Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2017.DOI

This work was supported in part by National Key R&D Program of China
(2019YFE0196400), Joint Project of China Mobile Research Institute & X-NET
(R202111101112JZC04), National Natural Science Foundation of China (61771358,
61901317, 62071352), Fundamental Research Funds for the Central Universities
(JB190104), Science and Technology Plan of Xi’An City
(2019217014GXRC006CG007-GXYD6.1), Joint Education Project between China and
Central-Estern European Countries (202005) and the 111 Project (B08038). L.
Hanzo would like to acknowledge the financial support of the Engineering and
Physical Sciences Research Council projects EP/P034284/1 and EP/P003990/1
(COALESCE) as well as of the European Research Council’s Advanced Fellow Grant
QuantCom (Grant No. 789028).

Corresponding author: Lajos Hanzo (e-mail: lh@ecs.soton.ac.uk).

# Open-Source Multi-Access Edge Computing for 6G: Opportunities and Challenges

LIQIANG ZHAO1  GUORONG ZHOU1  GAN ZHENG2  CHIH-LIN I3  XIAOHU YOU4  LAJOS
HANZO5  The State Key Laboratory of Integrated Services Networks, Xidian
University, Xi’an 710071, China (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS> The Wolfson School of Mechanical, Electrical and
Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU,
U.K. (e-mail<EMAIL_ADDRESS> Green Communication Research Center, China
Mobile Research Institute, Beijing 100053, China (e-mail<EMAIL_ADDRESS>The National Mobile Communications Research Laboratory, Southeast University,
Nanjing 210096, China (e-mail<EMAIL_ADDRESS> The School of Electronics and
Computer Science, University of Southampton, Southampton SO17 1BJ, U.K.
(e-mail<EMAIL_ADDRESS>
###### Abstract

Multi-access edge computing (MEC) is capable of meeting the challenging
requirements of next-generation networks, e.g., 6G, as a benefit of providing
computing and caching capabilities in the close proximity of the users.
However, the traditional MEC architecture relies on specialized hardware and
its bespoke software functions are closely integrated with the hardware, hence
it is too rigid for supporting the rapidly evolving scenarios in the face of
the demanding requirements of 6G. As a remedy, we conceive the compelling
concept of open-source cellular networking and intrinsically amalgamate it
with MEC, which is defined by open-source software running on general-purpose
hardware platforms. Specifically, an open-source MEC (OS-MEC) scheme is
presented relying on a pair of core principles: the decoupling of the MEC
functions and resources from each other with the aid of network function
virtualization (NFV); as well as the reconfiguration of the disaggregated MEC
functions and resources into customized edge instances. This philosophy allows
operators to adaptively customize their users’ networks. Then, we develop
improved networking functions for OS-MEC decoupling and discuss both its key
components as well as the process of OS-MEC reconfiguration. The typical use
cases of the proposed OS-MEC scheme are characterized with the aid of a small-
scale test network. Finally, we discuss some of the potential open-source-
related technical challenges when facing 6G.

###### Index Terms:

Open-Source Cellular Network, Multi-access Edge Computing, 6G, Network
Function Virtualization, Service-Based Architecture, Management and
Orchestration.

=-15pt

##  I Introduction

By integrating communication and information technologies, multi-access edge
computing (MEC) [1, 2, 3] intrinsically amalgamates the computing and caching
capabilities in the immediate proximity of the users, which reduces the
transmission delay and improves the resource utilization at the edge. As a
further benefit, the emergence of MEC alleviates both the potential data
transmission congestion of the transport network (TN) as well as the data
processing burden imposed on the core network (CN). Therefore, in December
2014, the European telecommunications standards institute (ETSI) has initiated
MEC standardization for continuously updating and improving the MEC reference
architecture and its applications [4]. Many operators and vendors provide a
convenient computing and caching environment at the edge by relying on ETSI’s
MEC architecture [5, 6, 7].

With the continual growth of the mobile Internet traffic, next-generation
networks are expected to operate at rates up to Tbps and delays lower than 1ms
[8, 9, 10]. MEC is considered as a potential enabling technology of the sixth
generation (6G), capable of satisfying enhanced service requirements [11]. For
example, MEC can guarantee ultra-low transmission delay and satisfy flawless
4k/8k video broadcast by processing computation-intensive data or caching
ultra-high-definition (UHD) videos at the edge [12, 13]. However, the exiting
MEC reference architecture is built upon specialized hardware and its software
functions are closely integrated with the hardware, which is inconvenient in
the face of rapidly evolving scenarios, when aiming for satisfying the
demanding requirements of 6G. As a remedy, the above challenges may be met by
introducing the open-source (OS) cellular network concept into MEC for
allowing operators to adaptively customize their users’ networks.

Open-source software [14] has a very long history, which can be traced back to
the organizations of SHARE and DECUS in 1950s. In the following years,
ARPANET, UNIX, Linux and Android were developed for altruistic public sharing.
By contrast, OS cellular networks are still in their infancy, despite the
associated benefits. Explicitly, in the OS cellular network, an open
infrastructure would enable flexible network reconfiguration based on the
specific requirements of diverse scenarios, and open source software would
enable users to develop the future network in a collaborative open-access
manner. Such an OS cellular network has to rely on the integration of 6G
candidate technologies, such as network function virtualization (NFV) [15],
software-defined networking (SDN) [16], network slicing [17] and MEC [18],
etc. Among them, SDN/NFV serve as the core drivers, which should additionally
be integrated with MEC for cell-edge performance enhancement in support of
customized radio access networks (RANs).

