to data-driven methods. Some of them are just suitable for specific anomaly
detection methods, such as reconstruction based methods [281]. They can just
apply to the anomaly detecting methods based features reconstruction. This
root cause finding methods work by compute the distances between every pair of
reconstructed feature and original feature. The greater the distance is, the
more the feature contributes to the anomaly. More generally, causal inference
based methods can apply to more kinds of anomaly detection methods, though
they are more computationally expensive. CloudRanger [245] use conditional
dependence to establish the topology of cause and effect relationship among
tasks and use the topology to find root cause. Sipple [290] uses integrated
gradient method to compute the contribution of each feature and find the root
cause.

###  VII-C Failure Recovery

One of the main advantages of cloud-native solutions lies in the possibility
of automatically detecting and overcoming failures. Failure recovery is made
possible by multiple technologies leveraged in building a cloud-native
application. First, containerization technologies like Docker introduce almost
zero overhead when launching a container, making it possible to re-spawn a
failed container within seconds possible. Second, modern metric collection and
processing systems like Prometheus[291] allow high integration into cloud-
native systems, further enabling well-informed decisions. Third, with fast
development of machine learning techniques, automatically-made decisions are
getting yet more efficient and effective. In this section, we will study three
main topics to recover from an error: redundancy, failover, and repair.

####  VII-C1 Redundancy

Redundancy refers to deploying multiple replicas of one resource
(microservice, computation, storage, etc.), preferably in multiple locations,
in order to minimize disruption even if one or several of them goes down. This
method has already been in use before the container-based cloud-native era
[292]. In addition to improved reliability, distributing data from multiple
locations can lead to reduced latency to end users. Kang et al. [293] design
and implement a custom controller in Kubernetes to select and use multiple
replicas of Virtual Network Functions (VNFs) so high processing ability can be
achieved. However, making $N$ redundant copies means $N$-time of resource
consumption, which could be costly. Uluyol et al. [294] propose a novel
encoding mechanism to save encode data before saving to multiple locations,
instead of simply creating replicas. Furthermore, the authors mitigated
increase in latency introduced by distributed storage by rethinking how
consensus can be reached, to offer near-optimal latency versus cost trade-
offs.

####  VII-C2 Failover

Failover is the process of switching to a backup server or other types of
resource when disruption is detected. This process is based on redundant
deployment talked in the previous section. By having proper algorithms
configured, the cloud orchestrator is able to make efficient use of replicas
to switch to other healthy replicas. There are multiple optimization targets
in the context of failover. Aldwyan et al. [295] identify that failover
between distributed data centers can lead to degraded performance due to added
network latencies, and propose a latency-aware failover strategy leveraging
genetic algorithms to take latancies into consideration when making a failover
decision. Jin et al. [296] build a SDN failover mechanism, FAVE, that is aware
of physical link failure, to be used in virtualized SDN environments. Landa et
al. [297] utilize TCP re-transmission metrics to declare network failure in
CDN networks, and quick re-route traffic through redundant links to keep high
availability.

####  VII-C3 Repair

Repair tries to fix the error instead of redirect traffic to other service
instances. Considering the nature of today’s container-based cloud-native
solutions, repairing a failed service is likely to be more efficient compared
to failover into backup. Giannakopoulos et al. [298] consider the complexity
in modern cloud deployments and identify that such complexity could lead to
failure in deployments. They build AURA, which transform a deployment into a
directed acyclic graph, so whenever an error happens, it is possible to
respawn only a small portion of the entire deployment, thus keeping the repair
process efficient.

###  VII-D Challenges and Research Opportunities

The commonality and special individuality of data distribution. In data-driven
models training, either for future state prediction or for anomaly detection,
there is a main concern that whether to train only one model for all the
servers or train single model for each server. On the one hand, it is cost to
train a model for each server. On the other hand, it predicts inaccurately
when training only a model for all the servers, as every server has its own
data distribution. Thus, there is a trade off between efficiency and accuracy.

The dynamic evolution of cloud environment. The cloud environment is dynamic.
New missions arrives and old missions ends at every moment. The models
generally becomes outdated and need retraining frequently. Designing a light-
weight retraining methods can bring huge benefit.

##  VIII Open Issues and Future Directions

Cloud-native computing has been gaining a lot of attention in recent years due
to its ability to enable agile, scalable and resilient software systems.
However, there are still some open issues and future directions that need to
be addressed. In the following, we list the open issues and possible research
directions.

  * •

Hybrid multi-cloud integration. As the popularity of cloud-native computing
continues to grow, many organizations are using multiple cloud providers or
leveraging both public and private clouds. Besides, the services are
applicaitons are deployed across the continuum of cloud-edge-device. It is
important to develop better tools and techniques with interoperability
capabilities for integrating these environments, including standardization of
APIs and data exchange while retaining control over sensitive private data
across multiple clouds.

  * •

User-friendly servie shapes (forms). As cloud-native being the foundation of
today’s most web applicaitons, more user-friendly service shapes to erase the
heavy burden of application deployment for hybrid edge-clouds are urgently
needed. Serverless computing is a good practice. It allows developers to focus
on their business logic without worrying about the infrastructure management.
However, serverless computing is criticized by its long cold time, inefficient
state management and other related issues. Better service shapes/forms for
more wider application scenarios are required.

  * •

Advanced automation and resource utilization. Automation is crucial to
realizing the full benefits of cloud-native architectures. However, there is
still a lot of room for improvement in terms of automating deployment,
scaling, and maintenance activities, especially for the distributed training
of the heavy big models. Another key benefit of cloud-native computing is the
ability to dynamically allocate resources based on demand. However, this can
also lead to inefficiencies if not properly managed. There is a need for
improved tools and algorithms that can optimize resource allocation and reduce
waste.

  * •

Enhanced cross-platform observability and security. Cloud-native architectures
tend to be highly distributed and dynamic, which can make it difficult to
observe and troubleshoot issues. There is a need for better observability and
monitoring tools to help developers and operations teams quickly identify and
resolve problems. Security related concerns are becoming more critical,
especially for hybrid multi-cloud scenarios. There is a string need for better
security mechanisms that can effectively protect against cyber threats,
especially as attacks become more sophisticated.

In summary, while cloud-native computing has come a long way, there are still
many open issues and future directions that need to be addressed to fully
realize its potential. By continuing to innovate and address these challenges,
we can create more efficient, secure, and scalable software systems for the
future.

##  IX Conclusions

Cloud-native, as the most influential principle for web applications, has
attracted more and more researchers and companies to get involved in studying
and using it. This survey attempts to provide possible research opportunities
through a succinct and effective classification. We present the research
roadmap of cloud-native from the perspective of services computing.
Specifically, we divide the development of cloud-native applications into four
states, building, orchestration, operate, and maintenance. State-of-the-art
research works and industrial applications are provided. We attempted to
provide some enlightening thoughts on the research of cloud-native computing
and services computing. We hope that this article can stimulate fruitful
discussions on potential future research directions on this topic.

## Acknowledgment

This work was supported in part by the National Key Research and Development
Program of China under Grant 2022YFB4500100, the National Science Foundation
of China under Grants 62125206 and U20A20173, and the Key Research Project of
Zhejiang Province under Grant 2022C01145.

