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Energy efficiency of VM consolidation in IaaS clouds

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Abstract

The energy efficiency of cloud computing has recently attracted a great deal of attention. As a result of raised expectations, cloud providers such as Amazon and Microsoft have started to deploy a new IaaS service, a MapReduce-style virtual cluster, to process data-intensive workloads. Considering that the IaaS provider supports multiple pricing options, we study batch-oriented consolidation and online placement for reserved virtual machines (VMs) and on-demand VMs, respectively. For batch cases, we propose a DVFS-based heuristic TRP-FS to consolidate virtual clusters on physical servers to save energy while guarantee job SLAs. We prove the most efficient frequency that minimizes the energy consumption, and the upper bound of energy saving through DVFS techniques. More interestingly, this frequency only depends on the type of processor. FS can also be used in combination with other consolidation algorithms. For online cases, a time-balancing heuristic OTB is designed for on-demand placement, which can reduce the mode switching by means of balancing server duration and utilization. The experimental results both in simulation and using the Hadoop testbed show that our approach achieves greater energy savings than existing algorithms.

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References

  1. Akhter N, Othman M (2016) Energy aware resource allocation of cloud data center: review and open issues. Clust Comput 26(1):1–20

    Google Scholar 

  2. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gen Comput Syst 28(5):755–768

    Article  Google Scholar 

  3. Berral JL, Goiri Í, Nou R, Julià F, Guitart J, Gavaldà R, Torres J (2010) Towards energy-aware scheduling in data centers using machine learning. In: IEEE Proceedings of the International Conference on Energy-Efficient Computing and Networking, pp 215–224

  4. Cardosa M, Singh A, Pucha H, Chandra A (2012) Exploiting spatio-temporal tradeoffs for energy-aware mapreduce in the cloud. IEEE Trans Comput 61(12):1737–1751

    Article  MathSciNet  Google Scholar 

  5. Carlson TE, Heirman W, Eyerman S, Hur I, Eeckhout L (2014) An evaluation of high-level mechanistic core models. ACM Trans Archit Code Optim 11(3):1–25

    Article  Google Scholar 

  6. Chen Y, Alspaugh S, Borthakur D, Katz R (2012) Energy efficiency for large-scale mapreduce workloads with significant interactive analysis. In: ACM Proceedings of the European Conference on computer systems (EuroSys), pp 43–56

  7. Chen Y, Das A, Qin W, Sivasubramaniam A, Wang Q, Gautam N (2005) Managing server energy and operational costs in hosting centers. In: ACM Proceedings of the International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pp 303–314

  8. Chen Y, Ganapathi A, Katz RH (2010) To compress or not to compress-compute vs. io tradeoffs for mapreduce energy efficiency. In: ACM Proceedings of ACM SIGCOMM workshop on green networking, pp 23–28

  9. Deng Q, Meisner D, Ramos L, Wenisch TF, Bianchini R (2011) Memscale: active low-power modes for main memory. ACM SIGARCH Comput Archit News 39(1):225–238

    Article  Google Scholar 

  10. Ghamkhari M, Mohsenian-Rad H (2013) Energy and performance management of green data centers: a profit maximization approach. IEEE Trans Smart Grid 4(2):1017–1025

    Article  Google Scholar 

  11. Goiri I, Julia F, Nou R, Berral JL, Guitart J, Torres J (2010) Energy-aware scheduling in virtualized datacenters. In: IEEE Proceedings of IEEE International Conference on Cluster Computing (CLUSTER), pp 58–67

  12. Govindan S, Choi J, Urgaonkar B, Sivasubramaniam A, Baldini A (2009) Statistical profiling-based techniques for effective power provisioning in data centers. In: ACM Proceedings of European Conference on Computer Systems (EuroSys), pp 317–330

  13. Guenter B, Jain N, Williams C (2011) Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In: IEEE INFOCOM, pp 1332–1340

  14. Jiang J, Feng Y, Zhao J, Li K (2016) Dataabc: a fast abc based energy-efficient live vm consolidation policy with data-intensive energy evaluation model. Future Gen Comput Syst 87(5):1–33

    Google Scholar 

  15. Kaur T, Chana I (2015) Energy efficiency techniques in cloud computing: a survey and taxonomy. ACM Comput Surv 48(2):1–46

    Article  Google Scholar 

  16. Kaushik RT, Bhandarkar M (2010) Greenhdfs: towards an energy-conserving, storage-efficient, hybrid hadoop compute cluster. In: Proceedings of the International Conference on Power Aware Computing and Systems (HotPower), USENIX Association, pp 1–9

  17. Khosravi A, Garg SK, Buyya R (2013) Energy and carbon-efficient placement of virtual machines in distributed cloud data centers. In: Proceedings of the 19th International Conference on Parallel Processing, pp 317–328

  18. Lang W, Patel JM (2010) Energy management for mapreduce clusters. VLDB Endow 3(1):129–139

    Article  Google Scholar 

  19. Lee YC, Zomaya AY (2012) Energy efficient utilization of resources in cloud computing systems. J Supercomput 60(2):268–280

    Article  Google Scholar 

  20. Leverich J, Kozyrakis C (2010) On the energy inefficiency of hadoop clusters. ACM SIGOPS Oper Syst Rev 44(1):61–65

    Article  Google Scholar 

  21. Li P, Guo S, Yu S, Zhuang W (2015) Cross-cloud mapreduce for big data. IEEE Trans Cloud Comput 26(3):1–14

    Google Scholar 

  22. Li S, Ahn JH, Strong RD, Brockman JB, Tullsen DM, Jouppi NP (2009) Mcpat: an integrated power, area, and timing modeling framework for multicore and manycore architectures. In: Proceedings of the 42nd Annual Symposium on Microarchitecture, pp 469–480