TABLE 1: A comparative summary of contributions of the salient existing works. Feature |  | OAI
---
[24]
| Open5G
---
[25]
| O-RAN
---
[20, 26]
| ONAP
---
[27]
| OPNFV
---
[28]
| ETSI’s
---
MEC[4]
| Akraino
---
[21]
| SDEC
---
[29]
| NDN-
---
ECC[30]
Proposed
Focusing on MEC |  |  |  |  |  | ✓ | ✓ | ✓ | ✓ | ✓
| Applied in 6G network
---
|  | ✓ | ✓ | ✓ |  | ✓ |  | ✓ | ✓
| Open-source architecture
---
✓ | ✓ | ✓ | ✓ | ✓ |  | ✓ | ✓ | ✓ | ✓
| Application and resource
---
decoupling using NFV
✓ | ✓ |  | ✓ | ✓ | ✓ | ✓ | ✓ |  | ✓
| Network functions
---
decoupling by SBA
|  |  | ✓ |  |  |  |  |  | ✓
| Using MANO technology
---
| ✓ | ✓ | ✓ |  | ✓ | ✓ | ✓ |  | ✓
| Reconfiguration by
---
templates and instances
|  |  |  |  |  |  |  |  | ✓
| Supporting multiple
---
access on RAN side
| ✓ |  |  |  | ✓ |  | ✓ |  | ✓
| Focusing on upper
---
layers of architecture
|  | ✓ | ✓ |  |  |  |  |  | ✓
| Using Docker and
---
Kubernetes technologies
|  |  | ✓ |  |  | ✓ |  |  | ✓

Substantial efforts have been invested in promoting the progress of OS
networks [19]. The Open-Air-Interface (OAI) community founded in Europe
exploited a fully-fledged fourth generation (4G) protocol stack based on
commercial off-the-shelf (COTS) hardware. As a further advance, the Open5G
community founded in China developed the concepts, technologies and platforms
of open-source fifth generation (5G). The O-RAN alliance [20] founded by the
global industry supported by academics has endeavoured to evolve RANs around
the world. Additionally, the Linux Foundation (LF) launched the open network
automation platform (ONAP) for orchestration. As for virtualization, the open
platform of NFV (OPNFV) was conceived. Recently, there have also been a few
open-source efforts on MEC [21]. For example, the Akraino project built by LF
offers a pair of blueprints, namely the 5G MEC/slice and the micro-MEC system,
but unfortunately it remains focused on the virtualization layer of the MEC
reference architecture. Based on all these efforts, we present a new paradigm
for developing an all-encompassing open-source MEC (OS-MEC) scheme.

The contributions of this paper are summarized as follows.

  1. 1.

Using ETSI’s MEC architecture as a springboard, this paper introduces an OS-
MEC framework to facilitate customized services for any potential scenario in
6G network, which is built on a pair of key principles, namely on network
decoupling and reconfiguration.

  2. 2.

With reference to the service-based architecture (SBA) of the 5G core network
(5GC) [22], a service-based MEC layer is developed for the OS-MEC scheme for
decomposing the tightly coupled service functions into multiple independent
network functions (NFs), thereby achieving MEC decoupling.

  3. 3.

In order to integrate these decoupled NFs as required and to reconfigure a MEC
system, the concept of templates and instances is implemented as part of the
NF’s management and orchestration (MANO) scheme, eventually generating a
customized entity for supporting complete services.

  4. 4.

Several typical use cases are demonstrated based on our test network for
validating its flexibility and customization. Our novel contributions are also
contrasted at a glance boldly and explicitly to the state-of-the-art in Table
1.

The rest of this paper is organized as follows. We first introduce the basic
concepts and key technologies of OS cellular networks in Section II. In
Section III, we present our OS-MEC framework, its service-based MEC layer,
interface, and the NF’s MANO. In Section IV, our small-scale test network, our
use cases and our demonstrations are presented. In Section V, we elaborate on
some potential technical challenges. Finally, we conclude in Section VI.

##  II The Open-Source Network

In this section, our vision of an OS cellular network relies on four key
elements: the cloud domain including CN; the edge domain including distributed
MEC servers; the heterogeneous terminal domain; and the network domain
connecting the cloud-edge-terminal domain, which includes a RAN linking the
edge and terminal domains, and a transport network linking the cloud and edge
domains, as shown in Fig. 1.

In the cloud domain, the OS CN architecture has been well documented. For
example, SBA has been identified as a basic element of the 5GC architecture in
the 3rd Generation Partnership Project’s (3GPP) standards, which exhibits a
high grade of flexibility and scalability. In SBA, the originally coupled
network functions are partitioned into multiple independent functional blocks,
where each of them provides one or more services that can be used by any of
the other functional blocks. Therefore, all functional blocks can communicate
with each other in order to support the users with customized applications, as
if they were connected to a service bus. In the edge domain, the OS-MEC
concept will be discussed in the next section. However, in the network domain,
the OS RAN architecture has not as yet been defined, because it is hampered by
its increased real-time complexity requirement. To achieve this, some
promising solutions, such as RAN decoupling and reconfiguration, have been put
forward. Firstly, the tightly coupled RAN elements, functions and resources of
conventional networks have to be completely split. Then, they could be
reassembled dynamically according to a user’s requirements, thus providing
customized virtual RANs. In a nutshell, the OS concept facilitates a
convenient LEGO-block based construction of 6G RANs.

Figure 1: OS cellular network.

###  II-A RAN Decoupling

The tightly coupled 4G cellular networks rely on a rigid closed operating
system. Hence at least four different RAN decoupling methods have been
conceived for orchestrating a beneficial degree of openness in 6G, namely
hardware/software decoupling (HSDe) [31], control plane and user plane
splitting (CUPS) [32], central unit and distributed unit splitting (CDUS)
[33], as well as downlink and uplink decoupling (DUDe) [34]. Since they have
different pros and cons, there is no wide agreement concerning the best OS RAN
architecture. It is worth noting that HSDe and CUPS constitute the basis of OS
cellular networks, while CDUS and DUDe further exploit the flexibility and
openness of cellular networks.

HSDe decouples the resources and functions from the physical facilities. Given
the mature family of NFV technologies, the network resources can be abstracted
and shared among different users, and diverse services can be supported by the
same infrastructure, thus separating the functionalities from the underlying
infrastructure.

The idea of CUPS stems from SDN, which decouples the control signaling from
the data transmission. Then, it further expands to cellular networks by
deploying control base stations (CBSs) in the most friendly lower frequency
band and data base stations (DBSs) in higher frequency bands to form the
control plane (CP) and user plane (UP), respectively.

CDUS represents one of the key technologies for the next-generation cloud RAN,
which focuses on separating the traditional evolved node base station (eNB)
into the radio transceiver (distributed unit) and the logical center (central
unit) from the perspective of the OS protocol stack.

