

Table 1 shows the search terms for each one of the sets: Video game terms,
Green terms, and Approach terms. From this table, we used boolean operators to
construct the search string. Specifically, all Video game terms were combined
by using the boolean ’OR’ operator; all Green terms were combined by using the
boolean ’OR’ operator; and all Approach terms were combined by using the
boolean ’OR’ operator. Then, we combined the Video game terms, the Green
terms, and the Approach terms by using the Boolean ’AND’ operator, which
implies that, for an article to appear in our search, it needs to include one
of the terms for Video game, one of the terms for Green Computing, and one of
the terms for Approach. The final search string is reported below:

(”Video game*” OR ”Computer game*” OR ”Videogame*” OR ”Digital games” OR
”Digital video games” OR ”Virtual games” OR ”Serious gam*” OR ”Simulation
gam*” OR ”Game based learning” OR ”MMOG” OR ”MMORPG” OR ”MUD” OR ”Online
games” OR ”Gamification” OR ”Green games” OR ”Sustainable development games”
OR ”Environmental games”) AND (”Green computing” OR ”Green software” OR
”Energy efficiency” OR ”Energy consumption” OR ”Sustainable software” OR
”Green software development” OR ”Green IT” OR ”Greener software” OR ”Green
software engineering” OR ”Sustainability” OR ”Triple bottom line” OR
”Education for sustainability” OR ”Sustainable management” OR ”Sustainable
development”) AND (”Technique*” OR ”Practice*” OR ”Solution*” OR ”Method*”)

####  2.5.2. Digital Sources

The digital sources considered in the survey were IEEE, ACM Digital Library,
Scopus, and Web of Science. These sources were selected based on the
experience reported by Dyba et al. (dyba2007applying, ) and Kitchenham and
Brereton (kitchenham2013systematic, ). Specifically, these works reported that
the use of IEEE and ACM as well as two indexing sources is sufficient
(petersen2015guidelines, ).

This review was carried out in 2023. The search for articles in the four
sources was conducted in January 2023. Therefore, the resulting articles
belong to 2022 or earlier. Table 2 shows the number of search results per
source and the number of selected studies at the end of the search process.

Table 2. Sources and search results Source | # Total studies found | # Primary studies
---|---|---
IEEE | 164 | 15
ACM | 7 | 0
Scopus | 1,299 | 24
Web of Science | 1,167 | 8

Note that Table 2 does not reflect the duplicity of papers: a paper can be
found in several sources. Due to the search process followed in this work,
Scopus papers were selected first, discarding duplicate papers from the other
sources. The search then continued by orderly selecting WOS papers, IEEE
papers, and ACM papers. With regard to the duplicity of papers, 51% of the 47
primary studies can be found in Scopus, 17% of the 47 primary studies can be
found in Web of Science, 32% of the 47 primary studies can be found in IEEE,
and 0% of the 47 primary studies can be found in ACM. Hence, although most of
the primary studies were found in Scopus, it is also possible to find them in
other sources.

###  2.6. Inclusion and Exclusion Criteria

To answer our research questions, the retrieved articles were evaluated
according to a series of exclusion and inclusion criteria. The articles that
met one of the exclusion criteria were discarded for our review. The following
criteria state the conditions for the exclusion of an article:

  * •

Articles that are duplicates of other articles.

  * •

Articles that are not written in English.

  * •

Articles presenting summaries of conferences or editorials.

  * •

Articles presenting non-peer-reviewed material.

  * •

Articles presenting review articles (e.g., surveys or systematic literature
reviews).

On the other hand, the inclusion criteria were applied to include only those
articles that were focused on Green Video Games. For this reason, the
inclusion criteria ensure that the articles belong to the computer science
area, that the articles are related to Green Computing, and that the articles
involve the use of video games to a greater or lesser extent.

In addition, when reviewing studies on Green Video Games, it is possible to
find two different perspectives: Green-IN and Green-BY (gutierrez2023green, ).
The first one deals with improving the energy consumption of video games per
se. For example, choi2021optimizing focuses on improving the energy efficiency
of game applications running on mobile devices. In contrast, the second one
focuses on the application of video games to improve sustainability in any
context. For example, (bang2006powerhhouse, ) presents a computer game called
PowerHouse, which uses video games to influence behaviors associated with
energy use and promotes an energy-conscious lifestyle among teenagers. Our
work focuses on the first perspective (GREEN-IN), so we have reflected this
fact in the inclusion criteria.

The following criteria state the conditions for the inclusion of an article:

  * •

Articles that are in the fields of computer science or engineering.

  * •

Articles that are related to Green Computing.

  * •

Articles that are related to video games.

  * •

Articles that focus on improving the energy consumption of video games.

To include an article, the article must meet all of the inclusion criteria.
The articles that met the inclusion criteria are the ones selected for our
review (primary studies). The number of excluded and included articles is
shown in Fig. 1.

###  2.7. Data Extraction

The data extraction form was devised to collect data from the primary studies.
Specifically, two researchers read the full papers in parallel to collect the
data. Table 3 shows the information collected for each primary study.

Table 3. Data Extraction Form # | Field | Research question
---|---|---
F1 | Author | n/a
F2 | Year | n/a
F3 | Title | n/a
F4 | Venue | n/a
F5 | Keywords | n/a
F6 | Abstract | n/a
F7 | Citation Count | n/a
F8 | Green Computing Issues defined in (li2011survey, ) | RQ1
F9 | Green Computing Issues defined in (jain2020novel, ) | RQ1
F10 | Green Video Games Motivations | RQ1
F11 | Green Video Games Devices | RQ1
F12 | Green Video Games Layers | RQ1
F13 | Green Techniques | RQ2
F14 | Level of Evidence | RQ3
F15 | Limitations | RQ3
F16 | Artifacts | RQ4
F17 | Genre of Video Games | RQ4
F18 | Content of Video Games | RQ4

The data extraction was divided into five parts:

  1. (1)

Demographic information (F1 to F7): For each paper, the meta-information was
extracted for both documentation purposes and the identification of the
contribution and quality of the reviewed articles. Fig. 5, Fig. 5, Fig. 5, and
Fig. 5 show the distributions of the primary studies concerning publication
year, publication type, and number of citations. Fig. 5 and Fig. 5 show that
Green Computing in video games has gained popularity in the last decade, with
a significant increase in the number of studies published in journals from
2014 to 2022.

Figure 2. Distribution of primary studies over publication year

Figure 3. Distribution of primary studies over publication type

Figure 4. Distribution of primary studies over type and year of publication

Figure 5. Distribution of primary studies over number of citations and year of
publication

  2. (2)

Data for RQ1 (F8 to F12): For each paper, we extracted the data to answer RQ1.
Each of these fields and their categories are detailed below.

F8 considers the taxonomy in (li2011survey, ) to identify the issue tackled in
each reviewed paper. According to this taxonomy, the research articles address
three types of issues in Green Computing:

     * •

Modeling and evaluation of energy efficiency focuses on investigating the
relationship between energy efficiency and other impact factors, in addition
to building the theoretical model and its energy efficiency evaluation
mechanism. This problem covers all aspects of computer systems including
chips, system architecture, compilers, operating systems, communication
networks, applications, etc.

     * •

Energy awareness focuses on enabling computer systems to run in a low power-
consumption manner under a broader range of conditions. Specifically, energy-
awareness strategies involve techniques in how systems and applications
respond to the state changes and adjust the behaviors of system components.

     * •

Green networking focuses on all network infrastructure design actions that
have an impact on its energy efficiency.

F9 considers another taxonomy described in (jain2020novel, ) to identify the
issue tackled in each reviewed paper. This taxonomy is more recent and
provides a larger number of issues for Green Computing. Specifically, this
taxonomy identifies seven issues for Green Computing:

     * •

Product longevity focuses on extending the lifetime of the equipment, taking
into account not only the manufacturing but also its upgradeability and
maintainability.

     * •

Data center design focuses on improving the energy efficiency of data centers,
where large amounts of energy are used primarily for information technology
systems, environmental conditions, air management, cooling systems, and
electrical systems.

     * •

Software and deployment optimization focuses on algorithmic efficiency as well
as resource allocation, terminal servers, and virtualization.

     * •

Power management focuses on different techniques that allow the control of the
energy consumed by the components of computer. This includes techniques such
as DVFS (Dual voltage Frequency scaling) or the selective disconnection of
different components when possible.

     * •

Materials recycling focuses on the recycling of computer equipment, especially
those components with potentially toxic or hazardous materials (e.g., lead,
mercury, and hexavalent chromium).

     * •

Telecommuting focuses on assessing the energy impact of moving from a face-to-
face to a telecommuting work model.

     * •

Telecommunication network devices energy indices focuses on researching the
relationship between network technology and environmental impact.
Specifically, some studies try to identify the key energy indices that allow a
relevant comparison between different devices (network elements).

The taxonomies used in F8 and F9 are oriented to Green Computing in general
and not to the specific issues of video games. For example, some video games
need to render 3D scenes, which requires determining which parts of every
object are visible in the final image. This has an important impact on the
energy efficiency of a GPU and the memory, so there are works that focus on
studying the trade-off between energy consumption and application performance,
such as Corbalan-Navarro20224375. However, the trade-off between energy
consumption and application performance is not an issue in the Green Computing
taxonomies.

In addition, identifying issues in green video games can be confusing because
you need to differentiate between motivation and solution. For example, the
motivation of 7351775 considers that the battery in mobile devices is a
critical reason to control consumption, so it proposes an energy efficiency
evaluation mechanism. Thus, according to its motivation, the study would be
classified as a Battery life issue, but according to its solution, it would be
classified as a Modelling and measurement of energy consumption issue.

Therefore, in Green Video Games, some issues cannot be classified using the
Green Computing taxonomies and the term issue is ambiguous if we do not
differentiate between motivation and solution. For these reasons, F10, F11,
and F12 were used to collect the main issues tackled from a video game
perspective.

F10 considers the motivation. Obviously, some of the motivations are based on
the Green Computing issues from the previous taxonomies, but others may be new
motivations for the field of Green Video Games.

In contrast, F11 and F12 focus on the solution. Since F11 considers the
devices that are used to test the solution (i.e., mobile devices, handheld
devices, wearable devices, computers, etc.), it focuses on devices to identify
whether or not the proposed solution is device-specific.