## References

  * [1] M. P. Papazoglou, “Service-oriented computing: Concepts, characteristics and directions,” in _Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003._ IEEE, 2003, pp. 3–12.
  * [2] M. P. Papazoglou, P. Traverso, S. Dustdar, and F. Leymann, “Service-oriented computing: a research roadmap,” _International Journal of Cooperative Information Systems_ , vol. 17, no. 02, pp. 223–255, 2008.
  * [3] N. Alshuqayran, N. Ali, and R. Evans, “A systematic mapping study in microservice architecture,” in _2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA)_. IEEE, 2016, pp. 44–51.
  * [4] L. Leite, C. Rocha, F. Kon, D. Milojicic, and P. Meirelles, “A survey of devops concepts and challenges,” _ACM Computing Surveys (CSUR)_ , vol. 52, no. 6, pp. 1–35, 2019.
  * [5] F. Zampetti, S. Geremia, G. Bavota, and M. Di Penta, “CI/CD pipelines evolution and restructuring: A qualitative and quantitative study,” in _2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)_. IEEE, 2021, pp. 471–482.
  * [6] S. Barlev, Z. Basil, S. Kohanim, R. Peleg, S. Regev, and A. Shulman-Peleg, “Secure yet usable: Protecting servers and linux containers,” _IBM Journal of Research and Development_ , vol. 60, no. 4, pp. 12–1, 2016.
  * [7] M. Mattetti, A. Shulman-Peleg, Y. Allouche, A. Corradi, S. Dolev, and L. Foschini, “Securing the infrastructure and the workloads of linux containers,” in _2015 IEEE Conference on Communications and Network Security (CNS)_. IEEE, 2015, pp. 559–567.
  * [8] “Docker: Modernize your applications, accelerate innovation,” [n.d.]. [Online]. Available: https://www.docker.com/
  * [9] “Kubernetes: Production-grade container orchestration.” [Online]. Available: https://kubernetes.io/
  * [10] A. Verma, L. Pedrosa, M. Korupolu, D. Oppenheimer, E. Tune, and J. Wilkes, “Large-scale cluster management at google with borg,” in _Proceedings of the Tenth European Conference on Computer Systems_ , 2015, pp. 1–17.
  * [11] N. Kratzke and P.-C. Quint, “Understanding cloud-native applications after 10 years of cloud computing-a systematic mapping study,” _Journal of Systems and Software_ , vol. 126, pp. 1–16, 2017.
  * [12] N. Kratzke, “A brief history of cloud application architectures,” _Applied Sciences_ , vol. 8, no. 8, p. 1368, 2018.
  * [13] G. Gil, D. Corujo, and P. Pedreiras, “Cloud native computing for industry 4.0: Challenges and opportunities,” in _2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)_. IEEE, 2021, pp. 01–04.
  * [14] Q. Duan, “Intelligent and autonomous management in cloud-native future networks—a survey on related standards from an architectural perspective,” _Future Internet_ , vol. 13, no. 2, p. 42, 2021.
  * [15] A. Senthuran and S. Hettiarachchi, “A review of dynamic scalability and dynamic scheduling in cloud-native distributed stream processing systems,” _ICDSMLA 2019_ , pp. 1539–1553, 2020.
  * [16] C. Carrión, “Kubernetes scheduling: Taxonomy, ongoing issues and challenges,” _ACM Computing Surveys (CSUR)_ , 2022.
  * [17] B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, S. Shenker, and I. Stoica, “Mesos: A platform for Fine-Grained resource sharing in the data center,” in _8th USENIX Symposium on Networked Systems Design and Implementation (NSDI 11)_. Boston, MA: USENIX Association, Mar. 2011. [Online]. Available: https://www.usenix.org/conference/nsdi11/mesos-platform-fine-grained-resource-sharing-data-center
  * [18] N. Naik, “Building a virtual system of systems using docker swarm in multiple clouds,” in _2016 IEEE International Symposium on Systems Engineering (ISSE)_. IEEE, 2016, pp. 1–3.
  * [19] V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth _et al._ , “Apache hadoop yarn: Yet another resource negotiator,” in _Proceedings of the 4th annual Symposium on Cloud Computing_ , 2013, pp. 1–16.
  * [20] W. Li, Y. Lemieux, J. Gao, Z. Zhao, and Y. Han, “Service mesh: Challenges, state of the art, and future research opportunities,” in _2019 IEEE International Conference on Service-Oriented System Engineering (SOSE)_. IEEE, 2019, pp. 122–1225.
  * [21] F. Soppelsa and C. Kaewkasi, _Native docker clustering with swarm_. Packt Publishing Ltd, 2016.
  * [22] F. Liu, G. Tang, Y. Li, Z. Cai, X. Zhang, and T. Zhou, “A survey on edge computing systems and tools,” _Proceedings of the IEEE_ , vol. 107, no. 8, pp. 1537–1562, 2019.
  * [23] W. Xu, “Test report on kubeedge’s support for 100,000 edge nodes,” https://kubeedge.io/en/blog/scalability-test-report/, 2022.
  * [24] “Kernel-based virtual machine.” [Online]. Available: https://www.redhat.com/en/topics/virtualization/what-is-KVM
  * [25] R. Morabito, J. Kjällman, and M. Komu, “Hypervisors vs. lightweight virtualization: a performance comparison,” in _2015 IEEE International Conference on cloud engineering_. IEEE, 2015, pp. 386–393.
  * [26] “Docker: Modernize your applications, accelerate innovation,” [n.d.]. [Online]. Available: https://www.docker.com/
  * [27] K. Kushwaha and N. Center, “How container runtimes matter in kubernetes?” 2017\.
  * [28] D. B. Rawat and S. R. Reddy, “Software defined networking architecture, security and energy efficiency: A survey,” _IEEE Communications Surveys & Tutorials_, vol. 19, no. 1, pp. 325–346, 2016.
  * [29] J. Deng, H. Hu, H. Li, Z. Pan, K.-C. Wang, G.-J. Ahn, J. Bi, and Y. Park, “Vnguard: An nfv/sdn combination framework for provisioning and managing virtual firewalls,” in _2015 IEEE Conference on Network Function Virtualization and Software Defined Network (NFV-SDN)_. IEEE, 2015, pp. 107–114.
  * [30] M. A. Harrabi, M. Jeridi, N. Amri, M. R. Jerbi, A. Jhine, and H. Khamassi, “Implementing nfv routers and sdn controllers in mpls architecture,” in _2015 World Congress on Information Technology and Computer Applications (WCITCA)_. IEEE, 2015, pp. 1–6.
  * [31] M. Mahalingam, D. Dutt, K. Duda, P. Agarwal, L. Kreeger, T. Sridhar, and C. W. Mike Bursell, “Virtual extensible local area network (vxlan): A framework for overlaying virtualized layer 2 networks over layer 3 networks,” https://datatracker.ietf.org/doc/rfc7348/, 2020.
  * [32] Y. Park, H. Yang, and Y. Kim, “Performance analysis of cni (container networking interface) based container network,” in _2018 International Conference on Information and Communication Technology Convergence (ICTC)_ , 2018, pp. 248–250.
  * [33] S. Qi, S. G. Kulkarni, and K. K. Ramakrishnan, “Assessing container network interface plugins: Functionality, performance, and scalability,” _IEEE Transactions on Network and Service Management_ , vol. 18, no. 1, pp. 656–671, 2021.
  * [34] R. Kumar and M. C. Trivedi, “Networking analysis and performance comparison of kubernetes cni plugins,” in _Advances in Computer, Communication and Computational Sciences_. Springer, 2021, pp. 99–109.
  * [35] L. Leite, C. Rocha, F. Kon, D. Milojicic, and P. Meirelles, “A survey of devops concepts and challenges,” _ACM Comput. Surv._ , vol. 52, no. 6, nov 2019. [Online]. Available: https://doi.org/10.1145/3359981
  * [36] T. K. Community, “Kubesphere devops: A powerful ci/cd platform built on top of kubernetes for devops-oriented teams,” https://kubesphere.io/devops/, 2022.
  * [37] H. Chen, S. Deng, H. Zhu, H. Zhao, R. Jiang, S. Dustdar, and A. Y. Zomaya, “Mobility-Aware Offloading and Resource Allocation for Distributed Services Collaboration,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 33, no. 10, pp. 2428–2443, 2022.
  * [38] C. Zheng, Q. Zhuang, and F. Guo, “A Multi-Tenant Framework for Cloud Container Services,” in _2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)_. IEEE, 2021, pp. 359–369.
  * [39] Ł. Wojciechowski, K. Opasiak, J. Latusek, M. Wereski, V. Morales, T. Kim, and M. Hong, “NetMARKS: Network metrics-AwaRe kubernetes scheduler powered by service mesh,” in _IEEE INFOCOM 2021-IEEE Conference on Computer Communications_. IEEE, 2021, pp. 1–9.
  * [40] Y. Fu, S. Zhang, J. Terrero, Y. Mao, G. Liu, S. Li, and D. Tao, “Progress-based container scheduling for short-lived applications in a kubernetes cluster,” in _2019 IEEE International Conference on Big Data (Big Data)_. IEEE, 2019, pp. 278–287.
  * [41] S. Deng, H. Zhao, Z. Xiang, C. Zhang, R. Jiang, Y. Li, J. Yin, S. Dustdar, and A. Y. Zomaya, “Dependent Function Embedding for Distributed Serverless Edge Computing,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 33, no. 10, pp. 2346–2357, 2021.
  * [42] M. C. Ogbuachi, C. Gore, A. Reale, P. Suskovics, and B. Kovács, “Context-aware K8S scheduler for real time distributed 5G edge computing applications,” in _2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)_. IEEE, 2019, pp. 1–6.
  * [43] G. Zhang, R. Lu, and W. Wu, “Multi-resource fair allocation for cloud federation,” in _2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)_. IEEE, 2019, pp. 2189–2194.
  * [44] G. Yeung, D. Borowiec, R. Yang, A. Friday, R. Harper, and P. Garraghan, “Horus: Interference-aware and prediction-based scheduling in deep learning systems,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 33, no. 1, pp. 88–100, 2021.
  * [45] Y. Han, S. Shen, X. Wang, S. Wang, and V. C. M. Leung, “Tailored learning-based scheduling for kubernetes-oriented edge-cloud system,” in _IEEE INFOCOM 2021-IEEE Conference on Computer Communications_. IEEE, 2021, pp. 1–10.
  * [46] T. Pusztai, A. Morichetta, V. C. Pujol, S. Dustdar, S. Nastic, X. Ding, D. Vij, and Y. Xiong, “Slo script: A novel language for implementing complex cloud-native elasticity-driven slos,” in _2021 IEEE International Conference on Web Services (ICWS)_. IEEE, 2021, pp. 21–31.
  * [47] ——, “A novel middleware for efficiently implementing complex cloud-native slos,” in _2021 IEEE 14th International Conference on Cloud Computing (CLOUD)_ , 2021, pp. 410–420.
  * [48] M. Imdoukh, I. Ahmad, and M. G. Alfailakawi, “Machine learning-based auto-scaling for containerized applications,” _Neural Computing and Applications_ , vol. 32, no. 13, pp. 9745–9760, 2020.
  * [49] R. Pinciroli, A. Ali, F. Yan, and E. Smirni, “Cedule+: Resource management for burstable cloud instances using predictive analytics,” _IEEE Transactions on Network and Service Management_ , vol. 18, no. 1, pp. 945–957, 2020.
  * [50] S. N. A. Jawaddi, M. H. Johari, and A. Ismail, “A review of microservices autoscaling with formal verification perspective,” _Software: Practice and Experience_ , 2022.
  * [51] Z. Wang, X. Tang, Q. Liu, and J. Han, “Jily: Cost-aware AutoScaling of heterogeneous GPU for DNN inference in public cloud,” in _2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC)_. IEEE, 2019, pp. 1–8.