The tightly coupled downlink (DL) and uplink (UL) transmission limits the
flexibility of a user’s connections. Upon adopting DUDe via dual-connectivity,
where a user connects to a macro base station (MBS) and a small base station
(SBS) for its DL and UL delivery respectively, flexible user association and
high energy efficiency can be achieved.

###  II-B RAN Reconfiguration

Upon using RAN decoupling technologies, conventional RANs have been
softwarized and split into independent virtual NFs. Then, to support diverse
application (APPs), only the necessary decoupled NFs will be chosen and
assembled to construct RANs on a demand basis. For reconfiguring 6G RAN
efficiently, there have been at least two possible solutions, namely RAN
slicing and MANO.

RAN slicing [17] allows the virtual NFs and radio resources in RAN to be
dynamically allocated to various services. Explicitly, when a specific service
request arrives, RAN slicing selects the necessary NFs using the resources
allocated, and then encapsulates them to form a customized virtual network.

MANO is also an effective technique of achieving RAN configuration, and it can
be applied more flexibly to different network scopes than RAN slicing. Among
them, the management part is responsible for the overall monitoring of RANs,
including life cycle management, fault detection, accounting and security
monitoring [35]. The orchestration part [36] supports the NFs and uses the
underlying resources for reassembling the RAN, which mainly includes two sub-
parts: choosing the appropriate NFs and appropriately scheduling them for a
certain service.

###  II-C RAN Capability Enhancement

MEC supports local computing and storage resources, which can be integrated
with the open RAN both for providing the users with low-latency services and
for reducing the transport network’s burden [37]. Although the OS RAN
architecture has not as yet been defined, an OS-MEC scheme will be established
and detailed in our paper, which relies on the above decoupling and
reconfiguration technologies.

Furthermore, 6G will have an ever increasing complexity in support of its
compelling applications, heterogeneous networking architecture, and diverse
resources. Hence the conventional network management methods (i.e.,
deployment, resource allocation and operation) associated with human-
controlled philosophies will become inadequate, calling for sophisticated
artificial intelligence (AI) techniques [9] in the RAN, for achieving self-
organizing, automated operation and CapEx/OpEx savings.

##  III The Open-Source MEC

In this section, we incorporate the fundamental concepts of OS networks into
MEC by relying on decoupling and reconfiguration, and propose the OS-MEC
concept for improving the openness and flexibility of the emerging MEC
systems. Explicitly, by developing a service-based MEC layer, we decompose the
tightly coupled service functions into multiple independent NFs and support
them flexibly using a simple service-based interface (SBI). Next, by
introducing the convenient template and instance concept, NF’s MANO is
proposed for reconfiguring MEC, in which the OS-MEC’s templates are predefined
and then instantiated when needed.

###  III-A OS-MEC Framework

The OS-MEC framework conceived is shown in Fig. 2. The entire framework
includes the application plane and MANO plane, where the former is mainly
responsible for data processing and transmission, involving the infrastructure
layer, virtualization layer, service-based MEC layer, and application layer
from bottom to top. By contrast, the latter is composed of the virtualized
infrastructure manager (VIM) and MANO, which completes the coordination and
resource management of the OS-MEC system. The pair of adjacent layers
communicate with each other through standard interfaces and cooperate for
completing the MEC services requested by users.

Figure 2: OS-MEC framework.

As seen in Fig. 2, the infrastructure layer is at the lowest level, covering
all computing, caching and communication resources of the system.
Specifically, the central processing unit (CPU) provides high-performance
computing power for the service-based MEC layer right below the application
layer at the top of Fig. 2. The memory and the hard disk drive (HDD)/solid
state drive (SSD) are the main components of the caching resources in the
infrastructure layer of Fig. 2. The communications resources of this layer
include the bandwidth as well as the eNB, next-generation node base station
(gNB) and access point (AP), as shown in the bottom layer of Fig. 2.

At the virtualization layer of Fig. 2, based on the NFV concept, the
underlying three-dimensional resources may be decoupled from the dedicated
hardware and abstracted into a resource pool that could be shared by various
NFs at the service-based MEC layer of Fig. 2. Then, OS-MEC creates multiple
Docker containers or virtual machines (VMs), which are run on the underlying
resource pool and could simultaneously support different NFs for providing
customized services [38], as seen at the virtualization layer of Fig. 2.

The service-based MEC layer of Fig. 2 constitutes the core part of OS-MEC,
which includes a unified SBI and diverse NFs, where the SBI can connect these
NFs together based on the unified stateless hypertext transfer protocol (HTTP)
for ensuring that they can communicate directly with each other when needed.
Furthermore, we decouple the originally centralized service functions into
independent NFs. Specifically, the user plane function (UPF) of the service-
based MEC layer in Fig. 2 is borrowed from 5GC for OS-MEC, in order to lend
the 5G new radio (NR) capabilities to OS-MEC. Subsequently, to enhance OS-MEC,
we download several other NFs at the control plane of 5GC and develop several
new NFs. These NFs are transparent to each other and can also be combined at
will, so that they could be activated and released by all users instantly in
support of their customized services.

Actually, the user does not have to rely on the specific details of OS-MEC.
Instead, they will use various APPs at the application layer to gain access to
customized services. For simplicity, only two APPs are considered in this
paper for processing caching and computing requests, respectively, i.e., the
high throughput and the intensive computation scenarios of the application
layer seen at the top of Fig. 2.

Additionally, the MANO plane at the right of Fig. 2 is responsible for
managing these NFs as well as APPs, and for scheduling the VIM to allocate
resources. As mentioned earlier, MANO is in charge of MEC reconfiguration,
where the OS-MEC’s templates and instances are proposed for implementing
flexible reconfiguration, as and when required. The VIM of the MANO plane in
Fig. 2 manages the virtualized resources according to MANO’s commands, so as
to ensure that appropriate computing, caching and communication resources are
supplied for the upper layers of Fig. 2.

Since there is rich literature on the design and implementation of both the
virtualization layer and on the infrastructure layer [4, 21, 24, 25, 28, 29,
30], we mainly consider the service-based MEC layer and the MANO plane of OS-
MEC.