Moreover, F12 considers the layers of the proposed solution: Hardware,
Network, Software, Design, and Art. For example, The work in 7351775 proposes
a memory-aware cooperative CPU-GPU dynamic voltage and frequency scaling
governor. Therefore, this solution affects the hardware layer.

  3. (3)

Data for RQ2 (F13): For each paper, we extracted the green techniques used to
design, develop, implement, and make available video games in order to answer
RQ2. To do this, the techniques are collected using natural language.

  4. (4)

Data for RQ3 (F14 to F15): For each paper, we extracted the data to answer
RQ3. Specifically, F14 records the evidence level, which is used to check the
maturity of the technique, practice, solution, or method presented in the
paper. F14 records the limitations of the primary studies in order to identify
open issues and possible future research directions for improvement.

While the data for F14 was collected using natural language, the data for F13
was collected using a scale. We adapted the scale that is described in
(galster2013variability, ), which is based on the levels of study design
proposed by (kitchenham2004procedures, ). This scale has five levels: (0) no
evidence, (1) evidence from demonstration or toy examples, (2) evidence from
expert opinions or observations, (3) evidence from academic studies, (4)
evidence from industrial studies, and (5) evidence from industrial practice.

Similarly, our scale has four evidence levels: (0) no video game, (1) toy
example or no commercial video game, (2) specific video game, and (3) multiple
video games. The weakest evidence level of our scale indicates that the paper
does not present any evidence (i.e., the presented technique, practice,
solution, or method is not evaluated in a video game). Moreover, the strongest
evidence level of our scale indicates that the paper presents strong evidence
(i.e., the presented technique, practice, solution, or method is evaluated in
multiple video games).

  5. (5)

Data for RQ4 (F16 to F18): For each paper, we extracted the data to answer
RQ4, which considers different aspects of video games that are largely
neglected in Green Computing. Specifically, F16 records which artifacts of the
video games are considered for the primary studies, such as game engines. F17
records the video game genres, such as MMOG. F18 records which content of the
video games is considered to improve sustainability in video games, such as
maps, audio, or textures. The data for these fields was collected using
natural language.

##  3\. Results

This section presents the outcomes of the survey based on RQs. The primary
studies are listed in the Survey References section, which comes after the
bibliography section of this work.

In addition, the results of the whole search process, step by step, are
available as supplementary material in https://svit.usj.es/gvg-survey/.
Specifically, this material contains the results of the selection process as
well as the results of the snowballing and data collection.

###  3.1. RQ1. What issues are addressed in the field of Green Video Games?

Following the taxonomy described in (li2011survey, ), 15 primary studies focus
on the modeling and evaluation of energy efficiency, 7 primary studies focus
on energy-awareness, and 16 primary studies focus on green networking.
However, 31 primary studies tackled green issues that are not considered by
this taxonomy. For this reason, 44,93% of the primary studies in Fig. 7 are
not categorized.

In contrast, following the taxonomy described in (jain2020novel, ), 3 primary
studies focus on Data center design, 9 primary studies focus on Software and
deployment optimization, 23 primary studies focus on power management, and 13
primary studies focus on Telecommunication network devices energy indices.
There are no primary studies that focus on Product longevity, material
recycling, and telecommuting. Moreover, there are still 21 primary studies
that are not categorized because they tackled green issues that are not
considered by the taxonomy.

Fig. 7 and Fig. 7 show which Green Computing issues are addressed in the Video
Game area, regarding the issues defined in (li2011survey, ) and
(jain2020novel, ), respectively. According to these figures, most of the
primary studies focus on power management. However, the taxonomies do not
cover all of the green issues that can be encountered in the context of video
games. In addition, it could be discussed or assessed whether the categories
that have a percentage of 0% should be considered as future lines of research
in this context.

Figure 6. Primary studies classified by the Green Computing issues described
in (li2011survey, )

Figure 7. Primary studies classified by the Green Computing issues described
in (jain2020novel, )

Since the Green Computing issues do not cover all the primary studies, F10 in
data extraction allows us to identify which new issues are present in Green
Video Games. Specifically, we identified three new issues:

  * •

Battery life: The rise of mobile devices as a platform for the consumption of
video games and the increase in the complexity and graphic resolution of
applications has shown that battery life limits or impairs the gaming
experience. Therefore, numerous studies address the improvement of the energy
efficiency of video games, not so much for environmental concerns but for the
impact that lower energy consumption has on battery life and, consequently, on
the user’s experience.

  * •

Trade-off between energy consumption and application performance, quality of
experience, and/or economical cost: Many techniques for reducing the energy
consumption of computer applications, in particular video games, such as
Dynamic voltage and frequency scaling (DVFS), can have a negative impact on
application performance or the user’s experience, and others can have an
influence on the economical cost. Many studies are therefore focused on
analyzing the ideal combination of parameters to maximize energy savings with
minimum degradation of application performance, quality of experience, and
acceptable cost.

  * •

Modelling and measurement of energy consumption: Improving the energy
efficiency of the software requires the programmer to know precisely the
contribution of each element or process to the overall energy consumption. The
level of detail of these studies therefore determines the level at which we
can intervene in a controlled manner. Studies that seek to measure the energy
consumption of software under controlled conditions and to be able to model
its behavior under different conditions from these measurements are therefore
a very useful tool in the evolution of software (more specifically of video
games towards more energy-efficient versions).

Fig. 9 shows the primary studies classified taking into account these new
issues. Comparing this figure with Fig. 7 and Fig. 7, it can be observed that
only one issue remains the same: Data center design. The Software and
deployment optimization is still an issue, but the number of articles in this
category is lower than in (jain2020novel, ). This does not mean that the
papers are incorrectly classified using the Green Computing issues or that the
other issues addressed in Green Computing are not addressed in the context of
video games. It means that the strategy used to identify an issue is different
in the Video Game area.

####  3.1.1. Motivation

The issues in Green Video Games revolve more around a specific motivation. For
example, considering that gaming applications consume significant power due to
the heavy utilization of CPU-GPU resources, 7351775 considers that the battery
in mobile devices is a critical reason to control consumption. This is the
main motivation of the work. The solution that the authors propose consists of
a memory-aware cooperative CPU-GPU dynamic voltage and frequency scaling
governor. This governor is an energy efficiency evaluation mechanism (i.e.,
Modeling and Evaluation of Energy Efficiency according to the Green Computing
issues in (li2011survey, )) that allows the control of the energy consumed by
the CPU-GPU (i.e., Power Management according to the Green Computing issues in
(jain2020novel, )). Since Green Computing issues do not have a clear
difference between motivation and solution, 7351775 would be classified based
on the perspective of the researcher. However, in Green Video Games, this may
lead to contradictions in classification. Following the above example, one
researcher might classify the paper as a Battery life issue because he focuses
on the motivation, but another researcher might classify the paper as a
Modelling and measurement of energy consumption issue because he focuses on
its solution. Therefore, although the differences between motivation and
solution may not matter in Green Computing, in Green Video Games it is
important to avoid misunderstandings.

Fig. 9 shows the papers classified based on motivations. Most of the primary
studies tackled Battery life (36,2% of the primary studies) or the Trade-off
between energy consumption and other aspects (36,2% of the primary studies) as
main motivations. The number of primary studies tackling the Modelling and
measurement of energy consumption also stands out (13% of primary studies).

Figure 8. Primary studies classified considering motivations in Green Video
Games

Figure 9. Primary studies classified considering devices in Green Video Games

The main studies, whose motivation is the Trade-off between energy consumption
and other aspects, compare energy consumption with at least one of these
aspects: application performance, quality of experience, and economic cost.
Specifically, 20 studies explore the trade-off of energy consumption and
application performance, 7 studies explore the trade-off of energy consumption
and quality of experience, and 1 study explores the trade-off of energy
consumption and economic cost.

Furthermore, three studies are classified as General. These articles review
and attempt to classify various aspects of interest related to the
environmental impact of video game development. They do not focus on a
specific motivation, but rather show an overall view.

####  3.1.2. Device

The issues in Green Computing usually focus on computers. However, Video Games
are played on multiple devices, not only computers. Therefore, the issues in
Green Video Games depend on devices. For example, video games consume
significant power, so the battery in portable devices is critical, but it does
not have the same importance in computers.

Fig. 9 shows the papers classified according to devices. Most of the primary
studies (44% of the primary studies) focus on mobile devices. The number of
primary studies focusing on cloud computing also stands out (18% of primary
studies). However, the number of studies that focus on computers is low in
Green Video Games (8% of primary studies).

####  3.1.3. Layer

The issues in Green Computing are defined providing great importance to the
infrastructure layer (i.e., hardware, network, software, etc.). For example,
Power management focuses on different techniques that allow the control of the
energy consumed by the components of computers. This definition is clearly
referencing to hardware in computers. However, in Green Video Games, articles
define the issues independently of the infrastructure layer.

For example, Battery life is the motivation for pathania2014integrated,
9816214, 4382127, and hosseini2012energy. However, these three studies focus
on a different layer. The work in pathania2014integrated proposes reducing the
power consumption using a unified view of a CPU-GPU Dynamic Voltage Frequency
Scaling (i.e., Hardware). The work in 9816214 proposes an adaptive bitrate
model to reduce the transfer latency that allows downloading massive data from
the server keeping the user perception experience (i.e., Network). The work in
4382127 provides a new technique for improving in-memory compression in order
to minimize memory consumption and also a memory bank partitioning technique
to cluster memory banks into active banks and inactive banks, which are
powered off dynamically (i.e., Software). The work in hosseini2012energy
propose two new energy-aware adaptation algorithms that reduce the cost by
limiting light and transforming textures in video games (i.e., Software and
Design).

The four studies address the same motivation (i.e., Battery life), but the
first one focuses on Hardware, the second focuses on Network, the third one
focuses on Software, and the fourth one focuses on Software and Design.
Similarly, the other identified motivations may be tackled using hardware,
network, software, design, and/or art solutions.

Figure 10. Primary studies classified considering
layers in Green Video Games

Figure 11. Distribution of the layers in Green Video Games by years

Fig. 11 shows the papers classified according to layers. Most of the primary
studies focus on researching solutions through hardware components (39% of the
primary studies). Some primary studies focus on researching solutions through
network and software components (30% and 26% of the primary studies,
respectively). There are a few papers that focus on design components (5% of
the primary studies). However, there are no primary studies that focus on art
components.