  * [52] J. P. K. S. Nunes, T. Bianchi, A. Y. Iwasaki, and E. Y. Nakagawa, “State of the art on microservices autoscaling: An overview,” _Anais do XLVIII Seminário Integrado de Software e Hardware_ , pp. 30–38, 2021.
  * [53] T.-T. Nguyen, Y.-J. Yeom, T. Kim, D.-H. Park, and S. Kim, “Horizontal pod autoscaling in Kubernetes for elastic container orchestration,” _Sensors_ , vol. 20, no. 16, p. 4621, 2020.
  * [54] J. Liu, S. Zhang, Q. Wang, and J. Wei, “Coordinating Fast Concurrency Adapting with Autoscaling for SLO-Oriented Web Applications,” _IEEE Transactions on Parallel and Distributed Systems_ , 2022.
  * [55] F. Rossi, M. Nardelli, and V. Cardellini, “Horizontal and vertical scaling of container-based applications using reinforcement learning,” in _2019 IEEE 12th International Conference on Cloud Computing (CLOUD)_. IEEE, 2019, pp. 329–338.
  * [56] F. Rossi, V. Cardellini, F. L. Presti, and M. Nardelli, “Geo-distributed efficient deployment of containers with Kubernetes,” _Computer Communications_ , vol. 159, pp. 161–174, 2020.
  * [57] F. Ebadifard, S. M. Babamir, and S. Barani, “A dynamic task scheduling algorithm improved by load balancing in cloud computing,” in _2020 6th International Conference on Web Research (ICWR)_. IEEE, 2020, pp. 177–183.
  * [58] A. De Santo, A. Galli, M. Gravina, V. Moscato, and G. Sperlì, “Deep learning for hdd health assessment: An application based on lstm,” _IEEE Transactions on Computers_ , vol. 71, no. 1, pp. 69–80, 2020.
  * [59] K. Nguyen, S. Drew, C. Huang, and J. Zhou, “Collaborative container-based parked vehicle edge computing framework for online task offloading,” in _2020 IEEE 9th International Conference on Cloud Networking (CloudNet)_. IEEE, 2020, pp. 1–6.
  * [60] Y. Bao, Y. Peng, and C. Wu, “Deep learning-based job placement in distributed machine learning clusters,” in _IEEE INFOCOM 2019-IEEE conference on computer communications_. IEEE, 2019, pp. 505–513.
  * [61] A. Beltre, P. Saha, and M. Govindaraju, “Kubesphere: An approach to multi-tenant fair scheduling for kubernetes clusters,” in _2019 IEEE Cloud Summit_. IEEE, 2019, pp. 14–20.
  * [62] M. F. Bestari, A. I. Kistijantoro, and A. B. Sasmita, “Dynamic Resource Scheduler for Distributed Deep Learning Training in Kubernetes,” in _2020 7th International Conference on Advance Informatics: Concepts, Theory and Applications (ICAICTA)_. IEEE, 2020, pp. 1–6.
  * [63] M. Carvalho and D. F. Macedo, “QoE-Aware Container Scheduler for Co-located Cloud Environments,” in _2021 IFIP/IEEE International Symposium on Integrated Network Management (IM)_. IEEE, 2021, pp. 286–294.
  * [64] L. Toka, “Ultra-reliable and low-latency computing in the edge with kubernetes,” _Journal of Grid Computing_ , vol. 19, no. 3, pp. 1–23, 2021\.
  * [65] A. Warke, M. Mohamed, R. Engel, H. Ludwig, W. Sawdon, and L. Liu, “Storage Service Orchestration with Container Elasticity,” in _2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)_. IEEE, 2018, pp. 283–292.
  * [66] D. Santoro, D. Zozin, D. Pizzolli, F. De Pellegrini, and S. Cretti, “Foggy: a platform for workload orchestration in a fog computing environment,” in _2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)_. IEEE, 2017, pp. 231–234.
  * [67] A. C. Caminero and R. Muñoz-Mansilla, “Quality of service provision in fog computing: Network-aware scheduling of containers,” _Sensors_ , vol. 21, no. 12, p. 3978, 2021.
  * [68] N. D. Nguyen, L.-A. Phan, D.-H. Park, S. Kim, and T. Kim, “Elasticfog: Elastic resource provisioning in container-based fog computing,” _IEEE Access_ , vol. 8, pp. 183 879–183 890, 2020.
  * [69] J. Santos, T. Wauters, B. Volckaert, and F. De Turck, “Towards network-aware resource provisioning in kubernetes for fog computing applications,” in _2019 IEEE Conference on Network Softwarization (NetSoft)_. IEEE, 2019, pp. 351–359.
  * [70] J. Santos, T. Wauters, B. Volckaert, and F. De Turck, “Resource provisioning in fog computing: From theory to practice,” _Sensors_ , vol. 19, no. 10, p. 2238, 2019.
  * [71] K. Kaur, S. Garg, G. Kaddoum, S. H. Ahmed, and M. Atiquzzaman, “KEIDS: Kubernetes-based energy and interference driven scheduler for industrial IoT in edge-cloud ecosystem,” _IEEE Internet of Things Journal_ , vol. 7, no. 5, pp. 4228–4237, 2019.
  * [72] Z. Zhong, J. He, M. A. Rodriguez, S. Erfani, R. Kotagiri, and R. Buyya, “Heterogeneous task co-location in containerized cloud computing environments,” in _2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC)_. IEEE, 2020, pp. 79–88.
  * [73] Y.-G. Yim, H.-J. Jang, and H.-W. Jin, “QoS for best-effort batch jobs in container-based cloud,” _Concurrency and Computation: Practice and Experience_ , 2021.
  * [74] P. Chhikara, R. Tekchandani, N. Kumar, and M. S. Obaidat, “An efficient container management scheme for resource-constrained intelligent IoT devices,” _IEEE Internet of Things Journal_ , vol. 8, no. 16, pp. 12 597–12 609, 2020.
  * [75] M. Xu and R. Buyya, “Brownout approach for adaptive management of resources and applications in cloud computing systems: A taxonomy and future directions,” _ACM Computing Surveys (CSUR)_ , vol. 52, no. 1, pp. 1–27, 2019.
  * [76] J. R. Gunasekaran, P. Thinakaran, N. C. Nachiappan, M. T. Kandemir, and C. R. Das, “Fifer: Tackling resource underutilization in the serverless era,” in _Proceedings of the 21st International Middleware Conference_ , 2020, pp. 280–295.
  * [77] P. Thinakaran, J. R. Gunasekaran, B. Sharma, M. T. Kandemir, and C. R. Das, “Kube-knots: Resource harvesting through dynamic container orchestration in gpu-based datacenters,” in _2019 IEEE International Conference on Cluster Computing (CLUSTER)_. IEEE, 2019, pp. 1–13.
  * [78] A. Chung, J. W. Park, and G. R. Ganger, “Stratus: Cost-aware container scheduling in the public cloud,” in _Proceedings of the ACM symposium on cloud computing_ , 2018, pp. 121–134.
  * [79] Z. Zhong and R. Buyya, “A cost-efficient container orchestration strategy in kubernetes-based cloud computing infrastructures with heterogeneous resources,” _ACM Transactions on Internet Technology (TOIT)_ , vol. 20, no. 2, pp. 1–24, 2020.
  * [80] Y. Xu, J. Yao, H.-A. Jacobsen, and H. Guan, “Cost-efficient negotiation over multiple resources with reinforcement learning,” in _2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)_. IEEE, 2017, pp. 1–6.
  * [81] M. Xu, A. N. Toosi, and R. Buyya, “A self-adaptive approach for managing applications and harnessing renewable energy for sustainable cloud computing,” _IEEE Transactions on Sustainable Computing_ , vol. 6, no. 4, pp. 544–558, 2020.
  * [82] I. Kim, K. J. Oh, and Y. I. Eom, “Overlit: New Storage Driver for Localization and Specialization,” in _2019 IEEE International Conference on Big Data and Smart Computing (BigComp)_. IEEE, 2019, pp. 1–4.
  * [83] N. Alshuqayran, N. Ali, and R. Evans, “A systematic mapping study in microservice architecture,” in _2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA)_. IEEE, 2016, pp. 44–51.
  * [84] K. Gos and W. Zabierowski, “The comparison of microservice and monolithic architecture,” in _2020 IEEE XVIth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH)_. IEEE, 2020, pp. 150–153.
  * [85] L. De Lauretis, “From monolithic architecture to microservices architecture,” in _2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)_. IEEE, 2019, pp. 93–96.
  * [86] F. Ponce, G. Márquez, and H. Astudillo, “Migrating from monolithic architecture to microservices: A rapid review,” in _2019 38th International Conference of the Chilean Computer Science Society (SCCC)_. IEEE, 2019, pp. 1–7.
  * [87] E. Jonas, J. Schleier-Smith, V. Sreekanti, C.-C. Tsai, A. Khandelwal, Q. Pu, V. Shankar, J. Carreira, K. Krauth, N. Yadwadkar _et al._ , “Cloud programming simplified: A berkeley view on serverless computing,” _arXiv preprint arXiv:1902.03383_ , 2019.
  * [88] C. Carrión, “Kubernetes scheduling: Taxonomy, ongoing issues and challenges,” _ACM Computing Surveys (CSUR)_ , 2022.
  * [89] N. Wang, R. Zhou, L. Jiao, R. Zhang, B. Li, and Z. Li, “Preemptive Scheduling for Distributed Machine Learning Jobs in Edge-Cloud Networks,” _IEEE Journal on Selected Areas in Communications_ , 2022.
  * [90] A. Xu, Z. Huo, and H. Huang, “On the acceleration of deep learning model parallelism with staleness,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2020, pp. 2088–2097.
  * [91] Y. Huang, Y. Cheng, A. Bapna, O. Firat, D. Chen, M. Chen, H. Lee, J. Ngiam, Q. V. Le, Y. Wu _et al._ , “Gpipe: Efficient training of giant neural networks using pipeline parallelism,” _Advances in neural information processing systems_ , vol. 32, 2019.
  * [92] H. Wang, Z. Liu, and H. Shen, “Job scheduling for large-scale machine learning clusters,” in _Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies_ , 2020, pp. 108–120.
  * [93] H. Albahar, S. Dongare, Y. Du, N. Zhao, A. K. Paul, and A. R. Butt, “SCHEDTUNE: A Heterogeneity-Aware GPU Scheduler for Deep Learning,” in _2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)_. IEEE, 2022, pp. 695–705.
  * [94] X. Wu, H. Xu, and Y. Wang, “Irina: Accelerating dnn inference with efficient online scheduling,” in _4th Asia-Pacific Workshop on Networking_ , 2020, pp. 36–43.
  * [95] H. Shen, L. Chen, Y. Jin, L. Zhao, B. Kong, M. Philipose, A. Krishnamurthy, and R. Sundaram, “Nexus: A gpu cluster engine for accelerating dnn-based video analysis,” in _Proceedings of the 27th ACM Symposium on Operating Systems Principles_ , 2019, pp. 322–337.
  * [96] N. Zhou, Y. Georgiou, M. Pospieszny, L. Zhong, H. Zhou, C. Niethammer, B. Pejak, O. Marko, and D. Hoppe, “Container orchestration on hpc systems through kubernetes,” _Journal of Cloud Computing_ , vol. 10, no. 1, pp. 1–14, 2021.
  * [97] C. Misale, M. Drocco, D. J. Milroy, C. E. A. Gutierrez, S. Herbein, D. H. Ahn, and Y. Park, “It’s a Scheduling Affair: GROMACS in the Cloud with the KubeFlux Scheduler,” in _2021 3rd International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC)_. IEEE, 2021, pp. 10–16.
  * [98] C. Misale, D. J. Milroy, C. E. A. Gutierrez, M. Drocco, S. Herbein, D. H. Ahn, Z. Kaiser, and Y. Park, “Towards standard Kubernetes scheduling interfaces for converged computing,” in _Smoky Mountains Computational Sciences and Engineering Conference_. Springer, 2021, pp. 310–326.
  * [99] A. M. Beltre, P. Saha, M. Govindaraju, A. Younge, and R. E. Grant, “Enabling hpc workloads on cloud infrastructure using kubernetes container orchestration mechanisms,” in _2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC)_. IEEE, 2019, pp. 11–20.
