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On the classification and quantification of server consolidation overheads

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Abstract

Virtualization technology is important for servers that compose cloud data centers. The current trend is to consolidate servers to manage them easily and reduce hardware and power consumption in data centers. However, performance degradation is inherent to virtualization technology and is caused by the hypervisor and overhead due to the consolidation of several virtual servers inside a physical server. Several ways exist to virtualize a server; these methods are based mainly on virtual machines and containers. In this paper, we propose a general method to estimate the value of the consolidation overhead classes, regardless of the virtualization platform, server characteristics and workload type. We conducted several experiments in different scenarios to illustrate the usefulness of the proposed method. The results show the applicability of the proposed method and indicate that these inherent overheads are not negligible in many cases depending on, first, the type of hypervisor and, second, the hardware resources features of the physical server.

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References

  1. Agarwal K, Jain B, Porter DE (2015) Containing the hype. In: Proceedings of the 6th Asia-Pacific Workshop on Systems. ACM, p 8

  2. Barik RK, Lenka RK, Rao KR, Ghose D (2016) Performance analysis of virtual machines and containers in cloud computing. In: 2016 International Conference on Computing, Communication and Automation (ICCCA). IEEE, pp 1204–1210

  3. Bermejo B, Juiz C (2020) Virtual machine consolidation: a systematic review of its overhead influencing factors. J Supercomput 76:324–361. https://doi.org/10.1007/s11227-019-03025-y

    Article  Google Scholar 

  4. Bermejo B, Juiz C, Guerrero C (2019) Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance. J Supercomput 75(2):808–836

    Article  Google Scholar 

  5. Buyya R, Vecchiola C, Selvi ST (2013) Mastering cloud computing: foundations and applications programming. Newnes, Oxford

    Google Scholar 

  6. Calzarossa MC, Della Vedova ML, Massari L, Petcu D, Tabash MI, Tessera D (2016) Workloads in the clouds. In: Principles of performance and reliability modeling and evaluation. Springer, Cham, pp 525–550

    Chapter  Google Scholar 

  7. Casalicchio E (2019) A study on performance measures for auto-scaling cpu-intensive containerized applications. Clust Comput 22:995–1006. https://doi.org/10.1007/s10586-018-02890-1

    Article  Google Scholar 

  8. Celesti A, Mulfari D, Galletta A, Fazio M, Carnevale L, Villari M (2019) A study on container virtualization for guarantee quality of service in cloud-of-things. Future Gener Comput Syst 99:356–364

    Article  Google Scholar 

  9. Cherkasova L, Gardner R (2005) Measuring CPU overhead for I/O processing in the Xen virtual machine monitor. In: USENIX Annual Technical Conference, General Track, vol 50

  10. Chung MT, Quang-Hung N, Nguyen MT, Thoai N (2016) Using docker in high performance computing applications. In: 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE). IEEE, pp 52–57

  11. Dua R, Raja AR, Kakadia D (2014) Virtualization vs containerization to support PaaS. In: 2014 IEEE International Conference on Cloud Engineering. IEEE, pp 610–614

  12. Felter W, Ferreira A, Rajamony R, Rubio J (2015) An updated performance comparison of virtual machines and linux containers. In: 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). IEEE, pp 171–172

  13. Graniszewski W, Arciszewski A (2016) Performance analysis of selected hypervisors (virtual machine monitors-VMMs). Int J Electron Telecommun 62(3):231–236

    Article  Google Scholar 

  14. Huber N, von Quast M, Hauck M, Kounev S (2011) Evaluating and modeling virtualization performance overhead for cloud environments. In: CLOSER, pp 563–573

  15. Hussein MK, Mousa MH, Alqarni MA (2019) A placement architecture for a container as a service (CaaS) in a cloud environment. J Cloud Comput 8(1):7