###  III-B MEC Decoupling

In order to decouple the MEC functions, we conceive the service-based MEC
layer of the OS-MEC framework, which evolved from the traditional MEC layer,
mainly composed of the SBI and the service-oriented NFs. We extract the
service-based MEC layer from Fig. 2, and portray its detailed structure in
Fig. 3.

Figure 3: A detailed view of the service-based MEC layer of Fig. 2.

The SBI connects all the NFs together for facilitating their interface
programming and implementation. The north-bound interface (NBI) is an
application program interface (API) provided for the application layer to
interact with the service-based MEC Layer in order to obtain services. The
east-bound interface (EBI) is an API for NF’s MANO to interact with the
service-based MEC Layer, which is mainly used to manage and orchestrate both
the resource allocation and life cycle of NFs. On the basis of using the same
HTTP, we design a unified Representational State Transfer-ful (RESTful) [23]
API for the SBI, NBI and EBI, which is lightweight and can be read both by
people and machines easily. Thus, the service-based MEC layer provides direct
communications between the NFs, the MANO and the NFs, as well as the NFs and
APPs, thereby reducing the interface-complexity and the protocol-inconsistency
in the traditional MEC architecture [18].

In this paper, all service-oriented NFs are independently deployed in the
Docker containers mentioned in Fig. 2 in order to become virtualized. There
are six basic NFs in our proposed OS-MEC of Fig. 3. Specifically, the MEC NFs
learned from 5GC include the unified data management (UDM), the NF repository
function (NRF) and UPF seen in Fig. 3. Furthermore, the communication protocol
conversion function (CPCF), the service registry function (SRF) and the
application selection function (ASF) of Fig. 3 are newly developed after
considering the context of OS-MEC. Below we briefly describe the functional
differences of UDM and NRF applied both in OS-MEC as well as 5GC, followed by
the portrayal of our proposed CPCF, SRF and ASF of Fig. 3.

Figure 4: A detailed view of the NF’s MANO of Fig. 2.

  * •

UDM is used for unified data storage and management. In contrast to the 5GC
UDM, the OS-MEC UDM does not have to deal with the issue of data structure
compatibility because of its semi-homogeneous data. It mainly manages the data
through the MySQL database management system [39], and implements data
insertion, search, update and deletion in the data tables of specific
services. OS-MEC UDM can also provide standardized interfaces for SRF and ASF
to ensure flexible access and real-time data update.

  * •

NRF is a repository provided for centralized storing of NFs. OS-MEC further
decouples the 5GC NRF, thus the OS-MEC NRF is only responsible for the storage
of NFs and APPs, while the service registration is repackaged as SRF, and the
service discovery is integrated into MANO. Due to the limited edge resources,
general NFs (including SRF, CPCF, UDM and NRF) use local storage, while
dedicated NFs, e.g., ASF and APPs are stored in the remote image repository.
As for the latter, the OS-MEC server will rely on specific NF and APP images
to provide services only when needed.

  * •

CPCF can complete the communication protocol conversion. In the OS-MEC system,
the stateless HTTP is mainly used for data exchange between NFs. However, the
protocols of users requesting edge services are heterogeneous, so CPCF has to
analyze and re-encapsulate them into a unified HTTP protocol for supporting
compatibility of the system.

  * •

SRF is responsible for the service registration of new APPs. When a new APP
gets to OS-MEC, SRF first registers the APP to UDM and updates the APP-related
parameters to data tables for improving the information exchange between APP
and NFs. Then, SRF stores the APP in the remote NRF image repository and
provides an external access interface for allowing all the other edge servers
to use the image.

  * •

ASF directly interacts with APPs through NBI for service selection.
Specifically, compelling services could be provided in the form of APPs, such
as intensive computation and high throughput services. Acting as the
middleware between MEC APPs and other NFs, ASF is used both for the specific
location of the requesting APPs and for communication between APPs and the
other basic NFs.

The NF’s MANO of Fig. 3 can flexibly schedule the NFs according to the
specific service types, since the unified HTTP is used as EBI between the
service-based MEC layer and NF’s MANO, and is also used as SBI in the service-
based MEC layer. With all the NFs deployed in the Docker containers in our
solution, Kubernetes [40], the open-source implementation of the container
cluster management, is advocated for centrally managing all the Docker
containers playing the role of MANO. Its specific architecture will be
introduced in the next subsection.

Based on the above NFs, we are ready for characterizing the basic
functionalities of OS-MEC. Hence anyone could develop his/her own NFs for
supporting new applications.

###  III-C MEC Reconfiguration

As the MEC management is not directly related to the reconfiguration of OS-
MEC, the joint management of MEC and NFV proposed by ETSI can still be used in
OS-MEC, albeit the conception of service-based MEC layers is bound to impose
challenges on the orchestration scheme. Therefore, by means of Kubernetes, we
present OS-MEC’s templates and instances of NF’s MANO, which supports the
flexible scheduling of NFs, and any further MEC reconfiguration. More
explicitly, some templates are pre-defined for the corresponding applications,
as exemplified by a computation-intensive template. However, when no user asks
for this application, we do not allocate the related resources to the
template, hence, the template is empty. By contrast, as shown in Fig. 4, if a
user requests this application, we shall activate the template by assigning
computing, caching and communication resources as an instantiation.

####  III-C1 NF’s MANO

A detailed view of the NF’s MANO extracted from Fig. 2 is shown in the middle
(blue) part of Fig. 4, where the right side represents the predefinition and
selection of templates, while the left side represents the templates’
instantiation based upon Kubernetes [40], which is a container-based cluster
management platform. The VIM seen at the bottom right of Fig. 4 is used for
scheduling the underlying resource pool to arrange for the immediate resource
deployment for instantiation, so as to avoid the assignment of excessive
resources.