  * [100] S. López-Huguet, J. D. Segrelles, M. Kasztelnik, M. Bubak, and I. Blanquer, “Seamlessly managing HPC workloads through Kubernetes,” in _International Conference on High Performance Computing_. Springer, 2020, pp. 310–320.
  * [101] S. Choochotkaew, T. Chiba, S. Trent, T. Yoshimura, and M. Amaral, “AutoDECK: Automated Declarative Performance Evaluation and Tuning Framework on Kubernetes,” in _2022 IEEE 15th International Conference on Cloud Computing (CLOUD)_. IEEE, 2022, pp. 309–314.
  * [102] D. Fan and D. He, “Knative Autoscaler Optimize Based on Double Exponential Smoothing,” in _2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)_. IEEE, 2020, pp. 614–617.
  * [103] W. Ling, L. Ma, C. Tian, and Z. Hu, “Pigeon: A dynamic and efficient serverless and faas framework for private cloud,” in _2019 International Conference on Computational Science and Computational Intelligence (CSCI)_. IEEE, 2019, pp. 1416–1421.
  * [104] K. Kaffes, N. J. Yadwadkar, and C. Kozyrakis, “Centralized core-granular scheduling for serverless functions,” in _Proceedings of the ACM symposium on cloud computing_ , 2019, pp. 158–164.
  * [105] S. Venkataraman, A. Panda, G. Ananthanarayanan, M. J. Franklin, and I. Stoica, “The power of choice in $\\{$Data-Aware$\\}$ cluster scheduling,” in _11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)_ , 2014, pp. 301–316.
  * [106] S. Venkataraman, A. Panda, K. Ousterhout, M. Armbrust, A. Ghodsi, M. J. Franklin, B. Recht, and I. Stoica, “Drizzle: Fast and adaptable stream processing at scale,” in _Proceedings of the 26th Symposium on Operating Systems Principles_ , 2017, pp. 374–389.
  * [107] H. Wang, D. Niu, and B. Li, “Distributed machine learning with a serverless architecture,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications_. IEEE, 2019, pp. 1288–1296.
  * [108] R. Gu, K. Zhang, Z. Xu, Y. Che, B. Fan, H. Hou, H. Dai, L. Yi, Y. Ding, G. Chen, and Others, “Fluid: Dataset abstraction and elastic acceleration for cloud-native deep learning training jobs,” in _2022 IEEE 38th International Conference on Data Engineering (ICDE)_. IEEE, 2022, pp. 2182–2195.
  * [109] X. Zhang, L. Li, Y. Wang, E. Chen, and L. Shou, “Zeus: Improving resource efficiency via workload colocation for massive kubernetes clusters,” _IEEE Access_ , vol. 9, pp. 105 192–105 204, 2021.
  * [110] X. Chen, L. Cheng, C. Liu, Q. Liu, J. Liu, Y. Mao, and J. Murphy, “A WOA-based optimization approach for task scheduling in cloud computing systems,” _IEEE Systems Journal_ , vol. 14, no. 3, pp. 3117–3128, 2020\.
  * [111] H. Zhao, S. Deng, Z. Liu, J. Yin, and S. Dustdar, “Distributed redundant placement for microservice-based applications at the edge,” _arXiv preprint arXiv:1911.03600_ , 2019.
  * [112] L. Ye, Y. Xia, L. Yang, and C. Yan, “SHWS: Stochastic Hybrid Workflows Dynamic Scheduling in Cloud Container Services,” _IEEE Transactions on Automation Science and Engineering_ , 2021.
  * [113] Y. Hu, H. Zhou, C. de Laat, and Z. Zhao, “Concurrent container scheduling on heterogeneous clusters with multi-resource constraints,” _Future Generation Computer Systems_ , vol. 102, pp. 562–573, 2020.
  * [114] M. Yu, Y. Tian, B. Ji, C. Wu, H. Rajan, and J. Liu, “Gadget: Online resource optimization for scheduling ring-all-reduce learning jobs,” in _IEEE INFOCOM 2022-IEEE Conference on Computer Communications_. IEEE, 2022, pp. 1569–1578.
  * [115] Y. Mao, Y. Fu, W. Zheng, L. Cheng, Q. Liu, and D. Tao, “Speculative container scheduling for deep learning applications in a kubernetes cluster,” _IEEE Systems Journal_ , 2021.
  * [116] W. Zheng, M. Tynes, H. Gorelick, Y. Mao, L. Cheng, and Y. Hou, “Flowcon: Elastic flow configuration for containerized deep learning applications,” in _Proceedings of the 48th International Conference on Parallel Processing_ , 2019, pp. 1–10.
  * [117] H. Zheng, F. Xu, L. Chen, Z. Zhou, and F. Liu, “Cynthia: Cost-efficient cloud resource provisioning for predictable distributed deep neural network training,” in _Proceedings of the 48th International Conference on Parallel Processing_ , 2019, pp. 1–11.
  * [118] D. Narayanan, A. Harlap, A. Phanishayee, V. Seshadri, N. R. Devanur, G. R. Ganger, P. B. Gibbons, and M. Zaharia, “Pipedream: generalized pipeline parallelism for dnn training,” in _Proceedings of the 27th ACM Symposium on Operating Systems Principles_ , 2019, pp. 1–15.
  * [119] P. Li, E. Koyuncu, and H. Seferoglu, “Respipe: Resilient model-distributed dnn training at edge networks,” in _ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_. IEEE, 2021, pp. 3660–3664.
  * [120] Z. Luo, X. Yi, G. Long, S. Fan, C. Wu, J. Yang, and W. Lin, “Efficient pipeline planning for expedited distributed dnn training,” _arXiv preprint arXiv:2204.10562_ , 2022.
  * [121] X. Yi, Z. Luo, C. Meng, M. Wang, G. Long, C. Wu, J. Yang, and W. Lin, “Fast training of deep learning models over multiple gpus,” in _Proceedings of the 21st International Middleware Conference_ , 2020, pp. 105–118.
  * [122] C. Olston, N. Fiedel, K. Gorovoy, J. Harmsen, L. Lao, F. Li, V. Rajashekhar, S. Ramesh, and J. Soyke, “Tensorflow-serving: Flexible, high-performance ml serving,” _arXiv preprint arXiv:1712.06139_ , 2017.
  * [123] “Multi model server: a tool for serving neural net models for inference,” 2022\. [Online]. Available: https://github.com/awslabs/multi-modelserver
  * [124] T. Liang, J. Glossner, L. Wang, S. Shi, and X. Zhang, “Pruning and quantization for deep neural network acceleration: A survey,” _Neurocomputing_ , vol. 461, pp. 370–403, 2021.
  * [125] A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer, “A survey of quantization methods for efficient neural network inference,” _arXiv preprint arXiv:2103.13630_ , 2021.
  * [126] Y. Choi and M. Rhu, “Prema: A predictive multi-task scheduling algorithm for preemptible neural processing units,” in _2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)_. IEEE, 2020, pp. 220–233.
  * [127] D. Mendoza, F. Romero, Q. Li, N. J. Yadwadkar, and C. Kozyrakis, “Interference-aware scheduling for inference serving,” in _Proceedings of the 1st Workshop on Machine Learning and Systems_ , 2021, pp. 80–88.
  * [128] N. Zhou, H. Zhou, and D. Hoppe, “Containerisation for high performance computing systems: Survey and prospects,” _IEEE Transactions on Software Engineering_ , 2022.
  * [129] A. Reuther, C. Byun, W. Arcand, D. Bestor, B. Bergeron, M. Hubbell, M. Jones, P. Michaleas, A. Prout, A. Rosa _et al._ , “Scalable system scheduling for hpc and big data,” _Journal of Parallel and Distributed Computing_ , vol. 111, pp. 76–92, 2018.
  * [130] M. A. Netto, R. N. Calheiros, E. R. Rodrigues, R. L. Cunha, and R. Buyya, “Hpc cloud for scientific and business applications: taxonomy, vision, and research challenges,” _ACM Computing Surveys (CSUR)_ , vol. 51, no. 1, pp. 1–29, 2018.
  * [131] Y. Fan, “Job scheduling in high performance computing,” _arXiv preprint arXiv:2109.09269_ , 2021.
  * [132] Q. Wofford, P. G. Bridges, and P. Widener, “A layered approach for modular container construction and orchestration in hpc environments,” in _Proceedings of the 11th Workshop on Scientific Cloud Computing_ , 2020, pp. 1–8.
  * [133] S. Julian, M. Shuey, and S. Cook, “Containers in research: initial experiences with lightweight infrastructure,” in _Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale_ , 2016, pp. 1–6.
  * [134] J. Higgins, V. Holmes, and C. Venters, “Orchestrating docker containers in the hpc environment,” in _International Conference on High Performance Computing_. Springer, 2015, pp. 506–513.
  * [135] N. Zhou, Y. Georgiou, L. Zhong, H. Zhou, and M. Pospieszny, “Container orchestration on hpc systems,” in _2020 IEEE 13th International Conference on Cloud Computing (CLOUD)_. IEEE, 2020, pp. 34–36.
  * [136] N. Zhou, “Containerization and orchestration on hpc systems,” in _Sustained Simulation Performance 2019 and 2020_. Springer, 2021, pp. 133–147.
  * [137] N. Zhou, L. Zhong, D. Hoppe, B. Pejak, O. Marko, J. Cardona, M. Czerkawski, I. Andonovic, C. Michie, C. Tachtatzis _et al._ , “Cybele:: A hybrid architecture of hpc and big data for ai applications in agriculture,” in _HPC, Big Data, and AI Convergence Towards Exascale_. CRC Press, 2022, pp. 255–272.
  * [138] F. Liu, K. Keahey, P. Riteau, and J. Weissman, “Dynamically negotiating capacity between on-demand and batch clusters,” in _SC18: International Conference for High Performance Computing, Networking, Storage and Analysis_. IEEE, 2018, pp. 493–503.
  * [139] M. E. Piras, L. Pireddu, M. Moro, and G. Zanetti, “Container orchestration on hpc clusters,” in _International Conference on High Performance Computing_. Springer, 2019, pp. 25–35.
  * [140] J. Carnero and F. J. Nieto, “Running simulations in hpc and cloud resources by implementing enhanced tosca workflows,” in _2018 International Conference on High Performance Computing & Simulation (HPCS)_. IEEE, 2018, pp. 431–438.
  * [141] E. Di Nitto, J. Gorroñogoitia, I. Kumara, G. Meditskos, D. Radolović, K. Sivalingam, and R. S. González, “An approach to support automated deployment of applications on heterogeneous cloud-hpc infrastructures,” in _2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)_. IEEE, 2020, pp. 133–140.
  * [142] I. Colonnelli, B. Cantalupo, I. Merelli, and M. Aldinucci, “Streamflow: cross-breeding cloud with hpc,” _IEEE Transactions on Emerging Topics in Computing_ , vol. 9, no. 4, pp. 1723–1737, 2020.
  * [143] P. Di Tommaso, M. Chatzou, E. W. Floden, P. P. Barja, E. Palumbo, and C. Notredame, “Nextflow enables reproducible computational workflows,” _Nature biotechnology_ , vol. 35, no. 4, pp. 316–319, 2017.
  * [144] “kube-batch,” 2019. [Online]. Available: https://github.com/kubernetes-sigs/kube-batch
  * [145] ©Kubeless 2022 project authors, “The kubernetes native serverless framework,” 2022, https://kubeless.io/.
  * [146] H. Govind and H. González-Vélez, “Benchmarking serverless workloads on kubernetes,” in _2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)_. IEEE, 2021, pp. 704–712.
  * [147] K. Djemame, M. Parker, and D. Datsev, “Open-source serverless architectures: an evaluation of apache openwhisk,” in _2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)_. IEEE, 2020, pp. 329–335.
  * [148] S. K. Mohanty, G. Premsankar, M. Di Francesco _et al._ , “An evaluation of open source serverless computing frameworks.” _CloudCom_ , vol. 2018, pp. 115–120, 2018.
  * [149] O. Mashayekhi, H. Qu, C. Shah, and P. Levis, “Execution templates: Caching control plane decisions for strong scaling of data analytics,” in _2017 USENIX Annual Technical Conference (USENIX ATC 17)_ , 2017, pp. 513–526.