  23. Luo J-P, Xia L, Chen M-R (2014) Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers. Expert Syst Appl 4(2):1–13

    Google Scholar 

  24. Mann ZA (2015a) Allocation of virtual machines in cloud data centersa survey of problem models and optimization algorithms. ACM Comput Surv 48(1):1–34

    Article  Google Scholar 

  25. Mann ZA (2015b) Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center. Future Gen Comput Syst 51(4):1–6

    Article  Google Scholar 

  26. Mashayekhy L, Nejad M, Grosu D, Zhang Q, Shi W (2015) Energy-aware scheduling of mapreduce jobs for big data applications. IEEE Trans Parallel Distrib Syst 26(10):2720–2733

    Article  Google Scholar 

  27. Meng X, Pappas V, Zhang L (2010) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: IEEE Proceedings of INFOCOM, pp 1–9

  28. Quang-Hung N, Thoai N, Son NT (2013) Epobf: energy efficient allocation of virtual machines in high performance computing cloud. J Sci Technol 51(4):173–182

    Google Scholar 

  29. Ribas BC, Suguimoto RM, Montaño RANR, Silva F, de Bona L, Castilho MA (2012) On modelling virtual machine consolidation to pseudo-boolean constraints. Springer, Heidelberg

    Book  Google Scholar 

  30. Sindelar M, Sitaraman RK, Shenoy P (2011) Sharing-aware algorithms for virtual machine colocation. In: ACM Proceedings of the 23th Annual ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), pp 367–378

  31. Song W, Xiao Z, Chen Q, Luo H (2013) Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans Comput 63(11):2647–2660

    Article  MathSciNet  Google Scholar 

  32. Tang M, Pan S (2014) A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process Lett 1(5):1–11

    Google Scholar 

  33. Urgaonkar R, Kozat U, Igarashi K, Neely M (2010) Dynamic resource allocation and power management in virtualized data centers. In: IEEE Proceedings of Network Operations and Management Symposium (NOMS), pp 479–486

  34. Van HN, Tran FD, Menaud J-M (2010) Performance and power management for cloud infrastructures. In: IEEE Proceedings of the 3rd International Conference on Cloud Computing (CLOUD), pp 329–336

  35. Verma A, Ahuja P, Neogi A (2008) pmapper: power and migration cost aware application placement in virtualized systems. In: Middleware. Springer, pp 243–264

  36. Verma A, Cherkasova L, Campbell RH (2011) Aria: automatic resource inference and allocation for mapreduce environments. In: Proceedings of the International Conference on Autonomic Computing (ICAC), pp 249–256

  37. Von Laszewski G, Wang L, Younge AJ, He X (2009) Power-aware scheduling of virtual machines in dvfs-enabled clusters. In: Proceedings of the IEEE International Conference on Cluster Computing (Clusters), pp 1–10

  38. Wang L, Zhang F, Zheng K, Vasilakos AV, Ren S, Liu Z (2014) Energy-efficient flow scheduling and routing with hard deadlines in data center networks. In: IEEE Proceedings of IEEE 34th International Conference on Distributed Computing Systems (ICDCS), pp 1–11

  39. Wirtz T, Ge R (2011) Improving mapreduce energy efficiency for computation intensive workloads. In: IEEE Proceedings of the International Green Computing Conference and Workshops (IGCC), pp 1–8

  40. Wong D, Annavaram M (2014) Implications of high energy proportional servers on cluster-wide energy proportionality. In: IEEE Proceedings of the 20th International Symposium on High Performance Computer Architecture (HPCA), pp 142–153

  41. Wu Y, Tang M, Fraser W (2012) A simulated annealing algorithm for energy efficient virtual machine placement. In: IEEE Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1245–1250

  42. Xie R, Jia X, Yang K, Zhang B (2013) Energy saving virtual machine allocation in cloud computing. In: IEEE Proceedings of the 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW), pp 132–137

  43. Zhou Z, Liu F, Jin H, Li B, Li B, Jiang H (2013) On arbitrating the power-performance tradeoff in saas clouds. In: IEEE Proceedings of INFOCOM, pp 872–880

  44. Zhuo J, Chakrabarti C (2008) Energy-efficient dynamic task scheduling algorithms for dvs systems. ACM Trans Embed Comput Syst 7(2):421–434

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Fei Teng, Tianrui Li & Danting Deng

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Fei Teng

  3. Sino-French Engineering School, Beihang University, Beijing, 100191, China

    Lei Yu

  4. Ecole Centrale Paris, Grande Voie des Vignes, 92295, Châtenay-Malabry, France

    Frédéric Magoulès

Authors
  1. Fei Teng
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  2. Lei Yu
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  3. Tianrui Li
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  4. Danting Deng
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  5. Frédéric Magoulès
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Corresponding author

Correspondence to Lei Yu.

Additional information

This work was partially supported by the National Natural Science Foundation of China (No. 61202043), the Soft Science Foundation of Sichuan Province (No. 2016ZR0034), the Open Research Fund of Key Laboratory of Network Intelligent Information Processing (No. SZJJ2014-049) and the State Key Laboratory of Software Development Environment of P. R. China under Grant (No. SKLSDE-2016ZX-25).

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Teng, F., Yu, L., Li, T. et al. Energy efficiency of VM consolidation in IaaS clouds. J Supercomput 73, 782–809 (2017). https://doi.org/10.1007/s11227-016-1797-5

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  • DOI: https://doi.org/10.1007/s11227-016-1797-5

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