Observe in Fig. 4 that there is always a main node and either one or several
secondary nodes in Kubernetes, where the former is in charge of managing
Kubernetes, while the latter is in charge of the orchestration of NFs. In a
secondary node, there are two basic components dedicated to instantiation in
NF’s MANO, namely Pod and Kubelet as seen in Fig. 4. As defined in [40], Pod
is the smallest unit of deploying and monitoring NFs in Kubernetes, which can
run several Docker containers simultaneously. Kubelet [40] of Fig. 4 maintains
the life cycle of Pods and the communication between the master and the
secondary nodes. In the master node, there are three basic components, namely
the scheduler, controller and API server. The API server provides the only
entry for secondary nodes to the application layer, i.e., Pods communicate
with the given templates selected by the user through the API server for
implementing MEC reconfiguration. The Scheduler and Controller of Fig. 4
manage and monitor all secondary nodes, e.g., for fault detection. Moreover,
there is a Kubernetes’ data center represented by Etcd of Fig. 4 for storing
the states of the above components.

####  III-C2 OS-MEC Template

The template seen close to the right of Fig. 4 is provided for extracting the
common characteristics of a class of problems. Given this idea, a static OS-
MEC template is predefined, e.g., for selecting appropriate NFs and for
predefining the related parameters according to the specific type of APPs.
Again, we consider two APPs, namely high throughput and intensive computation,
as seen at the right of Fig. 4. These are examples of describing the OS-MEC
templates, where the former is provided for high-throughput multimedia video
services, while the latter is mainly for computing intensive services.
Naturally, there are some differences between the two templates in terms of
the NFs selection and data table definition in UDM, as detailed in Fig. 5.

Explicitly, observe in Fig. 5 that the OS-MEC template is composed of three
tiers, including Managed NFs, Attributes and Actions. In this context, all NFs
have to communicate directly with the Managed NFs at the top layer, which are
responsible for monitoring the templates’ status and orchestrating NFs. The
middle-layer of Fig. 5 representing the Attributes acts as the database of
templates, where the associated parameters are stored in the UDM’s data
tables. Finally, the underlying Actions seen at the bottom of Fig. 5 are
provided for completing a range of customized functions by selecting
appropriate NFs as well as APPs and for updating the predefined UDM parameters
in real time.

We partition the computation-intensive and the high-throughput templates into
the shared parts (overlap of the templates in Fig. 5) and the dedicated non-
overlapping parts of each. The shared part is irrelevant for the APPs, it
essentially ensures the normal operation of the system, while the dedicated
template is the key for supporting OS-MEC in providing specific services for
the corresponding APP. For example, both the computation-intensive and the
high-throughput templates have to complete service registration provided by
SRF, protocol conversion completed by CPCF and data updates by UDM. However,
the dedicated ASF and APP of the computation-intensive template are
responsible for completing the computations based on the source-data, as seen
at the bottom left of Fig. 5 and for setting up a charging function. By
contrast, the dedicated ASF and APP of the high-throughput template process
video caching and analyze the video popularity, as observed at the bottom
right of Fig. 5.

Figure 5: A detailed view of the OS-MEC templates introduced at the right of
Fig. 4.

####  III-C3 OS-MEC Instance

The OS-MEC template of Fig. 5 cannot directly provide services and we do not
allocate the related resources for the template. As a result, users can only
gain access to the edge services through instances. The instantiation
determines whether the OS-MEC system can run stably, which is the key for
reconfiguring MEC.

The instantiation process mainly includes the following three steps. Firstly,
when a user requests edge services, such as intensive computation and high
throughput, the MANO of Fig. 4 configures the operating environment of NFs,
i.e. the API Server of Fig. 4 locates the idle Pod and updates the relevant
information to Etcd of Fig. 4, which provides basic environmental support for
instantiation. Secondly, the MANO of Fig. 4 schedules VIM for allocating the
specified virtual computing, caching and communication resources to users.
Finally, under the specifically configured operating environment and the
resources allocated, MANO actually runs the predefined NFs and APPs of the
template, i.e., Kubelet runs the Pod that specifically deploys NFs and APPs.
Therefore, the dedicated MEC instance has been created.

Figure 6: OS-MEC workflow.

In Fig. 6, we summarize the workflow of OS-MEC, commencing from the instant
when a user sends a service request until the edge service is instantiated.
Specifically,

  1. 1.

when a user sends a service request to the OS-MEC, such as the intensive
computation service request, CPCF firstly actions the protocol identification.

  2. 2.

Upon receiving a HTTP request, CPCF does not need to perform the protocol
conversion; otherwise, it re-encapsulates the service request using HTTP. In
this way, CPCF can send the unified HTTP request to MANO.

  3. 3.

Then, MANO selects the computation-intensive template for the user.
Explicitly,

    1. (a)

the predefined parameters of this template are added to the specific data
table in UDM.

    2. (b)

By querying the NFs and APP information specified by the template in UDM, MANO
selects the appropriate NFs and APPs stored in NRF.

    3. (c)

MANO updates the predefined parameters in UDM’s data table, completing the
selection of templates.

  4. 4.

Nextly, the instantiation process is implemented by the following three steps:
the environmental configuration, resource allocation, and NF activation and
APP activation.

  5. 5.

Finally, OS-MEC provides a computation-intensive edge service to the user in
the form of APP by the service interface.

At this point, we have succeeded in creating the OS-MEC scheme by completing
the MEC decoupling and reconfiguration.

##  IV Demonstration in Test Network

In this section, we critically appraise the proposed OS-MEC scheme in the test
network deployed at Xidian University and then characterize a pair of use
cases, namely the intensive computation and the high throughput scenarios. Our
test network is shown in Fig. 7. A 3GPP R10 based cellular network including
an evolved packet core (EPC) and six eNBs was built based on open-source
software provided by OAI [41] and general-purpose hardware, namely an Intel
Core<EMAIL_ADDRESS>CPU and 16 GB random access memory (RAM). A commercial
wireless router was used as an AP to deploy a WiFi network, which can support
multiple access. Then, we deploy all the network elements in Docker containers
to arrange for them to become virtualized, yielding a virtualized EPC (vEPC)
and virtual NFs (vNFs). Subsequently, the user plane of both the serving
gateway (SGW) and of the packet data network gateway (PGW) is moved from vEPC
into the OS-MEC server using an Intel Core<EMAIL_ADDRESS>CPU and 32 GB RAM.
The control plane, i.e. the home subscriber server (HSS) and the mobility
management entity (MME), remains in the vEPC. Finally, based on Kubernetes,
NF’s MANO including its templates and instances is implemented to furnish
users with customized services.