  * [150] J. Huang, C. Xiao, and W. Wu, “Rlsk: a job scheduler for federated kubernetes clusters based on reinforcement learning,” in _2020 IEEE International Conference on Cloud Engineering (IC2E)_. IEEE, 2020, pp. 116–123.
  * [151] Z. Gu, S. Tang, B. Jiang, S. Huang, Q. Guan, and S. Fu, “Characterizing Job-Task Dependency in Cloud Workloads Using Graph Learning,” in _2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)_. IEEE, 2021, pp. 288–297.
  * [152] “Hadoop: Fair scheduler,” April 2019. [Online]. Available: https://hadoop.apache.org/docs/current/hadoopyarn/hadoop-yarnsite/FairScheduler.html
  * [153] W. Song, Z. Xiao, Q. Chen, and H. Luo, “Adaptive resource provisioning for the cloud using online bin packing,” _IEEE Transactions on Computers_ , vol. 63, no. 11, pp. 2647–2660, 2013.
  * [154] R. Grandl, G. Ananthanarayanan, S. Kandula, S. Rao, and A. Akella, “Multi-resource packing for cluster schedulers,” _ACM SIGCOMM Computer Communication Review_ , vol. 44, no. 4, pp. 455–466, 2014.
  * [155] S. Wang, Z. Ding, and C. Jiang, “Elastic scheduling for microservice applications in clouds,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 32, no. 1, pp. 98–115, 2020.
  * [156] D. Zhao, M. Mohamed, and H. Ludwig, “Locality-aware scheduling for containers in cloud computing,” _IEEE Transactions on cloud computing_ , vol. 8, no. 2, pp. 635–646, 2018.
  * [157] L. Bulej, T. Bureš, P. Hnětynka, and D. Khalyeyev, “Self-adaptive K8S Cloud Controller for Time-sensitive Applications,” in _2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)_. IEEE, 2021, pp. 166–169.
  * [158] G. Ambrosino, G. B. Fioccola, R. Canonico, and G. Ventre, “Container Mapping and its Impact on Performance in Containerized Cloud Environments,” in _2020 IEEE International Conference on Service Oriented Systems Engineering (SOSE)_. IEEE, 2020, pp. 57–64.
  * [159] H. Zhao, S. Deng, F. Chen, J. Yin, S. Dustdar, and A. Y. Zomaya, “Learning to Schedule Multi-Server Jobs With Fluctuated Processing Speeds,” _IEEE Transactions on Parallel and Distributed Systems_ , 2022.
  * [160] F. Rossi, V. Cardellini, and F. L. Presti, “Elastic deployment of software containers in geo-distributed computing environments,” in _2019 IEEE Symposium on Computers and Communications (ISCC)_. IEEE, 2019, pp. 1–7.
  * [161] A. Das, S. Imai, S. Patterson, and M. P. Wittie, “Performance optimization for edge-cloud serverless platforms via dynamic task placement,” in _2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)_. IEEE, 2020, pp. 41–50.
  * [162] N. Akhtar, A. Raza, V. Ishakian, and I. Matta, “Cose: Configuring serverless functions using statistical learning,” in _IEEE INFOCOM 2020-IEEE Conference on Computer Communications_. IEEE, 2020, pp. 129–138.
  * [163] Y. Aldwyan, R. O. Sinnott, and G. T. Jayaputera, “Elastic deployment of container clusters across geographically distributed cloud data centers for web applications,” _Concurrency and Computation: Practice and Experience_ , vol. 33, no. 21, p. e6436, 2021.
  * [164] T. Shi, H. Ma, G. Chen, and S. Hartmann, “Location-Aware and Budget-Constrained Service Brokering in Multi-Cloud via Deep Reinforcement Learning,” in _International Conference on Service-Oriented Computing_. Springer, 2021, pp. 756–764.
  * [165] ——, “Location-aware and budget-constrained service deployment for composite applications in multi-cloud environment,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 31, no. 8, pp. 1954–1969, 2020.
  * [166] H. Sami, A. Mourad, H. Otrok, and J. Bentahar, “Fscaler: Automatic resource scaling of containers in fog clusters using reinforcement learning,” in _2020 international wireless communications and mobile computing (IWCMC)_. IEEE, 2020, pp. 1824–1829.
  * [167] M. Yan, X. Liang, Z. Lu, J. Wu, and W. Zhang, “Hansel: Adaptive horizontal scaling of microservices using bi-lstm,” _Applied Soft Computing_ , vol. 105, p. 107216, 2021.
  * [168] A. Marchese and O. Tomarchio, “Network-Aware Container Placement in Cloud-Edge Kubernetes Clusters,” in _2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)_. IEEE, 2022, pp. 859–865.
  * [169] S. Wang, Y. Guo, N. Zhang, P. Yang, A. Zhou, and X. Shen, “Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach,” _IEEE Transactions on Mobile Computing_ , vol. 20, no. 3, pp. 939–951, 2019.
  * [170] T. Pusztai, S. Nastic, A. Morichetta, V. C. Pujol, P. Raith, S. Dustdar, D. Vij, Y. Xiong, and Z. Zhang, “Polaris scheduler: Slo-and topology-aware microservices scheduling at the edge,” in _2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)_. IEEE, 2022, pp. 61–70.
  * [171] T. Pusztai, F. Rossi, and S. Dustdar, “Pogonip: Scheduling asynchronous applications on the edge,” in _2021 IEEE 14th International Conference on Cloud Computing (CLOUD)_. IEEE, 2021, pp. 660–670.
  * [172] S. Nastic, T. Pusztai, A. Morichetta, V. C. Pujol, S. Dustdar, D. Vii, and Y. Xiong, “Polaris scheduler: Edge sensitive and slo aware workload scheduling in cloud-edge-iot clusters,” in _2021 IEEE 14th International Conference on Cloud Computing (CLOUD)_. IEEE, 2021, pp. 206–216.
  * [173] Z. Tang, X. Zhou, F. Zhang, W. Jia, and W. Zhao, “Migration modeling and learning algorithms for containers in fog computing,” _IEEE Transactions on Services Computing_ , vol. 12, no. 5, pp. 712–725, 2018.
  * [174] Z. Miao, P. Yong, Y. Mei, Y. Quanjun, and X. Xu, “A discrete pso-based static load balancing algorithm for distributed simulations in a cloud environment,” _Future Generation Computer Systems_ , vol. 115, pp. 497–516, 2021.
  * [175] H. Lu, G. Xu, C. W. Sung, S. Mostafa, and Y. Wu, “A game theoretical balancing approach for offloaded tasks in edge datacenters,” in _2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)_. IEEE, 2022, pp. 526–536.
  * [176] M. Kumar and S. C. Sharma, “Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment,” _Computers & Electrical Engineering_, vol. 69, pp. 395–411, 2018.
  * [177] R. Yu, V. T. Kilari, G. Xue, and D. Yang, “Load balancing for interdependent iot microservices,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications_. IEEE, 2019, pp. 298–306.
  * [178] L. Huang, S. Cheng, Y. Guan, X. Zhang, and Z. Guo, “Consistent user-traffic allocation and load balancing in mobile edge caching,” in _IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_. IEEE, 2020, pp. 592–597.
  * [179] J. Wang, G. Zhao, H. Xu, H. Huang, L. Luo, and Y. Yang, “Robust service mapping in multi-tenant clouds,” in _IEEE INFOCOM 2021-IEEE Conference on Computer Communications_. IEEE, 2021, pp. 1–10.
  * [180] J. O. Gutierrez-Garcia and A. Ramirez-Nafarrate, “Agent-based load balancing in cloud data centers,” _Cluster Computing_ , vol. 18, no. 3, pp. 1041–1062, 2015.
  * [181] H. Menon and L. Kalé, “A distributed dynamic load balancer for iterative applications,” in _SC’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis_. IEEE, 2013, pp. 1–11.
  * [182] X. Xu, Q. Jiang, P. Zhang, X. Cao, M. R. Khosravi, L. T. Alex, L. Qi, and W. Dou, “Game theory for distributed iov task offloading with fuzzy neural network in edge computing,” _IEEE Transactions on Fuzzy Systems_ , vol. 30, no. 11, pp. 4593–4604, 2022.
  * [183] Z. Yao, Z. Ding, and T. Clausen, “Multi-agent reinforcement learning for network load balancing in data center,” in _Proceedings of the 31st ACM International Conference on Information & Knowledge Management_, 2022, pp. 3594–3603.
  * [184] A. Asghari and M. K. Sohrabi, “Combined use of coral reefs optimization and multi-agent deep q-network for energy-aware resource provisioning in cloud data centers using dvfs technique,” _Cluster Computing_ , vol. 25, no. 1, pp. 119–140, 2022.
  * [185] O. Houidi, S. Bakri, and D. Zeghlache, “Multi-agent graph convolutional reinforcement learning for intelligent load balancing,” in _NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium_. IEEE, 2022, pp. 1–6.
  * [186] A. Shribman and B. Hudzia, “Pre-copy and post-copy vm live migration for memory intensive applications,” in _European Conference on Parallel Processing_. Springer, 2012, pp. 539–547.
  * [187] D. Fernando, J. Terner, K. Gopalan, and P. Yang, “Live migration ate my vm: Recovering a virtual machine after failure of post-copy live migration,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications_. IEEE, 2019, pp. 343–351.
  * [188] C. C. Chou, Y. Chen, D. Milojicic, N. Reddy, and P. Gratz, “Optimizing post-copy live migration with system-level checkpoint using fabric-attached memory,” in _2019 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC)_. IEEE, 2019, pp. 16–24.
  * [189] C. Jo, Y. Cho, and B. Egger, “A machine learning approach to live migration modeling,” in _Proceedings of the 2017 Symposium on Cloud Computing_ , 2017, pp. 351–364.
  * [190] N. T. Khai, A. Baumgartner, and T. Bauschert, “A multi-step model for migration and resource reallocation in virtualized network infrastructures,” in _2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_. IEEE, 2017, pp. 730–735.
  * [191] A. Ruprecht, D. Jones, D. Shiraev, G. Harmon, M. Spivak, M. Krebs, M. Baker-Harvey, and T. Sanderson, “Vm live migration at scale,” _ACM SIGPLAN Notices_ , vol. 53, no. 3, pp. 45–56, 2018.
  * [192] C. Li, D. Feng, Y. Hua, W. Xia, L. Qin, Y. Huang, and Y. Zhou, “Bac: Bandwidth-aware compression for efficient live migration of virtual machines,” in _IEEE INFOCOM 2017-IEEE Conference on Computer Communications_. IEEE, 2017, pp. 1–9.
  * [193] F. Le and E. M. Nahum, “Experiences implementing live vm migration over the wan with multi-path tcp,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications_. IEEE, 2019, pp. 1090–1098.
  * [194] D. Basu, X. Wang, Y. Hong, H. Chen, and S. Bressan, “Learn-as-you-go with megh: Efficient live migration of virtual machines,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 30, no. 8, pp. 1786–1801, 2019.
  * [195] T. Benjaponpitak, M. Karakate, and K. Sripanidkulchai, “Enabling live migration of containerized applications across clouds,” in _IEEE INFOCOM 2020-IEEE Conference on Computer Communications_. IEEE, 2020, pp. 2529–2538.
  * [196] P. K. Sinha, S. S. Doddamani, H. Lu, and K. Gopalan, “mwarp: accelerating intra-host live container migration via memory warping,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_. IEEE, 2019, pp. 508–513.
  * [197] B. Xu, S. Wu, J. Xiao, H. Jin, Y. Zhang, G. Shi, T. Lin, J. Rao, L. Yi, and J. Jiang, “Sledge: Towards efficient live migration of docker containers,” in _2020 IEEE 13th International Conference on Cloud Computing (CLOUD)_. IEEE, 2020, pp. 321–328.