Furthermore, Fig. 11 shows the number of papers in each layer by year. Note
that there are no differences between the hardware, network, and software
layers. The primary studies in these three layers emerged at approximately the
same time. In the case of the design layer, studies have appeared
sporadically, although it seems that their appearances have been more
recurrent in recent years.

Since the studies in Green Video Games are more motivation-oriented, they
depend on the type of device and their solutions use components of specific
layers. Therefore, we propose defining the issues around three axes:
motivations, devices, and layers. Fig. 12 summarizes the categories for each
axe. The motivations and devices were identified from the primary studies and
the layers were identified by professional video game developers in order to
cover all of the relevant aspects of video game creation.

Figure 12. Green Video Games issues regarding motivation, device, and layer

Table 4 shows an overview of the primary studies by issues. Most of the
primary studies focus on Battery life (i.e., 36.23% of the primary studies) or
the Trade-off between energy consumption and other aspects (i.e., 36.23% of
the primary studies). In the case of Battery life, most of the studies provide
solutions based on the hardware layer in mobile devices. In the case of the
Trade-off between energy consumption and other aspects, most of the studies
provide solutions based on the network layer in cloud computing and mobile
devices. It is worth noting that these two issues are the only ones that
contain some study in the design layer, even though all the studies are on
mobile devices.

Table 4. Overview of the issues in Green Video Games based on: motivation (1-Battery life, 2-Data center design, 3-Modelling and measurement of energy consumption, 4-General, 5-Software and development optimization, and 6-Trade-off between energy consumption and other aspects); layer (H-Hardware, N-Network, S-Software, D-Design, and A-Art); and device. |  |  Cloud computing |  Computers |  Edge devices |  Gaming devices |  Handheld devices |  Mobile devices |  Portable devices |  Reconfigurable MPSoCs |  Smart devices |  Wearable devices |  Wireless sensors |  Not specified
---|---|---|---|---|---|---|---|---|---|---|---|---|---
1 | H |  |  |  |  |  |  choi2021optimizing 4749649 7351775 chen2015user peters2016frame pathania2014integrated dietrich2014lightweight pathania2015power park2017synergistic |  Muhuri20182311 gu2008control gu2006games gu2008power |  |  |  |  |
| N |  cai2013next |  |  |  |  |  Ghergulescu2014 zhang2017exploring 8057058 9816214 cai2013next |  |  |  |  |  |
| S |  |  |  |  |  4382127 |  4749649 5679572 thirugnanam2012dynamic pinto2017energy hosseini2012energy anand2009game |  |  |  |  pinto2017energy |  |
| D |  |  |  |  |  |  Yan201813 hosseini2012energy |  |  |  |  |  |
| A |  |  |  |  |  |  |  |  |  |  |  |
2 | H |  Behiya202084 |  |  |  |  |  |  |  |  |  |  |
| N |  Behiya202084 Dhib20222119 nae2008efficient |  |  |  |  |  |  |  |  |  |  |
| S |  |  |  |  |  |  |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |
| A |  |  |  |  |  |  |  |  |  |  |  |
3 | H |  |  |  |  |  |  chen2018user dietich2017estimating park2016hicap |  6471008 |  |  Song2019 |  Kone2016561 |  |
| N |  han2020virtual |  |  |  |  ma2017rofi |  ma2017rofi |  |  |  |  Kone2016561 |  |
| S |  |  Pereira2021 |  |  |  |  park2016hicap |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |
| A |  |  |  |  |  |  |  |  |  |  |  |
4 | H |  |  |  |  |  |  |  |  |  |  |  |
| N |  chuah2014cloud |  8566409 |  |  |  |  |  |  |  |  |  |
| S |  |  kasurinen2017concerns |  |  |  |  kasurinen2017concerns |  |  |  |  |  |
| D |  |  |  |  |  |  |  |  |  |  |  |
| A |  |  |  |  |  |  |  |  |  |  |  |
5 | H |  |  |  |  |  |  |  |  |  |  |  |
| N |  |  |  |  |  |  |  |  |  |  |  |
| S |  |  7380525 1193041 |  |  |  |  |  |  |  |  |  LeBorgne201263 |  Rodriguez202218704
| D |  |  |  |  |  |  |  |  |  |  |  |
| A |  |  |  |  |  |  |  |  |  |  |  |
6 | H |  Guan20152434 |  Hertz2016142 |  |  Nery201997 |  bose2012physics |  Olivito201564 Zhang20191546 Teshima2015 mochocki2006power |  |  Suriano2020136 |  |  |  |  Cho2019499 Toupas201988 512397
| N |  Kavalionak2015301 Shea201561 bhuyan2022end 7037666 Guan20152434 ahmadi2014game |  |  bhuyan2022end |  |  |  Jiang2022 Zhang20191546 lee2016exploiting 6490354 8566409 |  |  |  |  |  |
| S |  Gharsallaoui20171072 |  Hertz2016142 |  |  |  |  Olivito201564 Corbalan-Navarro20224375 9591818 |  |  Suriano2020136 |  |  |  |  boeing2007evaluation
| D |  |  |  |  |  |  9591818 |  |  |  |  |  |
| A |  |  |  |  |  |  |  |  |  |  |  |

The modelling and measurement of energy consumption is the next issue with
more studies (i.e., 13.04% of the primary studies). The studies tackling this
issue focus on the hardware layer and mobile devices overall. The Software and
deployment optimization is tackled by 5.80% of the primary studies. Its
studies focus on the software layer, but contrary to the other motivations,
its studies focus on computers instead of mobile devices. Data center design
is the issue with the fewest studies (i.e., 4.3% of primary studies), and all
of these studies tackle the issue through the network layer and cloud
computing.

None of the issues are addressed through the art layer, and three studies
address some issues through the design layers. This outcome will be taken into
account for discussion. Should we discard these layers from the taxonomy? Are
these layers interesting for Green Video Games?

###  3.2. RQ2. Which green techniques are applied for video game development?

This question sheds light on green techniques that are applied in the context
of video games. From the extracted data, we identified the following five
techniques:

  * •

Cloud resource management comprises the provisioning, allocation, and
monitoring of the cloud resources (e.g., servers, memory, storage, network,
CPU, application servers, virtual machines, etc) (sobers2021introduction, ).

  * •

Content energy-aware adaptation are techniques used to modify multimedia
content in terms of reception and presentation in order to fit content to
devices and at the same time minimize energy consumption (ismail2013survey, ).

  * •

CPU/GPU management refers to techniques that take an integrated approach to
study the influence of CPU and GPU on power consumption and performance in
order to achieve better energy efficiency, for instance through synergistic
frequency limiting policies park2017synergistic, or adapting the governing
policies of both components taking into account user demands chen2015user.

  * •

Dynamic voltage and frequency scaling (DVFS) is a technique that aims at
reducing the dynamic power consumption by dynamically adjusting the voltage
and frequency of a CPU (jha2001low, ; cardoso2017embedded, ).

  * •

Field-programmable gate arrays (FPGAs) are integrated circuits that can be
configured and reprogrammed by the customer. Some platforms are developed
using FPGAs because they deliver higher energy efficiency than CPU-only or
GPU-based platforms Cho2019499.

Fig. 13 shows the number of primary studies for each technique. The most
common techniques are Cloud resource management and Content energy-aware
adaptation. Nevertheless, the difference between the number of papers for each
technique is not large. Perhaps the most relevant aspect of this figure is the
number of primary studies that do not specify the technique applied.

Figure 13. Distribution of the primary studies based on technique

In addition, to answer RQ2, we studied these techniques based on the issues
identified in RQ1. In terms of the motivation (Table 5), it can be observed
that the five techniques are used for both Battery life and the Trade-off
between energy consumption and other aspects. For Modelling and measurement of
energy consumption, researchers are using four of the five techniques (i.e.,
Cloud resource management, Content energy-aware adaptation, CPU/GPU
management, and DVFS). For Software and development optimization, researchers
are using two of the five techniques (i.e., Content-energy aware adaptation
and CPU/GPU management). However, for Data center design, the only technique
being applied is Cloud resource management.

Table 5. Techniques used in primary studies over motivation (1-Battery life, 2-Data center design, 3-Modelling and measurement of energy consumption, 4-General, 5-Software and development optimization, and 6-Trade-off between energy consumption and other aspects). |  Cloud resource management |  Content energy-aware adaptation |  CPU/GPU management |  DVFS |  FPGA |  Not specified
---|---|---|---|---|---|---
1 |  cai2013next |  Ghergulescu2014 zhang2017exploring 5679572 8057058 9816214 thirugnanam2012dynamic hosseini2012energy anand2009game |  7351775 chen2015user pathania2014integrated gu2008power park2017synergistic |  Muhuri20182311 4749649 gu2008control peters2016frame gu2006games dietrich2014lightweight |  choi2021optimizing pathania2015power |  Yan201813 4382127 pinto2017energy
2 |  Behiya202084 Dhib20222119 nae2008efficient |  |  |  |  |
3 |  Kone2016561 han2020virtual |  ma2017rofi |  6471008 park2016hicap |  Song2019 chen2018user dietich2017estimating |  |  Pereira2021
4 |  chuah2014cloud 8566409 |  |  |  |  |  kasurinen2017concerns
5 |  |  LeBorgne201263 1193041 |  7380525 |  |  |  Rodriguez202218704
6 |  Kavalionak2015301 Shea201561 Jiang2022 bhuyan2022end 6490354 7037666 Guan20152434 8566409 |  Gharsallaoui20171072 Corbalan-Navarro20224375 ahmadi2014game |  Suriano2020136 lee2016exploiting |  mochocki2006power |  Olivito201564 Zhang20191546 Cho2019499 Nery201997 Toupas201988 bose2012physics |  Teshima2015 512397 Hertz2016142 9591818 boeing2007evaluation

In terms of the device (Table 6), it can be observed that the five techniques
are used for Mobile devices. In contrast, for Cloud computing, the technique
that is most used is Cloud resource management. Also noteworthy is the number
of studies that did not specify any technique. It is striking that most of the
papers that did not specify the device also did not specify the technique.