For instance, in the high throughput use case providing a video caching
service, the user firstly accesses the test network of Fig. 7 by selecting an
appropriate access mode, e.g., eNB. Then, the network has to confirm, whether
the requested video is stored in the OS-MEC server. If so, the high throughput
service may indeed be directly completed in the OS-MEC server by the specific
workflow seen in Fig. 6. By contrast, if the requested video cannot be found
in the OS-MEC server, then the vSGW and vPGW in the OS-MEC server will connect
to the cloud server of the campus network through the switch to access the
required resources.

In the intensive computation use case, we provide three services, i.e. sum and
prime sum computation as well as face recognition. These three services are
requested in three independent instances respectively, which are created by
the same computation-intensive template though. Therefore, three containers
have to be started for the above instances, respectively. By contrast, in the
high throughput use case providing a video caching service, a user watches
his/her video cached at the OS-MEC server, which only creates a single
instance and runs a single container.

Figure 7: Test network.
Figure 8: Instantiation time-duration for computation-intensive and high-
throughput services.
Figure 9: Computing time of three computation-intensive services.

Fig. 8 shows a histogram for 1000 experiments for characterizing the resultant
instantiation time-duration both in the proposed Kubernetes method and in the
traditional script running method used in [41], where we simultaneously
support three computation-intensive services to test the instantiation
performance. For both applications, the average instantiation duration of
Kubernetes is always lower than that of the traditional script, because the
Kubernetes method can run multiple containers in parallel, while the
traditional method only runs them separately through scripts. Moreover, the
intensive computation APP takes a little longer to instantiate, because it
starts three containers at the same time, while the high throughput APP only
runs a single container. However, when using our Kubernetes method, their
difference in time duration is small. This allows the operators to easily
incorporate new NFs or services as and when needed without degrading the user
experience.

To provide further insights, Fig. 9 portrays the comparison of the computing
time of three computation-intensive services by a histogram for 1000
experiments. Since each picture has to be transmitted from mobile phones to
the OS-MEC server and processed by the file transfer protocol (FTP), the
average computing time of face recognition is higher than that of the other
two services.

Figure 10: (a) CPU usage for intensive computation; (b) memory usage for
intensive computation.

Fig. 10 analyzes the CPU and memory usage by calculating the prime sum and
face recognition in turn. Since much more data have to be processed during
face recognition, the CPU load of face recognition is about four times higher
than that of prime sum computations. The face recognition also has to store
more data than the prime sum computation, so it requires more memory in Fig.
10(b) (83.9MB). Then, whenever the network completes data processing, the CPU
is freed up immediately and the computing resources are released. By contrast,
the memory will not be freed up unless we handle it manually. For example, as
seen in Fig. 10(a), the CPU is released immediately once the prime sum service
is completed at 9:20:28. However, in Fig. 10(b), the memory remained reserved,
until we released it manually at 9:20:30. Therefore, the limited edge
computing and caching resources of OS-MEC are used flexibly and efficiently.

Figure 11: Transmission and computing time for high throughput.

Fig. 11 quantifies the transmission and computing time of the high-throughput
service, in which different-length videos are cached at the edge or cloud.
Upon increasing the video-length, we can see that the gap of the transmission
time between the cloud and the OS-MEC scheme gradually increases, which shows
that the advantage of our proposed scheme is gradually enhanced. Since the
computation loads of the high-throughput service are limited, the computing
time of both the cloud and of the edge is only about 0.14s on average, and
their difference is small. Furthermore, the increase of video size has little
effect on the computing time, which is basically in a stable state.

##  V Technical challenges

As mentioned above, the rapidly evolving scenarios and requirements of the 6G
network provides a compelling opportunity for introducing the OS cellular
network concept into MEC, which allows the operators to adaptively customize
the users’ networks at the edge based on the proposed OS-MEC philosophy. Our
solutions have the potential of influencing the standardization of the MEC
architecture for next-generation networks. However, there are still some open
technical challenges in the research of 6G-oriented OS cellular networks
having a MEC functionality, which need further research and global discussion.

###  V-A Cloud-network-edge-terminal collaborative OS cellular network

In recent years, substantial progress has been made in the research of OS
networks. For example, due to the proposal of the 5GC SBA, the OS CN
architecture has been studied quite widely; the proposed OS-MEC scheme
provides a complete overview of the edge-based OS architecture and its
implementation. Nevertheless, the OS RAN architecture has not been clearly
defined due to its complex air interfaces, heterogeneous cell structure and
incompatible network protocols. Moreover, an integrated OS cellular network
paradigm involving collaborations between the cloud, the network, the edge and
the terminal is inevitable in the 6G network of the near future. Whether the
existing OS network components can be integrated and work together still needs
further testing.

###  V-B Effective integration with 6G scenarios

The 6G system will further improve the existing 5G networking scenarios [12,
13], with the objective of further improving ultra-reliable low latency
communications (URLLC). It will also embrace new paradigm shifts, e.g. space-
air-ground integrated networks [42], terahertz (THz) communication [43], etc.
This requires that the future 6G OS network must be sufficiently flexible and
intelligent for seamlessly supporting new scenarios and components, or for
upgrading the existing functions without causing global disruption to the
architecture. However, the proposed OS-MEC had to be developed and operated on
the existing general-purpose hardware and open-source software platforms.
Therefore, its integration with the future 6G application scenarios and
paradigms needs further research.

###  V-C Open-source market fragmentation

The research of OS network platforms has attracted the attention of numerous
organizations and institutions. Numerous OS network platforms have been
developed, such as O-RAN, ONAP, OPNFV and the proposed OS-MEC, etc. However,
the availability of a bewildering gamut of platforms can lead to the
fragmentation of the open-source market, hence resulting in interoperability
problems. In order to circumvent these problems, on the one hand, a unified
open-source standard should be established for supporting openness and
interoperability. On the other hand, it requires closer cooperation among
open-source organizations to coordinate all the OS network platforms in the
near future.