  * [198] R. Torre, E. Urbano, H. Salah, G. T. Nguyen, and F. H. Fitzek, “Towards a better understanding of live migration performance with docker containers,” in _European Wireless 2019; 25th European Wireless Conference_. VDE, 2019, pp. 1–6.
  * [199] F. Romero, Q. Li, N. J. Yadwadkar, and C. Kozyrakis, “$\\{$INFaaS$\\}$: Automated model-less inference serving,” in _2021 USENIX Annual Technical Conference (USENIX ATC 21)_ , 2021, pp. 397–411.
  * [200] C. Zhang, M. Yu, W. Wang, and F. Yan, “$\\{$MArk$\\}$: Exploiting cloud services for $\\{$Cost-Effective$\\}$,$\\{$SLO-Aware$\\}$ machine learning inference serving,” in _2019 USENIX Annual Technical Conference (USENIX ATC 19)_ , 2019, pp. 1049–1062.
  * [201] S. Shillaker and P. Pietzuch, “Faasm: Lightweight isolation for efficient stateful serverless computing,” in _2020 USENIX Annual Technical Conference (USENIX ATC 20)_ , 2020, pp. 419–433.
  * [202] N. Akhtar, I. Matta, A. Raza, and Y. Wang, “El-sec: Elastic management of security applications on virtualized infrastructure,” in _IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_. IEEE, 2018, pp. 778–783.
  * [203] G. R. Russo, V. Cardellini, G. Casale, and F. L. Presti, “Mead: Model-based vertical auto-scaling for data stream processing,” in _2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)_. IEEE, 2021, pp. 314–323.
  * [204] E. B. Lakew, A. V. Papadopoulos, M. Maggio, C. Klein, and E. Elmroth, “Kpi-agnostic control for fine-grained vertical elasticity,” in _2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)_. IEEE, 2017, pp. 589–598.
  * [205] Y. Sfakianakis, M. Marazakis, C. Kozanitis, and A. Bilas, “Latest: Vertical elasticity for millisecond serverless execution,” in _2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)_. IEEE, 2022, pp. 879–885.
  * [206] S. K. Tesfatsion, L. Tomás, and J. Tordsson, “Optibook: Optimal resource booking for energy-efficient datacenters,” in _2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)_. IEEE, 2017, pp. 1–10.
  * [207] X. Fei, F. Liu, H. Xu, and H. Jin, “Adaptive vnf scaling and flow routing with proactive demand prediction,” in _IEEE INFOCOM 2018-IEEE Conference on Computer Communications_. IEEE, 2018, pp. 486–494.
  * [208] M. Avgeris, D. Dechouniotis, N. Athanasopoulos, and S. Papavassiliou, “Adaptive resource allocation for computation offloading: A control-theoretic approach,” _ACM Transactions on Internet Technology (TOIT)_ , vol. 19, no. 2, pp. 1–20, 2019.
  * [209] R. Mahmud, K. Ramamohanarao, and R. Buyya, “Latency-aware application module management for fog computing environments,” _ACM Transactions on Internet Technology (TOIT)_ , vol. 19, no. 1, pp. 1–21, 2018.
  * [210] L. Schuler, S. Jamil, and N. Kühl, “Ai-based resource allocation: Reinforcement learning for adaptive auto-scaling in serverless environments,” in _2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)_. IEEE, 2021, pp. 804–811.
  * [211] “F5 nginx management suite.” [Online]. Available: https://www.nginx.com/
  * [212] “Haproxy:the reliable, high performance tcp/http load balancer.” [Online]. Available: https://www.haproxy.org/
  * [213] D. E. Eisenbud, C. Yi, C. Contavalli, C. Smith, R. Kononov, E. Mann-Hielscher, A. Cilingiroglu, B. Cheyney, W. Shang, and J. D. Hosein, “Maglev: A fast and reliable software network load balancer,” in _13th $\\{$USENIX$\\}$ Symposium on Networked Systems Design and Implementation ($\\{$NSDI$\\}$ 16)_, 2016, pp. 523–535.
  * [214] T. Barbette, C. Tang, H. Yao, D. Kostic, G. Q. Maguire Jr, P. Papadimitratos, and M. Chiesa, “A high-speed load-balancer design with guaranteed per-connection-consistency.” in _NSDI_ , 2020, pp. 667–683.
  * [215] M. E. Elsaid, H. M. Abbas, and C. Meinel, “Virtual machines pre-copy live migration cost modeling and prediction: a survey,” _Distributed and Parallel Databases_ , vol. 40, no. 2, pp. 441–474, 2022.
  * [216] M. Noshy, A. Ibrahim, and H. A. Ali, “Optimization of live virtual machine migration in cloud computing: A survey and future directions,” _Journal of Network and Computer Applications_ , vol. 110, pp. 1–10, 2018.
  * [217] L. Ma, S. Yi, N. Carter, and Q. Li, “Efficient live migration of edge services leveraging container layered storage,” _IEEE Transactions on Mobile Computing_ , vol. 18, no. 9, pp. 2020–2033, 2018.
  * [218] “Xen project brings the power of virtualization everywhere.” [Online]. Available: https://xenproject.org/
  * [219] “Open source container-based virtualization for linux.” [Online]. Available: https://openvz.org/
  * [220] “A project to implement checkpoint/restore functionality for linux.” [Online]. Available: criu.org
  * [221] G. Somma, C. Ayimba, P. Casari, S. P. Romano, and V. Mancuso, “When less is more: Core-restricted container provisioning for serverless computing,” in _IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_. IEEE, 2020, pp. 1153–1159.
  * [222] N. Salhab, R. Rahim, and R. Langar, “Nfv orchestration platform for 5g over on-the-fly provisioned infrastructure,” in _IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)_. IEEE, 2019, pp. 971–972.
  * [223] A. Shahidinejad, M. Ghobaei-Arani, and M. Masdari, “Resource provisioning using workload clustering in cloud computing environment: a hybrid approach,” _Cluster Computing_ , vol. 24, no. 1, pp. 319–342, 2021.
  * [224] “Horizontal pod autoscaler walkthrough.” [Online]. Available: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/
  * [225] “Scaling and concurrency in lambda.” [Online]. Available: https://docs.aws.amazon.com/lambda/latest/operatorguide/scaling-concurrency.html
  * [226] “an open-source enterprise-level solution to build serverless and event driven applications.” [Online]. Available: https://knative.dev/docs/serving/autoscaling/kpa-specific/
  * [227] R. Birke, I. Giurgiu, L. Y. Chen, D. Wiesmann, and T. Engbersen, “Failure analysis of virtual and physical machines: patterns, causes and characteristics,” in _2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks_. IEEE, 2014, pp. 1–12.
  * [228] D. Ford, F. Labelle, F. I. Popovici, M. Stokely, V.-A. Truong, L. Barroso, C. Grimes, and S. Quinlan, “Availability in globally distributed storage systems,” in _9th USENIX Symposium on Operating Systems Design and Implementation (OSDI 10)_ , 2010.
  * [229] V. N. Padmanabhan, S. Ramabhadran, S. Agarwal, and J. Padhye, “A study of end-to-end web access failures,” in _Proceedings of the 2006 ACM CoNEXT conference_ , 2006, pp. 1–13.
  * [230] C. Lu, K. Ye, G. Xu, C.-Z. Xu, and T. Bai, “Imbalance in the cloud: An analysis on alibaba cluster trace,” in _2017 IEEE International Conference on Big Data (Big Data)_. IEEE, 2017, pp. 2884–2892.
  * [231] Y. Jiang, M. Shahrad, D. Wentzlaff, D. H. Tsang, and C. Joe-Wong, “Burstable instances for clouds: Performance modeling, equilibrium analysis, and revenue maximization,” _IEEE/ACM Transactions on Networking_ , vol. 28, no. 6, pp. 2489–2502, 2020.
  * [232] D. Ardelean, A. Diwan, and C. Erdman, “Performance analysis of cloud applications,” in _15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18)_ , 2018, pp. 405–417.
  * [233] Y. Zhu, W. Zhang, Y. Chen, and H. Gao, “A novel approach to workload prediction using attention-based lstm encoder-decoder network in cloud environment,” _EURASIP Journal on Wireless Communications and Networking_ , vol. 2019, no. 1, pp. 1–18, 2019.
  * [234] Z. Chen, J. Hu, G. Min, A. Y. Zomaya, and T. El-Ghazawi, “Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 31, no. 4, pp. 923–934, 2020.
  * [235] C. Xu, G. Wang, X. Liu, D. Guo, and T.-Y. Liu, “Health status assessment and failure prediction for hard drives with recurrent neural networks,” _IEEE Transactions on Computers_ , vol. 65, no. 11, pp. 3502–3508, 2016.
  * [236] X. Sun, K. Chakrabarty, R. Huang, Y. Chen, B. Zhao, H. Cao, Y. Han, X. Liang, and L. Jiang, “System-level hardware failure prediction using deep learning,” in _2019 56th ACM/IEEE Design Automation Conference (DAC)_ , 2019, pp. 1–6.
  * [237] J. Xiao, Z. Xiong, S. Wu, Y. Yi, H. Jin, and K. Hu, “Disk failure prediction in data centers via online learning,” in _Proceedings of the 47th International Conference on Parallel Processing_ , ser. ICPP 2018. New York, NY, USA: Association for Computing Machinery, 2018. [Online]. Available: https://doi.org/10.1145/3225058.3225106
  * [238] X. Zeng, S. Garg, M. Barika, S. Bista, D. Puthal, A. Y. Zomaya, and R. Ranjan, “Detection of sla violation for big data analytics applications in cloud,” _IEEE Transactions on Computers_ , vol. 70, no. 5, pp. 746–758, 2020.
  * [239] X. Zhang, J. Kim, Q. Lin, K. Lim, S. O. Kanaujia, Y. Xu, K. Jamieson, A. Albarghouthi, S. Qin, M. J. Freedman _et al._ , “Cross-dataset time series anomaly detection for cloud systems,” in _2019 USENIX Annual Technical Conference (USENIX ATC 19)_ , 2019, pp. 1063–1076.
  * [240] T. Pimentel, M. Monteiro, A. Veloso, and N. Ziviani, “Deep active learning for anomaly detection,” in _2020 International Joint Conference on Neural Networks (IJCNN)_. IEEE, 2020, pp. 1–8.
  * [241] M. Ma, S. Zhang, J. Chen, J. Xu, H. Li, Y. Lin, X. Nie, B. Zhou, Y. Wang, and D. Pei, “$\\{$Jump-Starting$\\}$ multivariate time series anomaly detection for online service systems,” in _2021 USENIX Annual Technical Conference (USENIX ATC 21)_ , 2021, pp. 413–426.
  * [242] H. Peng, R. Yang, Z. Wang, J. Li, L. He, S. Y. Philip, A. Y. Zomaya, and R. Ranjan, “Lime: Low-cost and incremental learning for dynamic heterogeneous information networks,” _IEEE Transactions on Computers_ , vol. 71, no. 3, pp. 628–642, 2021.
  * [243] J. Xu, Y. Wang, P. Chen, and P. Wang, “Lightweight and adaptive service api performance monitoring in highly dynamic cloud environment,” in _2017 IEEE International Conference on Services Computing (SCC)_ , 2017, pp. 35–43.
  * [244] L. Li, X. Zhang, X. Zhao, H. Zhang, Y. Kang, P. Zhao, B. Qiao, S. He, P. Lee, J. Sun _et al._ , “Fighting the fog of war: Automated incident detection for cloud systems,” in _2021 USENIX Annual Technical Conference (USENIX ATC 21)_ , 2021, pp. 131–146.
  * [245] P. Wang, J. Xu, M. Ma, W. Lin, D. Pan, Y. Wang, and P. Chen, “Cloudranger: Root cause identification for cloud native systems,” in _2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)_. IEEE, 2018, pp. 492–502.
  * [246] U. Demirbaga, Z. Wen, A. Noor, K. Mitra, K. Alwasel, S. Garg, A. Y. Zomaya, and R. Ranjan, “Autodiagn: An automated real-time diagnosis framework for big data systems,” _IEEE Transactions on Computers_ , vol. 71, no. 5, pp. 1035–1048, 2021.
  * [247] “Dx unified infrastructure management,” [n.d.]. [Online]. Available: https://community.broadcom.com/enterprisesoftware/communities/communityhomeblogs?CommunityKey=170eb4e5-a593-4af2-ad1d-f7655e31513b
