Skip to main content
SpringerLink
Log in

Comprehensive Study on Machine Learning-Based Container Scheduling in Cloud

  • Conference paper
  • First Online:
The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022) (AMLTA 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 113))

Abstract

Containers are considered the best lightweight application in virtualization technology, and it is promising in enhancing cloud computing services quality. Due to cloud workload diversity, the scheduler module is considered the central part of the containers framework that optimizes resource utilization and reduces cost and energy consumption. Container scheduling algorithms can be classified into four main types: heuristic, metaheuristic, mathematical modeling, and machine learning. Machine Learning, with its high ability to analyze data and train the system to predict outputs based on previous data considered the best choice for predicting workloads and performance metrics. Such a vision allows schedulers to improve the quality of resource allocation with changing user requests rates in complicated work environments. This paper presents a comprehensive literature review for the current container orchestration machine learning-based algorithms. A detailed study is proposed for the main features, advantages, and disadvantages of existing algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Varghese, B., Buyya, R.: Next generation cloud computing: new trends and research directions. Future Gener. Comput. Syst. 79(3), 849–861 (2018)

    Google Scholar 

  2. Saber, W., Moussa, W., Ghuniem, A.M., Rizk, R.Y.: Hybrid load balance based on genetic algorithm in cloud environment. Int. J. Electr. Comput. Eng. (IJECE) 11(3), 2477–2489 (2020)

    Article  Google Scholar 

  3. da Cunha, H.G.V.O., Moreira, R., de Oliveira, F.: A comparative study between containerization and full-virtualization of virtualized everything functions in edge computing. In: Advanced Information Networking and Applications, pp. 771–782. AINA (2021)

    Google Scholar 

  4. Shah, J., Dubaria, D.: Building modern clouds: using Docker, Kubernetes & Google cloud platform. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, pp. 184–189 (2019)

    Google Scholar 

  5. Menouer, T., Cérin, C., Leclercq, É.: New multi-objectives scheduling strategies in Docker SwarmKit. In: Vaidya, J., Li, J. (eds.) ICA3PP 2018. LNCS, vol. 11336, pp. 103–117. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05057-3_8

    Chapter  Google Scholar 

  6. Kaushik, P., Raghavendra, S., Govindaraju, M., Tiwari, D.: Exploring the potential of using power as a first class parameter for resource allocation in apache mesos managed clouds. In: 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), Leicester, pp. 216–226. IEEE (2020)

    Google Scholar 

  7. Ahmad, I., AlFailakawi, M.Gh., AlMutawa, A., Alsalman, L.: Container scheduling techniques: a survey and assessment. J. King Saud Univ. Comput. Inf. Sci. (2021)

    Google Scholar 

  8. Gamal, M., Rizk, R., Mahdi, H., Elnaghi, B.E.: Osmotic bio-inspired load balancing algorithm in cloud computing. IEEE Access 7, 42735–42744 (2019)

    Article  Google Scholar 

  9. Attia, R., Hassaan, A., Rizk, R.: Advanced greedy hybrid bio-inspired routing protocol to improve IoV. IEEE Access 9, 131260–131272 (2021)

    Article  Google Scholar 

  10. Mohamed, A., Saber, W., Elnahry, I., Hassanien, A.E.: Coyote optimization based on a fuzzy logic algorithm for energy-efficiency in wireless sensor networks. IEEE Access 8, 185816–185829 (2020)

    Article  Google Scholar 

  11. Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 51(5), 1–36 (2018)

    Google Scholar 

  12. Bentaleb, O., Belloum, A.S.Z., Sebaa, A.: Containerization technologies: taxonomies, applications and challenges. J. Supercomput. 78, 1144–1181 (2021)

    Google Scholar 

  13. Goodarzy, S., Nazari, M., Han, R., Keller, E., Rozner, E.: Resource management in cloud computing using machine learning: a survey. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, pp. 811–816 (2020)

    Google Scholar 

  14. Bianchini, R., et al.: Toward ML-centric cloud platforms. Commun. ACM 63(2), 50–59 (2020)

    Article  Google Scholar 

  15. Kecskemeti, G., Marosi, A.C., Kertesz, A.: The ENTICE approach to decompose monolithic services into microservices. In: 2016 International Conference on High Performance Computing Simulation (HPCS), pp. 591–596, Innsbruck (2016)

    Google Scholar 

  16. Lv, J., Wei, M., Yu, Y.: A container scheduling strategy based on machine learning in microservice architecture. In: 2019 IEEE International Conference on Services Computing (SCC), Milan, pp. 65–71 (2019)

    Google Scholar 

  17. Rovnyagin, M.M., Dmitriev, S.O., Hrapov, A.S., Kozlov, V.K.: Algorithm of ML-based re-scheduler for container orchestration system. In: 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), Moscow, pp. 613–617 (2021)

    Google Scholar 

  18. Chiang, R.C.: Contention-aware container placement strategy for docker swarm with machine learning based clustering algorithms. Clust. Comput. (2020)

    Google Scholar 

  19. Nath, S.B., Addya, S.K., Chakraborty, S., Ghosh, S.K.: Green containerized service consolidation in cloud. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin , pp.1–6 (2020)

    Google Scholar 

  20. Mehta, H.K., Harvey, P., Rana, O., Buyya R., Varghese, B.: WattsApp: power-aware container scheduling. In: 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), Leicester, pp. 79–90 (2020)

    Google Scholar 

  21. Liu, J., Wang, S., Zhou, A., Xu, J., Yang, F.: SLA-driven container consolidation with usage prediction for green cloud computing. Front. Comp. Sci. 14(1), 42–52 (2019). https://doi.org/10.1007/s11704-018-7172-3

    Article  Google Scholar 

  22. Kim, S., Kim, Y.: Co-scheML: interference-aware container co-scheduling scheme using machine learning application profiles for GPU clusters. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), Kobe, pp. 104–108 (2020)

    Google Scholar 

  23. Imdoukh, M., Ahmad, I., Alfailakawi, M.G.: Machine learning-based auto-scaling for containerized applications. Neural Comput. Appl. 32, 9745–9760 (2020)

    Google Scholar 

  24. Lorido-Botran, T., Bhatti, M.K.: Adaptive container scheduling in cloud data centers: a deep reinforcement learning approach. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 227, pp. 572–581. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75078-7_57

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Port Said University, Port Said, 42526, Egypt

    Walid Moussa, Mona Nashaat, Walaa Saber & Rawya Rizk

Authors
  1. Walid Moussa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mona Nashaat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Walaa Saber
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rawya Rizk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Walid Moussa .

Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Port Said University, Port Fouad, Egypt

    Rawya Y. Rizk

  3. Department of Computer Science, VÅ B-TUO, Ostrava-Poruba, Czech Republic

    Václav Snášel

  4. Faculty of Engineering, Port Said University, Port Fouad, Egypt

    Rehab F. Abdel-Kader

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Moussa, W., Nashaat, M., Saber, W., Rizk, R. (2022). Comprehensive Study on Machine Learning-Based Container Scheduling in Cloud. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_48

Download citation

Publish with us

Policies and ethics

Search

Navigation