###  V-D Cybersecurity and intellectual property protection

The 6G-oriented OS cellular network architecture is expected to integrate all
operating networks and provide users with customized capabilities including
mobile services, critical industrial infrastructure, vertical sectors and
other new network services. However, these new network features and the wide
application of 6G are fraught with potential security and privacy challenges.
In addition, the OS architecture means that based on general-purpose hardware,
any potential user is able to study, update and release new software versions
to other users in a collaborative open-access manner. This however highlights
the virtualization security issues, and poses a challenge in terms of the
definition and protection of intellectual property. Therefore, how to
effectively protect cybersecurity and intellectual property is still a huge
challenge.

###  V-E Engaging Telecom Vendors

Although more and more researchers and practitioners rely on open-source
components to build customized cellular networks supporting MEC functionality,
telecom vendors are not overly motivated to follow this approach. Since this
open-source mode makes the specialized hardware associated with particular
operating systems or functions in the original traditional network obsolete,
vendors can no longer make profits by developing different specialized
hardware. Therefore, how to persuade telecom vendors to join this open-source
movement from an economic perspective and to further expand the target group
supporting this movement is still a challenge.

##  VI Conclusions and Future Research

In this paper, we have conceived the OS-MEC scheme for 6G relying on
sophisticated decoupling and reconfiguration principles. First of all, an OS-
MEC framework was presented based on ETSI’s MEC architecture. Then, by means
of the SBA and NFV technology, the tightly coupled service functions were
decomposed into independent NFs. Next, by introducing the template and
instantiation based concepts into MANO, the disaggregated NFs may be re-
assembled and the required resources may be allocated in order to provide
customized services for users. In this context, the evolved NFs were
illustrated in the service-based MEC layer. The key components and the
associated implementation processes were described in the NF’s MANO. Then, a
pair of typical use cases have been critically appraised through
demonstrations in a small-scale test network. Finally, we have discussed some
potential technical challenges of 6G-oriented OS cellular networks.

In our future work, we will aim for developing the OS RAN architecture in
order to construct a complete OS cellular network for 6G. Based on the twinned
core principles of the OS RAN architecture: RAN decoupling and RAN
reconfiguration, we envisage to propose a new three-dimensional RAN slicing
mechanism, which realizes the decoupling and re-encapsulation of resources and
NFs from the three dimensions of resource virtualization, function
virtualization and network slicing, so as to provide customized virtual RANs
for users. We hope to fill the gaps in the research of OS RAN architecture and
lay the foundations for the ultimate realization of cloud-network-edge-
terminal collaborative OS cellular networks.

## List of Abbreviations

3GPP |  3rd Generation Partnership Project
---|---
4G |  Fourth Generation
---|---
5G |  Fifth Generation
---|---
5GC |  5G Core Network
---|---
6G |  Sixth Generation
---|---
AP |  Access Point
---|---
API |  Application Program Interface
---|---
APP |  Application
---|---
ASF |  Application Selection Function
---|---
CBS |  Control Base Station
---|---
CDUS |  Central Unit and Distributed Unit Splitting
---|---
CN |  Core Network
---|---
COTS |  Commercial Off-The-Shelf
---|---
CP |  Control Plane
---|---
CPCF |  Communication Protocol Conversion Function
---|---
CPU |  Central Processing Unit
---|---
CUPS |  Control Plane and User Plane Splitting
---|---
DBS |  Data Base Station
---|---
DL |  Downlink
---|---
DUDe |  Downlink and Uplink Decoupling
---|---
EBI |  East-Bound Interface
---|---
eNB |  evolved Node Base Station
---|---
EPC |  Evolved Packet Core
---|---
ETSI |  European Telecommunications Standards Institute
---|---
FTP |  File Transfer Protocol
---|---
gNB |  Next-generation Node Base Station
---|---
HDD |  Hard Disk Drive
---|---
HSDe |  Hardware/Software Decoupling
---|---
HSS |  Home Subscriber Server
---|---
HTTP |  Hypertext Transfer Protocol
---|---
LF |  Linux Foundation
---|---
MANO |  Management and Orchestration
---|---
MBS |  Macro Base Station
---|---
MEC |  Multi-access Edge Computing
---|---
MME |  Mobility Management Entity
---|---
NBI |  North-Bound Interface
---|---
NDN-ECC |  Named Data Networking-based Edge Cloud Computing
---|---
NF |  Network Function
---|---
NFV |  Network Function Virtualization
---|---
NR |  New Radio
---|---
NRF |  NF Repository Function
---|---
OAI |  Open-Air-Interface
---|---
ONAP |  Open Network Automation Platform
---|---
OS-MEC |  Open-source MEC
---|---
OPNFV |  Open Platform of NFV
---|---
OS |  Open-Source
---|---
PGW |  Packet Data Network Gateway
---|---
RAM |  Random Access Memory
---|---
RAN |  Radio Access Networks
---|---
RESTful |  Representational State Transfer-ful
---|---
SBA |  Service-Based Architecture
---|---
SBI |  Service-Based Interface
---|---
SBS |  Small Base Station
---|---
SDEC |  Software-Defined Edge Computing
---|---
SDN |  Software-Defined Networking
---|---
SGW |  Serving Gateway
---|---
SRF |  Service Registry Function
---|---
SSD |  Solid State Drive
---|---
THz |  Terahertz
---|---
TN |  Transport Network
---|---
UDM |  Unified Data Management
---|---
UHD |  Ultra High Definition
---|---
UL |  Uplink
---|---
UP |  User Plane
---|---
UPF |  User Plane Function
---|---
URLLC |  Ultra Reliable Low Latency Communications
---|---
vEPC |  virtualized EPC
---|---
VIM |  Virtualized Infrastructure Manager
---|---
VM |  Virtual Machine
---|---
vNF |  vitual NF
---|---