Table 6. Techniques used in primary studies over devices (D1-Cloud computing, D2-Computers, D3-Edge devices, D4-Gaming devices, D5-Handheld devices, D6-Mobile devices, D7-Portable devices, D8-Reconfigurable MPSoCs, D9-Smart devices, D10-Wearable devices, D11-Wireless sensors, and D12-Not specified). |  Cloud resource management |  Content energy-aware adaptation |  CPU/GPU management |  DVFS |  FPGA |  Not specified
---|---|---|---|---|---|---
D1 |  Kavalionak2015301 Behiya202084 Shea201561 chuah2014cloud bhuyan2022end 7037666 Dhib20222119 Guan20152434 nae2008efficient cai2013next han2020virtual |  Gharsallaoui20171072 ahmadi2014game |  |  |  |
D2 |  8566409 |  1193041 |  7380525 |  |  |  Pereira2021 kasurinen2017concerns Hertz2016142
D3 |  bhuyan2022end |  |  |  |  |
D4 |  |  |  |  |  Nery201997 |
D5 |  |  ma2017rofi |  |  |  bose2012physics |  4382127
D6 |  Jiang2022 6490354 8566409 cai2013next |  Ghergulescu2014 ma2017rofi zhang2017exploring 5679572 8057058 Corbalan-Navarro20224375 9816214 thirugnanam2012dynamic hosseini2012energy anand2009game |  lee2016exploiting 7351775 chen2015user park2016hicap pathania2014integrated park2017synergistic |  chen2018user 4749649 dietich2017estimating peters2016frame dietrich2014lightweight mochocki2006power |  Olivito201564 Zhang20191546 choi2021optimizing pathania2015power |  Teshima2015 Yan201813 kasurinen2017concerns 9591818 pinto2017energy
D7 |  |  |  6471008 gu2008power |  Muhuri20182311 gu2008control gu2006games |  |
D8 |  |  |  Suriano2020136 |  |  |
D9 |  |  |  |  Song2019 |  |
D10 |  Kone2016561 |  |  |  |  |  pinto2017energy
D11 |  |  LeBorgne201263 |  |  |  |
D12 |  |  |  |  |  Cho2019499 Toupas201988 |  Rodriguez202218704 512397 boeing2007evaluation

In terms of the layer (Table 7), it can be observed that most techniques are
not only used in one specific layer. However, there are techniques that are
more commonly used on one layer, such as Cloud resource management. Cloud
Resource management is more frequently used in solutions on the Network layer.

In contrast, DVFS and FPGA are used exclusively in the Hardware layer. In
Table 7, one study that uses DVFS is classified in the Software layer.
Similarly, two studies that use FPGA are classified in the Network layer and
the Software layer. However, we can also find these studies in the Hardware
layer. These three studies provide solutions where part of this solution is
based on Hardware and part of this solution is based on Network or Software.
DVFS and FPGA are always used in the solution part that is based on Hardware.
Therefore, although these studies appear in Network and Software, DVFS and
FPGA are always used in the Hardware layer.

Table 7. Techniques used in primary studies over layers (H-Hardware, N-Network, S-Software, D-Design, and A-Art). |  Cloud resource management |  Content energy-aware adaptation |  CPU/GPU management |  DVFS |  FPGA |  Not specified
---|---|---|---|---|---|---
H |  Behiya202084 Kone2016561 Guan20152434 |  |  Suriano2020136 6471008 7351775 chen2015user park2016hicap pathania2014integrated gu2008power park2017synergistic |  Muhuri20182311 Song2019 chen2018user 4749649 gu2008control dietich2017estimating peters2016frame gu2006games dietrich2014lightweight mochocki2006power |  Olivito201564 Zhang20191546 Cho2019499 Nery201997 Toupas201988 choi2021optimizing bose2012physics pathania2015power |  Teshima2015 512397 Hertz2016142
N |  Kavalionak2015301 Behiya202084 Shea201561 Jiang2022 Kone2016561 chuah2014cloud bhuyan2022end 8566409 6490354 7037666 Dhib20222119 Guan20152434 8566409 nae2008efficient cai2013next han2020virtual |  Ghergulescu2014 ma2017rofi zhang2017exploring 8057058 9816214 ahmadi2014game |  lee2016exploiting |  |  Zhang20191546 |
S |  |  LeBorgne201263 Gharsallaoui20171072 1193041 5679572 Corbalan-Navarro20224375 thirugnanam2012dynamic hosseini2012energy anand2009game |  Suriano2020136 7380525 park2016hicap |  4749649 |  Olivito201564 |  Pereira2021 Rodriguez202218704 kasurinen2017concerns 4382127 Hertz2016142 9591818 pinto2017energy boeing2007evaluation
D |  |  hosseini2012energy |  6471008 |  |  |  Yan201813 9591818
A |  |  |  |  |  |

In the literature, there are some research works that focus on categorizing
the Green Computing techniques. The work in (choudhary2014survey, ) presents a
classification for the Green Computing techniques based on four different
levels: the hardware and firmware level, the operating system level, the
virtualization level, and the data center level. The work in
(shuja2017greening, ) presents a review of Green Computing techniques amidst
the emerging computing technologies that are evident in our society. However,
these works only provide descriptions of what type of techniques are employed
for the different levels or categories, and they do not go into what specific
techniques exist. For this reason, it is difficult to fairly compare if the
techniques used in Green Computing are the same ones used in video games.

###  3.3. RQ3. What are the limitations of the current Green Video Game
studies?

In this section, we outline the limitations of the existing studies in Green
Video Games. We also studied the evidence level, which is used to check the
maturity of the technique, practice, solution, or method presented in the
paper.

None of the studies provide threats to validity. Threats to validity would be
those internal and external factors that may prevent one from measuring what
one wants to measure or that may obscure the relationship between the
dependent and the independent variables. In other words, threats to validity
help to replicate the experiment or evaluation using the same conditions in
order to compare the results with other studies fairly. However, despite the
importance of the threats to validity, 100% of the primary studies have not
included them.

Since there were no threats to validity, we focused on extracting any
limitation, not only the ones related to the validation. Only four studies
describe some limitation of their work. The work in 1193041 points out two
limitations of the proposed method: 1) the probability of error can be high if
the environment contains a lot of small objects, and 2) the application of the
method to scenes with rigid objects only is limited. These limitations are
shared with other postfiltering methods. In anand2009game, the authors only
consider the wireless network interface card component of the Personal Digital
Assistant (PDA). In pathania2014integrated, since the authors were not aware
of any available record and replay mechanism for 3D games on the Android
platform, they requested volunteers to play the main level of the games with
repetition of their game-play as far as possible. The work in
dietrich2014lightweight uses the processor of a Samsung Galaxy Nexus for the
evaluation, but this processor is configured such that the CPU performance
counters cannot be read. To overcome this limitation, the authors ran the
games at all available processing frequencies and measured the frame rate as
well as the time spent in the loading state.

The extracted data also shows that the following topics are recurrently
reported as future works:

  * •

Generalization of the results evaluating the solution on other devices
Muhuri20182311 Zhang20191546 park2016hicap 5679572.

  * •

Additional parameters or parameter tuning zhang2017exploring 6490354 8057058
gu2008control han2020virtual.

  * •

Testing the solution with other games or different video game genres
thirugnanam2012dynamic 7380525 nae2008efficient.

  * •

Extending the solution using machine-learning techniques park2016hicap
park2017synergistic.

  * •

Integrating or improving solutions in the cloud Kavalionak2015301
Gharsallaoui20171072 ahmadi2014game.

In addition to the limitations, this section also provides information about
the evidence level. Fig. 14 shows the distribution of evidence levels
described in Section 2.7. This figure shows both the number of papers and the
percentage of papers for each evidence level. Taking into account this figure,
most of the studies have been evaluated using at least one specific video
game.

Figure 14. Distribution of the primary studies based on evidence level

In terms of the motivation (Table 8, most primary studies have been tested
with at least one specific video game, or even with several video games. The
exceptions, however, are the studies focusing on the Trade-off between energy
consumption and other aspects and the studies offering General perspectives.
In these two cases, the number of studies evaluated on toy examples, no-
commercial video games, or that do not use video games exceeds that of studies
with evaluations based on commercial games.

Table 8. Level of evidence of the primary studies over motivation (1-Battery life, 2-Data center design, 3-Modelling and measurement of energy consumption, 4-General, 5-Software and development optimization, and 6-Trade-off between energy consumption and other aspects). |  0 - no video game |  1 - toy example/no commercial video games |  2 - specific video game |  3 - multiple video games
---|---|---|---|---
1 |  4382127 4749649 9816214 pinto2017energy cai2013next |  Yan201813 |  Ghergulescu2014 5679572 gu2008control thirugnanam2012dynamic anand2009game gu2006games gu2008power |  Muhuri20182311 choi2021optimizing zhang2017exploring 7351775 8057058 chen2015user hosseini2012energy peters2016frame pathania2014integrated dietrich2014lightweight pathania2015power park2017synergistic
2 |  Behiya202084 |  |  Dhib20222119 nae2008efficient |
3 |  Kone2016561 Song2019 6471008 |  Pereira2021 |  |  chen2018user ma2017rofi dietich2017estimating park2016hicap han2020virtual
4 |  8566409 |  chuah2014cloud |  |  kasurinen2017concerns
5 |  Rodriguez202218704 |  1193041 |  LeBorgne201263 7380525 |
6 |  Jiang2022 Teshima2015 Toupas201988 6490354 512397 Hertz2016142 boeing2007evaluation |  Kavalionak2015301 Zhang20191546 lee2016exploiting bhuyan2022end 9591818 bose2012physics mochocki2006power |  Suriano2020136 Shea201561 Olivito201564 Gharsallaoui20171072 7037666 8566409 |  Cho2019499 Nery201997 Corbalan-Navarro20224375 Guan20152434 ahmadi2014game

In terms of the device (Table 9), the number of studies evaluated on video
games stands out, especially in the case of mobile devices. In the case of
cloud computing and portable devices, most studies were evaluated on a single
commercial video game. However, most of the studies, where the device is not
specified, conducted their evaluations without using a video game.