  * [248] “Ca unified infrastructure management,” [n.d.]. [Online]. Available: http://aspiretp.com/uim/
  * [249] “Dtrace,” [n.d.]. [Online]. Available: http://dtrace.org/blogs/about
  * [250] P. Barham, A. Donnelly, R. Isaacs, and R. Mortier, “Using magpie for request extraction and workload modelling.” in _OSDI_ , vol. 4, 2004, pp. 18–18.
  * [251] M. Arnold, M. Hind, and B. G. Ryder, “Online feedback-directed optimization of java,” _ACM SIGPLAN Notices_ , vol. 37, no. 11, pp. 111–129, 2002.
  * [252] M. Hauswirth, P. F. Sweeney, A. Diwan, and M. Hind, “Vertical profiling: Understanding the behavior of object-priented applications,” _SIGPLAN Not._ , vol. 39, no. 10, p. 251–269, oct 2004. [Online]. Available: https://doi.org/10.1145/1035292.1028998
  * [253] L. Zheng, J. Zhai, X. Tang, H. Wang, T. Yu, Y. Jin, S. L. Song, and W. Chen, “Vapro: Performance variance detection and diagnosis for production-run parallel applications,” in _Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming_ , ser. PPoPP ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 150–162. [Online]. Available: https://doi.org/10.1145/3503221.3508411
  * [254] P. Su, S. Jiao, M. Chabbi, and X. Liu, “Pinpointing performance inefficiencies via lightweight variance profiling,” in _Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis_ , 2019, pp. 1–19.
  * [255] I. Laguna, D. H. Ahn, B. R. De Supinski, S. Bagchi, and T. Gamblin, “Diagnosis of performance faults in largescale mpi applications via probabilistic progress-dependence inference,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 26, no. 5, pp. 1280–1289, 2014.
  * [256] P. Dinda, “Design, implementation, and performance of an extensible toolkit for resource prediction in distributed systems,” _IEEE Transactions on Parallel and Distributed Systems_ , vol. 17, no. 2, pp. 160–173, 2006.
  * [257] R. N. Calheiros, E. Masoumi, R. Ranjan, and R. Buyya, “Workload prediction using arima model and its impact on cloud applications’ qos,” _IEEE Transactions on Cloud Computing_ , vol. 3, no. 4, pp. 449–458, 2015.
  * [258] G. K. Shyam and S. S. Manvi, “Virtual resource prediction in cloud environment: A bayesian approach,” _Journal of Network and Computer Applications_ , vol. 65, pp. 144–154, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1084804516300091
  * [259] P. Singh, P. Gupta, and K. Jyoti, “Tasm: technocrat arima and svr model for workload prediction of web applications in cloud,” _Cluster Computing_ , vol. 22, no. 2, pp. 619–633, 2019.
  * [260] W. Zhang, B. Li, D. Zhao, F. Gong, and Q. Lu, “Workload prediction for cloud cluster using a recurrent neural network,” in _2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)_ , 2016, pp. 104–109.
  * [261] J. Kumar, R. Goomer, and A. K. Singh, “Long short term memory recurrent neural network (lstm-rnn) based workload forecasting model for cloud datacenters,” _Procedia Computer Science_ , vol. 125, pp. 676–682, 2018, the 6th International Conference on Smart Computing and Communications. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050917328557
  * [262] H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 35, no. 12, 2021, pp. 11 106–11 115.
  * [263] G. Hamerly, C. Elkan _et al._ , “Bayesian approaches to failure prediction for disk drives,” in _ICML_ , vol. 1, no. 2001, 2001, pp. 202–209.
  * [264] G. F. Hughes, J. F. Murray, K. Kreutz-Delgado, and C. Elkan, “Improved disk-drive failure warnings,” _IEEE transactions on reliability_ , vol. 51, no. 3, pp. 350–357, 2002.
  * [265] J. F. Murray, G. F. Hughes, K. Kreutz-Delgado, and D. Schuurmans, “Machine learning methods for predicting failures in hard drives: A multiple-instance application.” _Journal of Machine Learning Research_ , vol. 6, no. 5, 2005\.
  * [266] S. J. Russell, _Artificial intelligence a modern approach_. Pearson Education, Inc., 2010.
  * [267] S. Roy, A. C. Konig, I. Dvorkin, and M. Kumar, “Perfaugur: Robust diagnostics for performance anomalies in cloud services,” in _2015 IEEE 31st International Conference on Data Engineering_. IEEE, 2015, pp. 1167–1178.
  * [268] T. Chalermarrewong, T. Achalakul, and S. C. W. See, “Failure prediction of data centers using time series and fault tree analysis,” in _2012 IEEE 18th International Conference on Parallel and Distributed Systems_. IEEE, 2012, pp. 794–799.
  * [269] M. Godse, U. Bellur, and R. Sonar, “Automating qos based service selection,” in _2010 IEEE International Conference on Web Services_. IEEE, 2010, pp. 534–541.
  * [270] I. Fronza, A. Sillitti, G. Succi, M. Terho, and J. Vlasenko, “Failure prediction based on log files using random indexing and support vector machines,” _Journal of Systems and Software_ , vol. 86, no. 1, pp. 2–11, 2013.
  * [271] S. Fu and C.-Z. Xu, “Exploring event correlation for failure prediction in coalitions of clusters,” in _Proceedings of the 2007 ACM/IEEE conference on Supercomputing_ , 2007, pp. 1–12.
  * [272] B. Cavallo, M. Di Penta, and G. Canfora, “An empirical comparison of methods to support qos-aware service selection,” in _Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems_ , 2010, pp. 64–70.
  * [273] A. Amin, A. Colman, and L. Grunske, “An approach to forecasting qos attributes of web services based on arima and garch models,” in _2012 IEEE 19th International Conference on Web Services_ , 2012, pp. 74–81.
  * [274] X. Chen, C.-D. Lu, and K. Pattabiraman, “Failure prediction of jobs in compute clouds: A google cluster case study,” in _2014 IEEE International Symposium on Software Reliability Engineering Workshops_. IEEE, 2014, pp. 341–346.
  * [275] M. Du, F. Li, G. Zheng, and V. Srikumar, “Deeplog: Anomaly detection and diagnosis from system logs through deep learning,” in _Proceedings of the 2017 ACM SIGSAC conference on computer and communications security_ , 2017, pp. 1285–1298.
  * [276] J. Gao, H. Wang, and H. Shen, “Task failure prediction in cloud data centers using deep learning,” _IEEE Transactions on Services Computing_ , vol. 15, no. 3, pp. 1411–1422, 2022.
  * [277] S. Mohseni, M. Pitale, J. Yadawa, and Z. Wang, “Self-supervised learning for generalizable out-of-distribution detection,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 34, no. 04, 2020, pp. 5216–5223.
  * [278] J. Sun, L. Yang, J. Zhang, F. Liu, M. Halappanavar, D. Fan, and Y. Cao, “Gradient-based novelty detection boosted by self-supervised binary classification,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 36, no. 8, 2022, pp. 8370–8377.
  * [279] A. Daneshpazhouh and A. Sami, “Entropy-based outlier detection using semi-supervised approach with few positive examples,” _Pattern Recognition Letters_ , vol. 49, pp. 77–84, 2014.
  * [280] S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning,” _Pattern Recognition_ , vol. 58, pp. 121–134, 2016\.
  * [281] C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, and N. V. Chawla, “A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data,” in _Proceedings of the AAAI conference on artificial intelligence_ , vol. 33, no. 01, 2019, pp. 1409–1416.
  * [282] W. Chen, L. Tian, B. Chen, L. Dai, Z. Duan, and M. Zhou, “Deep variational graph convolutional recurrent network for multivariate time series anomaly detection,” in _International Conference on Machine Learning_. PMLR, 2022, pp. 3621–3633.
  * [283] T. Wen and R. Keyes, “Time series anomaly detection using convolutional neural networks and transfer learning,” _arXiv preprint arXiv:1905.13628_ , 2019\.
  * [284] G. Michau and O. Fink, “Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer,” _Knowledge-Based Systems_ , vol. 216, p. 106816, 2021.
  * [285] A. Borghesi, A. Bartolini, M. Lombardi, M. Milano, and L. Benini, “Anomaly detection using autoencoders in high performance computing systems,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 33, no. 01, 2019, pp. 9428–9433.
  * [286] Y. Zuo, Y. Wu, G. Min, C. Huang, and K. Pei, “An intelligent anomaly detection scheme for micro-services architectures with temporal and spatial data analysis,” _IEEE Transactions on Cognitive Communications and Networking_ , vol. 6, no. 2, pp. 548–561, 2020.
  * [287] L. Shen, Z. Yu, Q. Ma, and J. T. Kwok, “Time series anomaly detection with multiresolution ensemble decoding,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 35, no. 11, 2021, pp. 9567–9575.
  * [288] H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng _et al._ , “Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications,” in _Proceedings of the 2018 world wide web conference_ , 2018, pp. 187–196.
  * [289] Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, “Robust anomaly detection for multivariate time series through stochastic recurrent neural network,” in _Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining_, 2019, pp. 2828–2837.
  * [290] J. Sipple, “Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure,” in _International Conference on Machine Learning_. PMLR, 2020, pp. 9016–9025.
  * [291] “Prometheus - monitoring system & time series database,” [n.d.]. [Online]. Available: https://prometheus.io/
  * [292] H. Abu-Libdeh, L. Princehouse, and H. Weatherspoon, “Racs: a case for cloud storage diversity,” in _Proceedings of the 1st ACM symposium on Cloud computing_ , 2010, pp. 229–240.
  * [293] R. Kang, M. Zhu, F. He, and E. Oki, “Implementation of virtual network function allocation with diversity and redundancy in kubernetes,” in _2021 IFIP Networking Conference (IFIP Networking)_. IEEE, 2021, pp. 1–2.
  * [294] M. Uluyol, A. Huang, A. Goel, M. Chowdhury, and H. V. Madhyastha, “$\\{$Near-Optimal$\\}$ latency versus cost tradeoffs in $\\{$Geo-Distributed$\\}$ storage,” in _17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20)_ , 2020, pp. 157–180.
  * [295] Y. Aldwyan and R. O. Sinnott, “Latency-aware failover strategies for containerized web applications in distributed clouds,” _Future Generation Computer Systems_ , vol. 101, pp. 1081–1095, 2019.
  * [296] H. Jin, G. Yang, B.-y. Yu, and C. Yoo, “Fave: Bandwidth-aware failover in virtualized sdn for clouds,” in _2019 IEEE 12th International Conference on Cloud Computing (CLOUD)_. IEEE, 2019, pp. 505–507.
  * [297] R. Landa, L. Saino, L. Buytenhek, and J. T. Araújo, “Staying alive: Connection path reselection at the edge,” in _18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21)_ , 2021, pp. 233–251.
  * [298] I. Giannakopoulos, I. Konstantinou, D. Tsoumakos, and N. Koziris, “Cloud application deployment with transient failure recovery,” _Journal of Cloud Computing_ , vol. 7, no. 1, pp. 1–20, 2018.