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|  Liqiang Zhao (Member, IEEE) received the B.Sc. degree in Electrical
Engineering from Shanghai Jiaotong University, China, in 1992, the M.Sc.
degree in Communications and Information Systems and the Ph.D. degree in
Information and Communications Engineering from Xidian University, China, in
2000 and 2003, respectively. In 2008, he was awarded by the Program for New
Century Excellent Talents in University, Ministry of Education, China. From
1992 to 2005, he was a Research Engineer with the $20^{th}$ Research
Institute, Chinese Electronics Technology Group Corporation (CETC), China. His
research focused on mobile communication systems and spread spectrum
communications. From 2005 to 2007, he was an Associate Professor with State
Key Laboratory of Integrated Service Networks (ISN), Xidian University, China.
His research focused on WiMAX, WLAN, and wireless sensor network. He was
appointed as a Marie Curie Research Fellow at the Centre for Wireless Network
Design (CWiND), University of Bedfordshire in June 2007 to conduct research in
the GAWIND project funded under EU FP6 HRM programme. His activities focused
on the area of automatic wireless broadband access network planning and
optimization. Since June 2008, he has returned Xidian University, first as an
Associate Professor and later as a Professor. His current research focuses on
broadband wireless communications and space communications, etc. He has more
than 100 published in authorized academic periodicals both in and abroad and
in international science conferences, wherein 30 of which are retrieved in
SCI, and more than 70 of them are EI indexed, and 6 national invention
patents. He has hosted / participated many national research projects, such as
the National Natural Science Foundation, the 863 Program and the National
Science Technology Major Projects, and several international research projects
including the EU FP6, FP7 plans for international cooperation exchange
projects, and some research projects from companies, e.g., Huawei.
---|---
|  Guorong Zhou is currently pursuing the Ph.D. degree from the school of
Telecommunication Engineering, Xidian University, China. Since April 2021, she
has been a visiting Ph.D. student at the School of Electronics and Computer
Science, University of Southampton, Southampton, U.K., on the funds from the
Program of the China Scholarships Council. Her research interests include
wireless communications technologies, network slicing, multi-access edge
computing and machine learning for communications.
---|---
|  Gan Zheng (Fellow, IEEE) received the BEng and the MEng from Tianjin
University, Tianjin, China, in 2002 and 2004, respectively, both in Electronic
and Information Engineering, and the PhD degree in Electrical and Electronic
Engineering from The University of Hong Kong in 2008. He is currently Reader
of Signal Processing for Wireless Communications in the Wolfson School of
Mechanical, Electrical and Manufacturing Engineering, Loughborough University,
UK. His research interests include machine learning for communications, UAV
communications, mobile edge caching, full-duplex radio, and wireless power
transfer. He is the first recipient for the 2013 IEEE Signal Processing
Letters Best Paper Award, and he also received 2015 GLOBECOM Best Paper Award,
and 2018 IEEE Technical Committee on Green Communications & Computing Best
Paper Award. He was listed as a Highly Cited Researcher by Thomson
Reuters/Clarivate Analytics in 2019 and he is an IEEE Fellow. He currently
serves as an Associate Editor for IEEE Communications Letters and IEEE
Wireless Communications Letters.
---|---
|  Chih-Lin I (Fellow, IEEE) is CMCC Chief Scientist of Wireless Technologies.
She received Ph.D. EE from Stanford University. She has won 2005 IEEE ComSoc
Stephen Rice Prize, 2018 IEEE ComSoc Fred W. Ellersick Prize, the 7th IEEE
Asia-Pacific Outstanding Paper Award, and 2015 IEEE Industrial Innovation
Award for Leadership and Innovation in Next-Generation Cellular Wireless
Networks. She is the Chair of O-RAN Technical Steering Committee and an O-RAN
Executive Committee Member, the Chair of FuTURE 5G/6G SIG, the Chair of WAIA
(Wireless AI Alliance) Executive Committee, an Executive Board Member of
GreenTouch, a Network Operator Council Founding Member of ETSI NFV, a Steering
Board Member and Vice Chair of WWRF, a Steering Committee member and the
Publication Chair of IEEE 5G and Future Networks Initiatives, the Founding
Chair of IEEE WCNC Steering Committee, the Director of IEEE ComSoc Meetings
and Conferences Board, a Senior Editor of IEEE Trans. Green Comm. &
Networking, a member of IEEE ComSoc SDB, SPC, and CSCN-SC, and a Scientific
Advisory Board Member of Singapore NRF. She has published over 200 papers in
scientific journals, book chapters and conferences and holds over 100 patents.
She is co-author of the book “Green and Software-defined Wireless Networks –
From Theory to Practice” and has also Co-edited two books: “Ultra-dense
Networks – Principles and Applications” and “5G Networks – Fundamental
Requirements, Enabling Technologies, and Operations Management”. She is a
Fellow of IEEE and a Fellow of WWRF. Her current research interests center
around ICDT Deep Convergence: “From Green & Soft to Open & Smart”.
---|---
|  Xiaohu You (Fellow, IEEE) received the B.S., M.S. and Ph.D. degrees in
electrical engineering from Nanjing Institute of Technology, Nanjing, China,
in 1982, 1985, and 1989, respectively. From 1987 to 1989, he was with Nanjing
Institute of Technology as a Lecturer. From 1990 to the present time, he has
been with Southeast University, first as an Associate Professor and later as a
Professor. His research interests include mobile communications, adaptive
signal processing, and artificial neural networks with applications to
communications and biomedical engineering. He is the Chief of the Technical
Group of China 3G/B3G Mobile Communication R & D Project. He received the
excellent paper prize from the China Institute of Communications in 1987 and
the Elite Outstanding Young Teacher Awards from Southeast University in 1990,
1991, and 1993. He was also a recipient of the 1989 Young Teacher Award of Fok
Ying Tung Education Foundation, State Education Commission of China.
---|---
|  Lajos Hanzo (Fellow, IEEE) received the master’s and Ph.D. degrees from the
Technical University (TU) of Budapest, in 1976 and 1983, respectively, and the
D.Sc. degree from the University of Southampton, in 2004. He has published
more than 1900 contributions at IEEE Xplore and 19 Wiley-IEEE Press books, and
has helped the fast-track career of 123 Ph.D. students. Over 40 of them are
professors at various stages of their careers in academia and many of them are
leading scientists in the wireless industry. He is a Fellow of the Royal
Academy of Engineering, IET, and EURASIP. He is a Foreign Member of the
Hungarian Academy of Sciences and the former Editor-in-Chief of the IEEE
Press. He has served several terms as the Governor of both IEEE ComSoc and
VTS. He was awarded the Honorary Doctorates by the TU of Budapest, in 2009,
and by the University of Edinburgh, in 2015.
---|---


2011, Translated from the 2007 French original.

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