Table 9. Level of evidence of the primary studies over devices (D1-Cloud computing, D2-Computers, D3-Edge devices, D4-Gaming devices, D5-Handheld devices, D6-Mobile devices, D7-Portable devices, D8-Reconfigurable MPSoCs, D9-Smart devices, D10-Wearable devices, D11-Wireless sensors, and D12-Not specified). |  0 - no video game |  1 - toy example/no commercial video games |  2 - specific video game |  3 - multiple video games
---|---|---|---|---
D1 |  Behiya202084 cai2013next |  Kavalionak2015301 chuah2014cloud bhuyan2022end |  Shea201561 Gharsallaoui20171072 7037666 Dhib20222119 nae2008efficient |  Guan20152434 ahmadi2014game han2020virtual
D2 |  8566409 Hertz2016142 |  Pereira2021 1193041 |  7380525 |  kasurinen2017concerns
D3 |  |  bhuyan2022end |  |
D4 |  |  |  |  Nery201997
D5 |  4382127 |  bose2012physics |  |  ma2017rofi
D6 |  Jiang2022 Teshima2015 6490354 4749649 9816214 pinto2017energy cai2013next |  Zhang20191546 Yan201813 lee2016exploiting 9591818 mochocki2006power |  Olivito201564 Ghergulescu2014 5679572 8566409 thirugnanam2012dynamic anand2009game |  kasurinen2017concerns choi2021optimizing chen2018user ma2017rofi zhang2017exploring 7351775 8057058 Corbalan-Navarro20224375 chen2015user hosseini2012energy dietich2017estimating peters2016frame park2016hicap pathania2014integrated dietrich2014lightweight pathania2015power park2017synergistic
D7 |  6471008 |  |  gu2008control gu2006games gu2008power |  Muhuri20182311
D8 |  |  |  Suriano2020136 |
D9 |  Song2019 |  |  |
D10 |  Kone2016561 pinto2017energy |  |  |
D11 |  |  |  LeBorgne201263 |
D12 |  Toupas201988 Rodriguez202218704 512397 boeing2007evaluation |  |  |  Cho2019499

In terms of the layer (Table 10), there was a fairly similar number of studies
at all levels of evidence, although the number of studies evaluated on toy
examples or non-commercial video games was somewhat lower. The result that is
a bit striking is the progression in the studies evaluated on multiple video
games. As you move up the layer, there are fewer studies evaluated on multiple
video games. In other words, the hardware layer has more studies evaluated
with multiple games than the network layer; the network layer has more studies
evaluated with multiple games than the software layer; the software layer has
more studies evaluated with multiple games than the design layer; and the
design layer has more studies evaluated with multiple games than the art
layer.

Table 10. Level of evidence of the primary studies over layers (H-Hardware, N-Network, S-Software, D-Design, and A-Art). |  0 - no video game |  1 - toy example/no commercial video games |  2 - specific video game |  3 - multiple video games
---|---|---|---|---
H |  Behiya202084 Kone2016561 Song2019 Teshima2015 Toupas201988 4749649 512397 6471008 Hertz2016142 |  Zhang20191546 bose2012physics mochocki2006power |  Suriano2020136 Olivito201564 gu2008control gu2006games gu2008power |  Muhuri20182311 Cho2019499 Nery201997 choi2021optimizing chen2018user 7351775 Guan20152434 chen2015user dietich2017estimating peters2016frame park2016hicap pathania2014integrated dietrich2014lightweight pathania2015power park2017synergistic
N |  Behiya202084 Jiang2022 Kone2016561 8566409 6490354 9816214 cai2013next |  Kavalionak2015301 Zhang20191546 chuah2014cloud lee2016exploiting bhuyan2022end |  Shea201561 Ghergulescu2014 7037666 Dhib20222119 8566409 nae2008efficient |  ma2017rofi zhang2017exploring 8057058 Guan20152434 ahmadi2014game han2020virtual
S |  Rodriguez202218704 4382127 4749649 Hertz2016142 pinto2017energy boeing2007evaluation |  Pereira2021 1193041 9591818 |  Suriano2020136 LeBorgne201263 Olivito201564 Gharsallaoui20171072 7380525 5679572 thirugnanam2012dynamic anand2009game |  kasurinen2017concerns Corbalan-Navarro20224375 hosseini2012energy park2016hicap
D |  6471008 |  Yan201813 9591818 |  |  hosseini2012energy
A |  |  |  |

###  3.4. RQ4. What other video game aspects should be considered to develop
Green Video Games?

In this section, we outline different aspects of video games that are largely
neglected in Green Computing. Specifically, we focus on video game artifacts,
genres, and content.

####  3.4.1. Artifacts

Among the main artifacts of video games, game engines and physics engines are
known for their complexity and consumption of resources. Game engines (e.g.,
Unity or Unreal) are software tools for developing video games that use
libraries or support programs instead of creating everything from the ground
up. Physics engines are software components that simulate the motion of
objects based on parameters such as mass, friction, or gravity.

One way to develop greener video games could be to improve energy efficiency
during their development by improving the energy efficiency of the game
engines and physics engines used to develop video games. However, according to
our survey, only four primary studies take these artifacts into account.

The authors in gu2006games presents a novel characterization of the workload
of computer games based on a game engine. This same authors present in
gu2008power a novel dynamic voltage scaling scheme that is specifically
directed towards 3D graphics-intensive interactive gaming applications running
on battery operated portable devices. In both papers, the importance of the
game engine (Quake II) and the physics engine are highlighted. They used these
engines to evaluate the proposed algorithms. However, neither of these works
seeks to improve the energy efficiency of the game engine.

In contrast, the other two primary studies focus on improving the energy
efficiency of physics engines. The work in bose2012physics demonstrates that
physics processing can be optimized using a reconfigurable processor. In
addition, Toupas201988 considers the use of FPGAs to accelerate certain
demanding components of the physics simulation pipeline aiming to provide
better performing solutions at a significantly lower energy cost.

In terms of the motivation and the device, the two studies that consider game
engines (gu2006games and gu2008power) focus on battery life in portable
devices. In contrast, the two studies that consider physics engines
(bose2012physics and Toupas201988) focus on the Trade-off between energy
consumption and application performance. In terms of the layer, the four
studies focus on the hardware layer.

####  3.4.2. Genres

Today, there are many genres in video games, and each game has different
requirements. For example, an offline single-player game does not require the
same infrastructure and energy as MMOG. From the primary studies, we have
identified the following genres:

  * •

First-Person Shooter (FPS) is a gun-based video game in which the player
observes the scenario from the protagonist’s point of view, i.e., the user
carries a weapon and sees the protagonist’s perspective in first-person. For
the user to achieve good immersion, 3D graphics are essential in this genre.
In addition, some FPS video games may be played by multiple users online.
Battlefield 2042 or Far Cry 6 are examples of FPS video games.

  * •

Massively Multiplayer Online Game (MMOG) is an online video game that is
played by a large number of users through the same server. World of Warcraft
or Destiny 2 are examples of MMOG.

  * •

Real-Time Strategy (RTS) is a strategy video game where all of the users play
simultaneously (in real-time) instead of playing in turns. The games are
usually fast (maximum one hour) and dynamic. Some games of this genre are
played online and/or have a multiplayer mode. Crusader Kings III or Age of
Empires IV are examples of RTS video games.

  * •

Board games are video games based on board games. Stacklands or UNO are
examples of Board games.

Fig. 15 shows the genres of the video games used in the primary studies. Most
of the primary studies do not mention the genre of the video games used to
evaluate their work. FPS and MMOG are the genres that are most used in the
primary studies (12% and 13% respectively). Board games are used in 7% of the
primary studies, and only 1% of the primary studies use RTS video games.

Figure 15. Distribution of the primary studies based on genres of video games

In terms of the motivation (Table 11), most of the primary studies using FPS
and MMOG video games focus on Battery life or the Trade-off between energy
consumption and other aspects. There is only one study that uses RTS video
games, and it also focuses on Battery life. However, most of the studies using
Board games focus on Software and development optimization. With regard to
Modelling and measurement of energy consumption, all of the studies use MMOG.
Moreover, the studies that focus on General motivation do not specify the
genre because they are not using a video game to evaluate their work.

Table 11. Genres of the video games used in primary studies over motivation (1-Battery life, 2-Data center design, 3-Modelling and measurement of energy consumption, 4-General, 5-Software and development optimization, and 6-Trade-off between energy consumption and other aspects). |  FPS |  MMOG |  RTS |  Board games
---|---|---|---|---
1 |  Muhuri20182311 gu2008control thirugnanam2012dynamic anand2009game gu2006games |  8057058 chen2015user pinto2017energy |  5679572 |
2 |  |  Dhib20222119 nae2008efficient |  |
3 |  chen2018user |  6471008 |  |
4 |  |  |  |
5 |  |  |  |  LeBorgne201263 Rodriguez202218704 7380525
6 |  Suriano2020136 8566409 |  Kavalionak2015301 7037666 ahmadi2014game |  |  Olivito201564 lee2016exploiting

In terms of the device (Table 12), FPS video games are used overall by studies
that focus on handheld and mobile devices. In contrast, MMOG video games are
used overall by studies that focus on cloud computing and handheld devices.
Cloud computing is a valuable resource for MMOG games since they are played on
a server. Therefore, it is logical for most of the studies on cloud computing
to be evaluated using MMOG games.

The study that uses RTS video games 5679572 focuses on mobile devices.
Finally, Board games are used to evaluate studies that focus on multiple
devices, such as computers, mobile devices, or wireless sensors.

Table 12. Genres of the video games used in primary studies over devices (D1-Cloud computing, D2-Computers, D3-Edge devices, D4-Gaming devices, D5-Handheld devices, D6-Mobile devices, D7-Portable devices, D8-Reconfigurable MPSoCs, D9-Smart devices, D10-Wearable devices, D11-Wireless sensors, and D12-Not specified). |  FPS |  MMOG |  RTS |  Board games
---|---|---|---|---
D1 |  |  Kavalionak2015301 7037666 Dhib20222119 ahmadi2014game nae2008efficient |  |
D2 |  |  |  |  7380525
D3 |  |  |  |
D4 |  |  |  |
D5 |  |  |  |
D6 |  chen2018user 8566409 thirugnanam2012dynamic anand2009game |  8057058 chen2015user pinto2017energy |  5679572 |  Olivito201564 lee2016exploiting
D7 |  Muhuri20182311 gu2008control gu2006games |  6471008 |  |
D8 |  Suriano2020136 |  |  |
D9 |  |  |  |
D10 |  |  pinto2017energy |  |
D11 |  |  |  |  LeBorgne201263
D12 |  |  |  |  Rodriguez202218704

In terms of the layer (Table 13), most of the studies that use FPS video games
present solutions for the hardware layer. As previously mentioned, in order to
achieve good immersion of the user in FPS video games, 3D graphics are
essential in this genre. Therefore, the consumption of the CPU and GPU
rendering 3D is a point for improving sustainability in video games.