|  Shuiguang Deng is currently a full professor at the College of Computer
Science and Te chnology in Zhejiang University, China, where he received a BS
and PhD degree both in Computer Science in 2002 and 2007, respectively. He
previously worked at the Massachusetts Institute of Technology in 2014 and
Stanford University in 2015 as a visiting scholar. His research interests
include Edge Computing, Service Computing, Cloud Computing, and Business
Process Management. He serves for the journal IEEE Trans. on Services
Computing, Knowledge and Information Systems, Computing, and IET Cyber-
Physical Systems: Theory & Applications as an Associate Editor. Up to now, he
has published more than 100 papers in journals and refereed conferences. In
2018, he was granted the Rising Star Award by IEEE TCSVC. He is a fellow of
IET and a senior member of IEEE.
---|---
|  Hailiang Zhao received the B.S. degree in 2019 from the school of computer
science and technology, Wuhan University of Technology, Wuhan, China. He is
currently pursuing the Ph.D. degree with the College of Computer Science and
Technology, Zhejiang University, Hangzhou, China. His research interests
include cloud & edge computing, distributed computing and optimization
algorithms. He has published several papers in flagship conferences and
journals including IEEE ICWS 2019, IEEE TPDS, IEEE TMC, etc. He has been a
recipient of the Best Student Paper Award of IEEE ICWS 2019. He is a reviewer
for IEEE TSC and Internet of Things Journal.
---|---
|  Binbin Huang is an Assistant Professor in the College of Computer Science
at the University of Hangzhou Dianzi, in Hangzhou, China. She received her PhD
degree in Computer Science and Technology from Beijing University of Posts and
Telecommunications in 2014. His research interests include cloud computing,
mobile edge computing, and reinforcement learning.
---|---
|  Cheng Zhang received the MS degree in electrical engineering from Zhejiang
University, China, in 2013, Currently, he is working toward the PhD degree in
computer science and technology at Zhejiang University. His research interests
include edge computing and edge intelligence.
---|---
|  Feiyi Chen received the B.S. degree in 2021 from the school of computer
science and engineering, Sun Yat-sen University (SYSU), Guangzhou, China. She
is currently pursuing the master degree with the College of Computer Science
and Technology, Zhejiang University, Hangzhou, China. Her research interests
include cloud computing, edge computing, and distributed systems.
---|---
|  Yinuo Deng received the B.S. degree in 2022 from the School of Artificial
Intelligence, Beijing University of Posts and Telecommunications, Beijing,
China. He is currently a M.S. student in computer science of technology at
College of Computer Science and Technology, Zhejiang University, Hangzhou,
China. His research interests include cloud computing, networking, and
distributed systems.
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|  Jianwei Yin received the Ph.D. degree in computer science from Zhejiang
University (ZJU) in 2001. He was a Visiting Scholar with the Georgia Institute
of Technology. He is currently a Full Professor with the College of Computer
Science, ZJU. Up to now, he has published more than 100 papers in top
international journals and conferences. His current research interests include
service computing and business process management. He is an Associate Editor
of the IEEE Transactions on Services Computing.
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|  Schahram Dustdar is a Full Professor of Computer Science (Informatics) with
a focus on Internet Technologies heading the Distributed Systems Group at the
TU Wien. He was founding co-Editor-in-Chief of ACM Transactions on Internet of
Things (ACM TIoT). He is Editor-in-Chief of Computing (Springer). He is an
Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions
on Cloud Computing, ACM Computing Surveys, ACM Transactions on the Web, and
ACM Transactions on Internet Technology, as well as on the editorial board of
IEEE Internet Computing and IEEE Computer. Dustdar is recipient of multiple
awards: TCI Distinguished Service Award (2021), IEEE TCSVC Outstanding
Leadership Award (2018), IEEE TCSC Award for Excellence in Scalable Computing
(2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021),
IBM Faculty Award (2012). He is an elected member of the Academia Europaea:
The Academy of Europe, where the chairman of the Informatics Section for
multiple years. He is an IEEE Fellow (2016), an Asia-Pacific Artificial
Intelligence Association (AAIA) President (2021) and Fellow (2021). He is an
EAI Fellow (2021) and an I2CICC Fellow (2021). He is a Member of the IEEE
Computer Society Fellow Evaluating Committee (2022 and 2023).
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|  Albert Y. Zomaya is the Peter Nicol Russell Chair Professor of Computer
Science and Director of the Centre for Distributed and High-Performance
Computing at the University of Sydney. To date, he has published > 600
scientific papers and articles and is (co-)author/editor of > 30 books. A
sought-after speaker, he has delivered > 250 keynote addresses, invited
seminars, and media briefings. His research interests span several areas in
parallel and distributed computing and complex systems. He is currently the
Editor in Chief of the ACM Computing Surveys and processed in the past as
Editor in Chief of the IEEE Transactions on Computers (2010-2014) and the IEEE
Transactions on Sustainable Computing (2016-2020). Professor Zomaya is a
decorated scholar with numerous accolades including Fellowship of the IEEE,
the American Association for the Advancement of Science, and the Institution
of Engineering and Technology (UK). Also, he is an Elected Fellow of the Royal
Society of New South Wales and an Elected Foreign Member of Academia Europaea.
He is the recipient of the 1997 Edgeworth David Medal from the Royal Society
of New South Wales for outstanding contributions to Australian Science, the
IEEE Technical Committee on Parallel Processing Outstanding Service Award
(2011), IEEE Technical Committee on Scalable Computing Medal for Excellence in
Scalable Computing (2011), IEEE Computer Society Technical Achievement Award
(2014), ACM MSWIM Reginald A. Fessenden Award (2017), the New South Wales
Premier’s Prize of Excellence in Engineering and Information and
Communications Technology (2019), and the Research Innovation Award, IEEE
Technical Committee on Cloud Computing (2021).
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