    Article  Google Scholar 

  16. Jha DN, Garg S, Jayaraman PP, Buyya R, Li Z, Ranjan R (2018) A holistic evaluation of docker containers for interfering microservices. In: 2018 IEEE International Conference on Services Computing (SCC). IEEE, pp 33–40

  17. Juiz C, Bermejo B (2020) The CiS2: a new metric for performance and energy trade-off in consolidated servers. Clust Comput. https://doi.org/10.1007/s10586-019-03043-8

    Article  Google Scholar 

  18. Kozhirbayev Z, Sinnott RO (2017) A performance comparison of container-based technologies for the cloud. Future Gener Comput Syst 68:175–182

    Article  Google Scholar 

  19. Li J, Wang Q, Jayasinghe D, Park J, Zhu T, Pu C (2013) Performance overhead among three hypervisors: an experimental study using hadoop benchmarks. In: 2013 IEEE International Congress on Big Data. IEEE, pp 9–16

  20. Mazumdar S, Pranzo M (2017) Power efficient server consolidation for cloud data center. Future Gener Comput Syst 70:4–16

    Article  Google Scholar 

  21. Menon A, Santos JR, Turner Y, Janakiraman GJ, Zwaenepoel W (2005) Diagnosing performance overheads in the Xen virtual machine environment. In: Proceedings of the 1st ACM/USENIX International Conference on Virtual Execution Environments. ACM, pp 13–23

  22. Molero X, Juiz C, Rodeño M (2004) Evaluación y modelado del rendimiento de los sistemas informáticos. Pearson Educación, London

    Google Scholar 

  23. Padala P, Zhu X, Wang Z, Singhal S, Shin KG et al (2007) Performance evaluation of virtualization technologies for server consolidation. HP Labs technical report, vol 137

  24. Plauth M, Feinbube L, Polze A (2017) A performance evaluation of lightweight approaches to virtualization. Cloud Comput 2017:14

    Google Scholar 

  25. Pousa D, Rufino J (2017) Evaluation of type-1 hypervisors on desktop-class virtualization hosts. IADIS J Comput Sci Inf Syst 12(2):86–101

    Google Scholar 

  26. Sandholm T, Lee D (2014) Notes on cloud computing principles. J Cloud Comput 3(1):21

    Article  Google Scholar 

  27. Scheepers MJ (2014) Virtualization and containerization of application infrastructure: a comparison. In: 21st Twente Student Conference on IT, vol 1, pp 1–7

  28. Sharma P, Chaufournier L, Shenoy P, Tay Y (2016) Containers and virtual machines at scale: a comparative study. In: Proceedings of the 17th International Middleware Conference. ACM, p 1

  29. Shetty J, Upadhaya S, Rajarajeshwari H, Shobha G, Chandra J (2017) An empirical performance evaluation of docker container, openstack virtual machine and bare metal server. Indones J Electr Eng Comput Sci 7(1):205–213

    Article  Google Scholar 

  30. Ward JS, Barker A (2014) Observing the clouds: a survey and taxonomy of cloud monitoring. J Cloud Comput 3(1):24

    Article  Google Scholar 

  31. Xavier MG, Neves MV, Rossi FD, Ferreto TC, Lange T, De Rose CA (2013) Performance evaluation of container-based virtualization for high performance computing environments. In: 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. IEEE, pp 233–240

  32. Zhang J, Lu X, Panda DK (2019) Performance characterization of hypervisor-and container-based virtualization for HPC on SR-IOV enabled infiniband clusters. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, pp 1777–1784

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  1. Computer Science Department, Balearic Islands University, E07122, Palma, Spain

    Belen Bermejo & Carlos Juiz

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  1. Belen Bermejo
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  2. Carlos Juiz
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Correspondence to Belen Bermejo.

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Bermejo, B., Juiz, C. On the classification and quantification of server consolidation overheads. J Supercomput 77, 23–43 (2021). https://doi.org/10.1007/s11227-020-03258-2

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  • DOI: https://doi.org/10.1007/s11227-020-03258-2

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