In contrast, most of the studies that use MMOG video games present solutions
for the network layer. In this case, the server is an essential component for
this genre: how the users are connected simultaneously at the same server, how
the data of the video game is transferred, what the latency is, etc.
Therefore, saving energy on the network layer may be interesting for MMOG
video games.

Finally, most studies that use RTS and Board games present solutions for the
software layer. RTS video games and especially Board games do not require
complex computations through the CPU or GPU. In addition, many of these video
games are played offline, so they do not require complex or power-hungry
components at the network layer. However, the efficiency of these video games
can be improved by using more efficient algorithms.

Table 13. Genres of the video games used in primary studies over layers (H-Hardware, N-Network, S-Software, D-Design, and A-Art). |  FPS |  MMOG |  RTS |  Board games
---|---|---|---|---
H |  Muhuri20182311 Suriano2020136 chen2018user gu2008control gu2006games |  6471008 chen2015user |  |  Olivito201564
N |  8566409 |  Kavalionak2015301 7037666 8057058 Dhib20222119 ahmadi2014game nae2008efficient |  |  lee2016exploiting
S |  Suriano2020136 thirugnanam2012dynamic anand2009game |  pinto2017energy |  5679572 |  LeBorgne201263 Olivito201564 Rodriguez202218704 7380525
D |  |  6471008 |  |
A |  |  |  |

####  3.4.3. Content

The content consists of the narrative, gameplay, technical, audio, and visual
components of a video game, from the storyboards to characters, scenarios,
music, etc. From the primary studies, we have identified four types of
content: maps, lights, textures, and video broadcasting or encoding.

In video games, maps are used as the main user interface, as a reference
guide, or both. One primary study focuses on maps. Specifically, Nery201997
tackled the consumption of pathfinding algorithms. Its experimental results
are presented including performance, circuit-area, and energy consumption
results for a set of artificial and commercial (game) maps.

Lights are not only important for visualizing the depth of a scene or an
object. They are also used to provide information to help players or to evoke
moods. For example, in horror video games, there are many different shadows
for hiding or provoking a sense of tension in the user. Two of the primary
studies tackled the consumption of video games and virtual environments that
offer immersive experiences. The work in Yan201813 shifts the default fixed
full brightness in video games to a dark adaptation based dynamically scaled
brightness. The work in hosseini2012energy proposes two new energy-aware
adaptation schemes that can be employed in 3D graphics applications. One of
these two schemes is based on lighting limitation.

The other scheme is based on textural transformation. Textures are also
content in video games. Textures are the graphical skins that render the
objects within a video game. Textures require memory space for storage and
bandwidth for transfer to the graphics card for rendering. To store and
transfer the textures for rendering, video games make use of the GPU and the
bandwidth, respectively. Therefore, hosseini2012energy focuses on energy
saving with acceptable sacrifices to a user’s visual experience, specifically
transforming to the textures.

Finally, five of the primary studies consider video broadcasting or encoding.
Three of these primary studies focus on different strategies for cloud gaming.
The work in Gharsallaoui20171072 proposes a new strategy that takes into
consideration the quality of service parameters and the energy-consumption of
the decoder. The work in Ghergulescu2014 proposes an energy-aware adaptive
multimedia game-based e-learning framework that builds on top of the idea to
render the game on the server side and stream a recording of it to the
player’s device over the Internet. The work in ahmadi2014game introduces the
concept of game attention model (GAM), which reduces the bit rate of the
streaming video more efficiently.

In addition, the other two primary studies belong to the same authors and
focus on broadcast game playthroughs in real time. In 8057058, the authors
present a novel interaction-aware optimization framework to improve the energy
utilization and stream quality for mobile gamecasting. This framework is based
on their measurements revealing that there are strong associations between the
users’ touch interactions and the viewers’ gazing patterns. This study was
then extended by the authors in zhang2017exploring, where they include more
details about the framework and the evaluation and include a further
discussion.

In terms of the motivation, these primary studies focus on Battery life or the
Trade-off between energy consumption and other aspects. In fact, all of the
primary studies that tackled Battery life (i.e., Yan201813,
hosseini2012energy, Ghergulescu2014, zhang2017exploring, and 8057058) focus on
mobile devices. In contrast, two out of the three primary studies which
tackled the Trade-off between energy consumption and other aspects focus on
cloud computing (i.e., Gharsallaoui20171072 and ahmadi2014game), and the other
one focuses on gaming devices (i.e., Nery201997).

In terms of the layer, most of these primary studies present solutions on the
network layer (i.e., Ghergulescu2014, zhang2017exploring, 8057058,
ahmadi2014game). Nevertheless, there are studies on the rest of the layers,
except for the art layer. The solution in Nery201997 relies on the hardware
layer, the solution in Gharsallaoui20171072 relies on the software layer, and
the solution in Yan201813 relies on the design layer. The solution in
hosseini2012energy relies in both the software and the design layer.

##  4\. Discussion

Through a thorough analysis of the results obtained by our SLR, we have raised
a series of discussion points regarding the state of the art on Green Video
Games. The following subsections bring forth such a discussion.

###  4.1. Limitation of the Video Game issues

In Green Video Games, many works address issues that are related to the energy
sustainability of video games; however, not always this energy sustainability
is not always the main motivation. For example, Muhuri20182311 highlights
battery life as one of the drawbacks to user satisfaction. Therefore, by
consuming less power, the battery will last longer, which will in turn have a
positive impact on user satisfaction. The main motivation is not to develop
greener video games, but to improve battery life to the satisfaction of users.
There is indirection between the sustainability solution proposed by the study
and the motivation of the study.

Moreover, there is no common way to define an issue in Green Video Games. The
motivation and solution of one work can be interchanged depending on the
perspective of the research. For example, the motivation of 7351775 considers
that the battery in mobile devices is a critical reason for controlling
consumption, so it proposes an energy efficiency evaluation mechanism that
allows to control of the energy consumed by the CPU-GPU. Therefore, according
to this motivation, the study would be classified as a Battery life issue, but
according to its solution, it would be classified as a Modelling and
measurement of energy consumption issue. To avoid this ambiguity in the issue
definition, we propose always focusing on motivation.

In the case of solutions, there is a wide range of different solutions, so it
is difficult to identify commonalities for them. From the primary studies, we
identified two commonalities: devices and layers. Device refers to the
electronic device on which end-users play a video game, so the solution should
be evaluated on this device. Layer refers to the video game layer (i.e.,
hardware, network, software, etc.) on which components are affected or
enhanced by the solution. Both commonalities may be improved.

With regard to devices, there are a lot of similar categories: wearable
devices, smart devices, portable devices, handheld devices, or mobile devices.
When thinking about the definitions, there are small differences that
characterize each one of these categories. However, why are the differences
relevant for the studies and why do the studies focus more on mobile devices
instead of portable devices or handheld devices? As yet, we have not been able
to answer these questions. From the primary studies, there are no clear
reasons for improving the battery life on a mobile device instead of improving
the battery life on a portable device or a handheld device. A possible reason
for this breach on the device in study might be that the mobile games revenue
accounted for 52% ($93.2Bn) of the global market in 2021 while the console
games revenue accounted for 28% ($50.4Bn) (wijman2021games, ). We also have to
take into account that most consoles are not portable or handheld devices, so
only a fraction of this percentage belongs to these devices.

With regard to layers, they were identified by professional video game
developers to cover all of the relevant aspects of video game creation.
However, not all of these layers have been considered for improving the
sustainability of video games. Are these layers not relevant to creating green
video games? From our point of view, all layers can be used to improve the
sustainability of a video game, but researchers have not yet noticed the
potential of video game-exclusive layers. This is a topic that will be
discussed in depth later.

To sum up, in Green Video Games, we found two main limitations to identifying
issues: 1) the indirection between the sustainability solution and the
motivation of the study, and 2) the ambiguity in identifying an issue by its
motivation or its solution. These problems make it difficult to find related
works and compare results with other works. To solve these problems, we
propose defining Green Video Game issues based on motivation, device, and
layer. Although this definition has limitations, it can be a starting point
that will be improved depending on how the Green Video Game area evolves.

###  4.2. From Green Computing to Green Video Games

Concern for energy efficiency in video games is a relatively new field of
research. According to the demographic information extracted, interest has
been growing since around 2007 to the present day. However, although it is not
an extensive field of research, researchers have already proposed many
solutions to improve energy sustainability at the hardware, network, and
software levels.

Moreover, the first three layers of the issues (i.e., hardware, software, and
network) have been addressed at the same time since the beginning of the
research field. This is a fact that we consider surprising and completely
different from our expectations. Perhaps because of our personal experience or
because of our engineering careers, we assumed that the issues at the lowest
layer (hardware) would be investigated first and then the research would
progress to the higher layers when the results were consolidated.

However, the results obtained in this work have proven us wrong, which has
given us food for thought. Is this a consequence of the diversity of the
venues? or Could there be other reasons that we have not yet considered?

Of the 69 primary studies, 31 have been published in conferences and 25 in
journals. When comparing conferences, we have seen that there are 26 different
conferences and 21 different journals. In other words, the ratio between the
number of conferences and the number of studies is 0.84, and the ratio between
the number of journals and the number of studies is 0.84. Eighty-four percent
of the studies have been published in different venues. At first glance, there
appear to be no conferences or journals of reference or prominence in this
area of research.

Some conferences or journals focus more on hardware (e.g., IEEE International
Conference on Consumer Electronics), others on networks (e.g., International
Wireless Communications and Mobile Computing Conference), and still yet others
on software (e.g., International Conference on Software and Information
Engineering). However, there are also conferences and journals whose topics of
interest are more generic, such as the Design Automation Conference or IEEE
Systems Journal.

Considering the above data, none of the venues focus on Green Video Games, and
most have only published one paper in this field. This leads us to think that
engineers researching other Green Computing problems have started to show
interest in transferring their findings to the field of video games.

Green Computing is a research area whose findings and techniques in hardware,
networks, and software are already consolidated. Therefore, transferring these
findings and techniques to the field of video games, where there is high
energy consumption, would be a natural step from Green Computing. It would
also explain why most primary studies do not take into account other layers of
video games, such as design or art.

###  4.3. Energy-awareness

Based on our observations and the results obtained, we believe that there is a
lack of environmental awareness in video games. Everything points to the fact
that energy sustainability in video games is a secondary aspect when
designing, developing, and maintaining video games.

As we commented in the previous section, the main motivation of the studies is
not to improve the energy sustainability of video games. There is indirection
between the sustainability solution and the motivation of the study. For
example, many studies focus on video games installed on mobile devices and
their main motivation is to extend battery life to improve user satisfaction.
A prematurely depleted battery has a negative impact on the user experience
and can negatively affect the rating of the video game.

In addition to the impact on battery life, the progressive increase in the
complexity of video games (i.e., graphics, gameplay, interaction with other
users) also affects the number and cost of resources required to deploy the
video game, especially in the increasingly common MMOG. In these games, a
large number of users connect to the same server to enjoy online video games.
MMOGs require resources such as communication infrastructures and, above all,
data centers configured in the cloud.

It seems that the core around which all motivations revolve is the developer’s
profit. Improving some aspects of the video game means that there is a profit
for the developers. For example, if users are happy they keep playing our
game, so we have to solve the problems that frustrate users (battery
consumption, latency, CPU or GPU consumption, etc.). If we improve the design
of the data center we spend less money on the energy consumed.

What we want to emphasize is that sustainability should not be a side effect
that we benefit from when it happens. Rather, it should be one of the concerns
or objectives during the development of the video game. This is something that
is not yet being achieved according to the results obtained in this survey.
However, we encourage researchers to consider all of the advantages of
sustainable development for video games.

###  4.4. Trends and opportunities

Thanks to the survey carried out, we have been able to observe certain trends
and identify some points as future lines of research. These trends and future
lines are included below.

  1. (1)

We have noticed that some of the issues in Green Computing (i.e., Product
longevity, material recycling, and telecommuting) are not yet being taken into
account for Green Video Games. This leaves some open questions:  Do video
games have an expiration date? Can consoles or devices be reused? How should
gaming devices be recycled? Can cloud computing be considered a way to
minimize the material to be recycled? Can telecommuting be used to improve the
energy sustainability of video games?.

  2. (2)

We have observed that few studies propose solutions from the design layer and
none of the studies propose solutions from the art layer. Although not much
attention is being paid to these layers, we believe they could be considered
an opportunity. At the design layer, developers could take advantage of
elements designed to minimize power consumption. For example, in a video game,
a map could be designed with two main scenarios connected by a corridor. As
the player travels down that corridor, the main scenario is loaded in that
user’s direction. This way, if the user heads toward the second main scenario,
it will load, or vice versa. Instead of loading the entire game at the
beginning, the main scenarios are loaded based on the user’s location. In
addition, the game does not have to wait for the next scenario to load,
because while the loading occurs, the user is entertained by walking the
corridor. In the end, the energy and resources required to run a single main
scenario should be less than that required to run the entire game.

In fact, there are video games that employ the same or similar strategies.
However, strategies are employed without addressing how they influence the
energy consumption of the video game. Furthermore, they are often not
documented or reported, so the research community cannot benefit from the
ecological advances made. Therefore, there is still a need to assess and
report on these types of strategies from an ecological perspective.

Similarly, in the art layer, developers could consider how to save energy by
taking advantage of the artistic components of video games. For example, the
rhythm of a song may encourage the player to walk more slowly or dialogues may
make the player stop to read its content, which could provide the processor
with a few moments of relief or cede resources to other tasks. Also, simpler
textures and the usage of 2D meshes instead of 3D meshes would reduce the
resources needed by the GPU while maintaining an aesthetic that is attractive
for users. There are examples of successful commercial video games that use
these techniques, like Void Bastards and Cult of the Lamb.

  3. (3)

There are very few works that take into account artifacts, such as the game
engine or physics engine, despite their high energy consumption. The video
game genre is also barely considered, even though the energy consumption is
not the same for a single or multiplayer game played locally or online.

Furthermore, only eight of the primary studies address the development of
Green Video Games with specific content in mind (i.e., textures, lights, maps,
etc.). For example, complex textures not only add to the diversity of colors,
but they make the application of lighting effects, which have to be computed
for all 3D textures within the world, more expensive since this involves
modifying a large number of memory objects and stresses the memory system
hosseini2012energy. A decade has passed since the publication of this paper,
but it is the only paper that considers textures as a way to reduce energy
consumption.

Everything seems to indicate that these three aspects (artifacts, genre, and
content) can be leveraged to improve the effectiveness of the video game.
However, researchers should think from the point of view of video games,
rather than seeing them as just another application for their devices. Video
games have their artifacts, genres, content, and even other specific aspects
that we have not considered in this study. The question is how researchers can
take advantage of these aspects or others to develop greener video games.

  4. (4)

We have noted the lack of threats to validity, limitations, and /or future
work. None of the studies included threats to validity. The few included
limitations are too specific and there are no relation among them. Therefore,
the main limitation of Green Video Games today may be the youthfulness of this
area of research. Green Video Game research would improve rapidly if
researchers would compare and discuss the results of their studies with each
other. This seems difficult in the current state: 1) Green Video Games studies
are published in a wide diversity of venues, making it difficult to find
related publications or to identify all researchers working on related
articles; 2) There is ambiguity and redirection in the description of issues,
so it also complicates finding related articles; 3) Most studies do not report
data sets, devices, techniques, threats to validity, or limitations, making it
difficult to compare results fairly. Nevertheless, we hope that the overview
provided by this survey will help somewhat to minimize these problems and
encourage researchers to report their studies more fully.

##  5\. Threats to validity

###  5.1. Threats to validity

In this section, we use the classification of threats to validity of
(VAL:Wohlin12, ) and the study of threats to validity of systematic literature
reviews in (zhou2016map, ) to acknowledge the limitations of our survey.

Construct validity: This aspect of validity reflects the extent to which the
operational measures that are studied represent what the researchers have in
mind. Examples of issues are whether the concepts are defined clearly enough
before measurements are defined and the interaction of different treatments
when people are involved in more than one study (wohlin2003empirical, ).

  * •

To avoid the threat of non-specification of survey setting and sufficient
details, we describe the search string, the digital sources, the inclusion and
exclusion criteria, and the data extraction in detail.

  * •

To avoid the threat of inappropriate or incomplete search terms in automatic
search, we used the steps suggested by Kitchenham and Charters
(kitchenham2007guidelines, ). First, we used the PICO (Population,
Intervention, Comparison, and Outcomes) criteria to derive the major terms
from the RQs. Then, we identified synonyms for these terms. Finally, we
verified the search terms that other relevant surveys used in their search
strings.

  * •

To avoid the threat of incorrect search method, our search strategy is based
on the guidelines provided in (kitchenham2007guidelines, ;
kitchenham2013systematic, ; kitchenham2015evidence, ).

  * •

To avoid the threat of inappropriate exclusion and inclusion criteria, the
exclusion criteria were defined according to criteria commonly used in
surveys. Moreover, to minimize this threat, the inclusion criteria were
defined by domain experts in order to reduce the probability of not including
studies whose conclusion could be relevant for our research field.

  * •

To minimize the threat of inappropriate research questions, specific sessions
were held with all of the co-authors to discuss and assess which research
questions were most appropriate in order to provide an overview of Green Video
Games that would be useful to both researchers and practitioners.

Internal Validity: This aspect of validity is of concern when causal relations
are examined. There is a risk that the factor being investigated may be
affected by other neglected factors. In this work, the outcomes could be
affected by how the primary studies are selected.

  * •

To minimize the threat of misclassification of primary studies, the studies
were classified by two authors of the paper independently. Then, their results
were compared. In case of disagreement, the first and second authors held a
discussion in order to reach a consensus.

  * •

To avoid the threat of primary study duplication, duplication was included as
an exclusion criterion.

  * •

To minimize the threat of bias in study selection, the selection was conducted
by two authors of the paper. Therefore, the selection process was double-
checked. Moreover, they strictly followed the search strategy defined for the
selection.

  * •

To minimize the threat of bias in data extraction, similarly to the previous
threat, the extraction was conducted by two authors of the paper. Moreover,
the features for data extraction was identified before starting the extraction
process.

External Validity: This aspect of validity is concerned with to what extent it
is possible to generalize the findings, and to what extent the findings are of
relevance for other cases. There is a risk that the papers recovered by the
search are not representative of the target population.

  * •

To minimize the threat of incomplete research information or conclusions by
primary studies, we used snowballing to avoid missing relevant studies.

  * •

To avoid the threat of restricted time span, we did not include any temporal
restriction in our search method except for the date on which the search was
performed in the digital sources.

Conclusion validity: This aspect is concerned with to what extent the process
can be replicated with the same results. Researchers may influence the result
by looking for a specific outcome.

  * •

To minimize the threat of subjective interpretation of the extracted data, we
clearly separated the results from the discussion. The results show extracted
data for answering RQs. The discussion provides different points regarding the
state of Green Video Games according to the author’s interpretation of the
results.

  * •

To avoid the threat of replication of the study, the documents of the whole
search process (step by step) have been made publicly available.

##  6\. Conclusions

Today, there is a huge number of video game players that use everything from
game consoles that are created specifically for playing video games to
computers and mobiles. The combination of the large number of users with the
high computational requirements of modern video games has motivated research
on Green Video Games. However, this is recent research where publications are
scattered, so it is difficult to find related works, compare results, or even
determine trends or future lines for research work. For this reason, the main
objective of this survey is to provide an overview of Green Video Games
publications. This article also aims to serve to identify recommendations for
practitioners, outstanding research challenges, and several potential avenues
of future research for Green Video Games.

In this paper, the first survey in this field of study, we have systematically
reviewed the works in Green Video Games. We have reviewed a total of 2,637
papers, selecting 69 studies for further analysis. The results of this survey
bring light to the current state of the art. Through a detailed analysis of
the results, we propose a new way to define the Green Video Game issues based
on the motivation of the studies and the device and the layer on which the
solutions are evaluated. Then, based on the Green Video Games issues, we
analyzed the different applied techniques, the limitations and levels of
evidence, and some aspects that are specific to video games.

The overview provided by this paper can be useful for practitioners and
researchers alike when studying the current state of the field. Additionally,
through the recommendations provided, along with the discussion of the
results, our survey has the potential to produce a positive impact in the
Video Green Computing area highlighting that video games have specific aspects
that can be leveraged to develop even greener video games. Our study focuses
on enhancing the understanding of those works that improve the sustainability
of video games. This paper could be complemented by analyzing related fields
of research that are left open as interesting opportunities for future
literature exploration, e.g., in the form of reviews that tackle the use of
video games to raise people’s awareness of environmental impact or to teach
them ecological practices for a more sustainable environment.

## Acknowledgements

This work was supported in part by the Ministry of Economy and Competitiveness
(MINECO) through the Spanish National R+D+i Plan and ERDF funds under the
Project VARIATIVA under Grant PID2021-128695OB-I00, and in part by the
Gobierno de Aragón (Spain) (Research Group S05_20D).

## References

  * [1] Building a more sustainable future of gaming.  https://news.xbox.com/en-us/2023/01/11/xbox-carbon-aware-console-sustainability/.  Accessed: 2023-11-12.
  * [2] Geforce Now.  https://play.geforcenow.com/mall/#/layout/games.  Accessed: 2023-11-12.
  * [3] How many gamers are there? (new 2023 statistics).  https://explodingtopics.com/blog/number-of-gamers.  Accessed: 2023-11-12.
  * [4] Video gaming worldwide - statistics & facts.  https://www.statista.com/topics/1680/gaming/#topicOverview.  Accessed: 2023-11-12.
  * [5] Xbox Cloud Gaming.  https://www.xbox.com/es-es/play.  Accessed: 2023-11-12.
  * [6] Magnus Bang, Carin Torstensson, and Cecilia Katzeff.  The powerhhouse: A persuasive computer game designed to raise awareness of domestic energy consumption.  In International conference on persuasive technology, pages 123–132. Springer, 2006.
  * [7] Elizabeth Boyle, Madalina Manea, and Anne Karki.  Systematic review of the literature on computer games for education.  2013\.
  * [8] João Manuel Paiva Cardoso, José Gabriel de Figueiredo Coutinho, and Pedro C Diniz.  Embedded computing for high performance: Efficient mapping of computations using customization, code transformations and compilation.  Morgan Kaufmann, 2017.
  * [9] M Casals, M Gangolells, M Macarulla, Alba Fuertes, Rory Jones, Sabine Pahl, and M Ruiz.  Promoting energy users’ behavioural change in social housing through a serious game.  In eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2016, pages 573–577. CRC Press, 2017.
  * [10] Moulay L Chalal, Benachir Medjdoub, Nacer Bezai, Richard Bull, and May Zune.  Visualisation in energy eco-feedback systems: A systematic review of good practice.  Renewable and Sustainable Energy Reviews, 162:112447, 2022.
  * [11] N Chandrakant.  Green computing and mobile cloud computing inspired middleware for next generation.  International Journal of Advanced Research in Computer Science and Electronics Engineering, 2(7):542–545, 2013.
  * [12] Sonu Choudhary.  A survey on green computing techniques.  International Journal on Computer Science and Information Technology, 5(5):6248–6252, 2014.
  * [13] Thomas M Connolly, Elizabeth A Boyle, Ewan MacArthur, Thomas Hainey, and James M Boyle.  A systematic literature review of empirical evidence on computer games and serious games.  Computers & education, 59(2):661–686, 2012.
  * [14] Mahdi Dhaini, Mohammad Jaber, Amin Fakhereldine, Sleiman Hamdan, and Ramzi A Haraty.  Green computing approaches-a survey.  Informatica, 45(1), 2021.
  * [15] Tore Dyba, Torgeir Dingsoyr, and Geir K Hanssen.  Applying systematic reviews to diverse study types: An experience report.  In First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), pages 225–234. IEEE, 2007.
  * [16] Matthias Galster, Danny Weyns, Dan Tofan, Bartosz Michalik, and Paris Avgeriou.  Variability in software systems—a systematic literature review.  IEEE Transactions on Software Engineering, 40(3):282–306, 2013\.
  * [17] María Gutiérrez, Ma Ángeles Moraga, Félix García, and Coral Calero.  Green-in machine learning at a glance.  Computer, 56(6):35–43, 2023.
  * [18] Amanda K Hall, Enmanuel Chavarria, Vasana Maneeratana, Beth H Chaney, and Jay M Bernhardt.  Health benefits of digital videogames for older adults: A systematic review of the literature.  Games for health: research, development, and clinical applications, 1(6):402–410, 2012.
  * [19] Mohd Norasri Ismail, Rosziati Ibrahim, and Mohd Farhan Md Fudzee.  A survey on content adaptation systems towards energy consumption awareness.  Advances in Multimedia, 2013:3–3, 2013.
  * [20] Jay Kumar Jain and Chandrapal Singh Dangi.  A novel survey on green computing.  International Journal of Communication and Information Technology, 2020.
  * [21] Niraj K Jha.  Low power system scheduling and synthesis.  In IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No. 01CH37281), pages 259–263. IEEE, 2001.
  * [22] Daniel Johnson, Ella Horton, Rory Mulcahy, and Marcus Foth.  Gamification and serious games within the domain of domestic energy consumption: A systematic review.  Renewable and Sustainable Energy Reviews, 73:249–264, 2017.
  * [23] Korina Katsaliaki and Navonil Mustafee.  A survey of serious games on sustainable development.  In Proceedings of the 2012 Winter Simulation Conference (WSC), pages 1–13. IEEE, 2012.
  * [24] Barbara Kitchenham.  Procedures for performing systematic reviews.  Keele, UK, Keele University, 33(2004):1–26, 2004.
  * [25] Barbara Kitchenham and Pearl Brereton.  A systematic review of systematic review process research in software engineering.  Information and software technology, 55(12):2049–2075, 2013.
  * [26] Barbara Kitchenham and Stuart Charters.  Guidelines for performing systematic literature reviews in software engineering.  2007\.
  * [27] Barbara Ann Kitchenham, David Budgen, and Pearl Brereton.  Evidence-based software engineering and systematic reviews, volume 4.  CRC press, 2015.
  * [28] Qilin Li and Mingtian Zhou.  The survey and future evolution of green computing.  In 2011 IEEE/ACM International Conference on Green Computing and Communications, pages 230–233. IEEE, 2011.
  * [29] Luca Morganti, Federica Pallavicini, Elena Cadel, Antonio Candelieri, Francesco Archetti, and Fabrizia Mantovani.  Gaming for earth: Serious games and gamification to engage consumers in pro-environmental behaviours for energy efficiency.  Energy Research & Social Science, 29:95–102, 2017.
  * [30] Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz.  Guidelines for conducting systematic mapping studies in software engineering: An update.  Information and software technology, 64:1–18, 2015.
  * [31] Nasir Rashid and Siffat Ullah Khan.  Green agility for global software development vendors: A systematic literature review protocol.  Proceedings of the Pakistan Academy of Sciences, 52(4):301–313, 2015.
  * [32] Muhammad Salam and Siffat Ullah Khan.  Developing green and sustainable software: Success factors for vendors.  In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), pages 1059–1062. IEEE, 2016.
  * [33] Muhammad Salam and Siffat Ullah Khan.  Identification of practices for green software development in multi-sourced project: a systematic literature review protocol.  International Journal of Computer Applications, 141(8):33–38, 2016\.
  * [34] Junaid Shuja, Raja Wasim Ahmad, Abdullah Gani, Abdelmuttlib Ibrahim Abdalla Ahmed, Aisha Siddiqa, Kashif Nisar, Samee U Khan, and Albert Y Zomaya.  Greening emerging it technologies: techniques and practices.  Journal of Internet Services and Applications, 8(1):1–11, 2017\.
  * [35] G Sobers Smiles David, K Ramkumar, P Shanmugavadivu, and PS Eliahim Jeevaraj.  Introduction to cloud resource management.  Autonomic Computing in Cloud Resource Management in Industry 4.0, pages 1–16, 2021.
  * [36] Steven Sorrell et al.  Digitalisation of goods: a systematic review of the determinants and magnitude of the impacts on energy consumption.  Environmental Research Letters, 15(4):043001, 2020.
  * [37] Marios Stanitsas, Konstantinos Kirytopoulos, and Elise Vareilles.  Facilitating sustainability transition through serious games: A systematic literature review.  Journal of cleaner production, 208:924–936, 2019.
  * [38] Tom Wijman.  The games market and beyond in 2021: The year in numbers, 2021.  [Online; accessed 22-December-2021].
  * [39] Claes Wohlin.  Guidelines for snowballing in systematic literature studies and a replication in software engineering.  In Proceedings of the 18th international conference on evaluation and assessment in software engineering, pages 1–10, 2014.
  * [40] Claes Wohlin, Martin Höst, and Kennet Henningsson.  Empirical research methods in software engineering.  In Empirical methods and studies in software engineering, pages 7–23. Springer, 2003.
  * [41] Claes Wohlin, Per Runeson, Martin Höst, Magnus C Ohlsson, Björn Regnell, and Anders Wesslén.  Experimentation in Software Engineering.  Springer Science & Business Media, 2012.
  * [42] Xianfeng Wu, Shuli Liu, and Ashish Shukla.  Serious games as an engaging medium on building energy consumption: A review of trends, categories and approaches.  Sustainability, 12(20):8508, 2020.
  * [43] Xin Zhou, Yuqin Jin, He Zhang, Shanshan Li, and Xin Huang.  A map of threats to validity of systematic literature reviews in software engineering.  In 2016 23rd Asia-Pacific Software Engineering Conference (APSEC), pages 153–160. IEEE, 2016.

* plain selectedPapersVF