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Load dispersion-aware VM placement in favor of energy-performance tradeoff

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Abstract

Huge energy consumption in cloud infrastructure has turned into a challenging problem. Virtualization technology, which can be regarded as the first step in energy conservation by offering benefits like on-demand resource provisioning and live migration, creates a platform on which different resource allocation and scheduling policies can be defined on how to accumulate VMs on fewer number of hosts while respecting performance metrics. In this paper, after presenting a classification on VM placement strategies, we propose different combinatorial placement policies that take load dispersion of hosts into account to dynamically adapt their placement decisions. Simulated experiments through Cloudsim showed noteworthy results concerning energy-performance tradeoff.

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References

  1. Orgerie AC, Assuncao MDD, Lefevre L (2014) A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput Surv (CSUR) 46(4):47

    Article  Google Scholar 

  2. Corradi A, Fanelli M, Foschini L (2013) Management infrastructures for power-efficient cloud computing architectures. Cloud computing. Springer, London, pp 133–152

    Chapter  Google Scholar 

  3. Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420

    Article  Google Scholar 

  4. Jing SY, Ali S, She K, Zhong Y (2013) State-of-the-art research study for green cloud computing. J Supercomput 65(1):445–468

    Article  Google Scholar 

  5. Mann Z (2015) Allocation of virtual machines in cloud data centers: a survey of problem models and optimization algorithms. ACM Comput Surv (CSUR) 48(1):11

    Article  Google Scholar 

  6. Kaur T, Chana I (2015) Energy efficiency techniques in cloud computing: a survey and taxonomy. ACM Comput Surv (CSUR) 48(2):22

    Article  Google Scholar 

  7. Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson JM, Vasilakos AV (2015) Cloud computing: survey on energy efficiency. ACM Comput Surv (CSUR) 47(2):33

    Google Scholar 

  8. Beloglazov A, Buyya R, Lee YC, Zomaya A (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv Comput 82(2):47–111

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Shaw SB, Singh AK (2015) Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center. Comput Electr Eng 47:241–254

    Article  Google Scholar 

  11. Mastroianni C, Meo M, Papuzzo G (2013) Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans Cloud Comput 1(2):215–228

    Article  Google Scholar 

  12. Yang CT, Liu JC, Huang KL, Jiang FC (2014) A method for managing green power of a virtual machine cluster in cloud. Future Gener Comput Syst 37:26–36

    Article  Google Scholar 

  13. Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H (2015) Using ant colony system to consolidate vms for green cloud computing. IEEE Trans Serv Comput 8(2):187–198

    Article  Google Scholar 

  14. Nadjar A, Abrishami S, Deldari H (2015). Hierarchical VM scheduling to improve energy and performance efficiency in IaaS Cloud data centers. In: 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, pp 131–136

  15. Goiri í, Berral JL, Fit JO, Juli F, Nou R, Guitart J, Torres J (2012) Energy-efficient andmultifaceted resource management for profit-driven virtualized datacenters. Future Gener Comput Syst 28(5):718–731

    Article  Google Scholar 

  16. Zaman S, Grosu D (2013) A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds. IEEE Trans Cloud Comput 1(2):129–141

    Article  Google Scholar 

  17. Zhao L, Lu L, Jin Z, Yu C (2015) Online virtual machine placement for increasing cloud providers revenue. IEEE Trans Serv Comput. doi:10.1109/TSC.2015.2447550

  18. Wood T, Tarasuk-Levin G, Shenoy P, Desnoyers P, Cecchet E, Corner MD (2009) Memory buddies: exploiting page sharing for smart colocation in virtualized data centers. In: Proceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments. ACM, pp 31–40

  19. Takouna I, Meinel C (2014). Coordinating VMs’ memory demand heterogeneity and memory DVFS for energy-efficient VMs consolidation. In: Internet of Things (iThings), 2014 IEEE International Conference on, and Green Computing and Communications (GreenCom), IEEE and Cyber, Physical and Social Computing (CPSCom). IEEE, pp 478–485

  20. Ye K, Wu Z, Wang C, Zhou BB, Si W, Jiang X, Zomaya AY (2015) Profiling-based workload consolidation and migration in virtualized data centers. IEEE Trans Parallel Distrib Syst 26(3):878–890

    Article  Google Scholar 

  21. Verboven S, Vanmechelen K, Broeckhove J (2013) Black box scheduling for resource intensive virtual machine workloads with interference models. Future Gener Comput Syst 29(8):1871–1884

    Article  Google Scholar 

  22. Meng X, Isci C, Kephart J, Zhang L, Bouillet E, Pendarakis D (2010) Efficient resource provisioning in compute clouds via vm multiplexing. In: Proceedings of the 7th International Conference on Autonomic Computing. ACM, pp 11–20

  23. Li X, Ventresque A, Iglesias JO, Murphy J (2015) Scalable correlation-aware virtual machine consolidation using two-phase clustering. In: 2015 International Conference on High Performance Computing and Simulation (HPCS). IEEE, pp 237–245

  24. Kuang W, Brown LE, Wang Z (2015) Modeling cross-architecture co-tenancy performance interference. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, pp 231–240

  25. Wang JV, Cheng CT, Chi KT (2015) A power and thermal-aware virtual machine allocation mechanism for Cloud data centers. In: 2015 IEEE International Conference on Communication Workshop (ICCW). IEEE, pp 2850–2855

  26. Mhedheb Y, Jrad F, Tao J, Zhao J, Koodziej J, Streit A (2013) Load and thermal-aware VM scheduling on the cloud. In: International Conference on Algorithms and Architectures for Parallel Processing. Springer International Publishing, pp 101–114

  27. Chaudhry MT, Ling TC, Manzoor A, Hussain SA, Kim J (2015) Thermal-aware scheduling in green data centers. ACM Comput Surv (CSUR) 47(3):39

    Article  Google Scholar 

  28. Zhang Q, Riska A, Sun W, Smirni E, Ciardo G (2005) Workload-aware load balancing for clustered web servers. IEEE Trans Parallel Distrib Syst 16(3):219–233

    Article  Google Scholar 

  29. Tai J, Li Z, Chen J, Mi N (2014) Load balancing for cluster systems under heavy-tailed and temporal dependent workloads. Simul Model Pract Theory 44:63–77

    Article  Google Scholar 

  30. Xiao Z, Song W, Chen Q (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117

    Article  Google Scholar 

  31. Li X, Qian Z, Lu S, Wu J (2013) Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Math Comput Model 58(5):1222–1235

    Article  MathSciNet  Google Scholar 

  32. Rao KS, Thilagam PS (2015) Heuristics based server consolidation with residual resource defragmentation in cloud data centers. Future Gener Comput Syst 50:87–98

    Article  Google Scholar 

  33. Chen J, Chiew K, Ye D, Zhu L, Chen W (2013) Aaga: affinity-aware grouping for allocation of virtual machines. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA). IEEE, pp 235–242

  34. Sonnek J, Greensky J, Reutiman R, Chandra A (2010) Starling: minimizing communication overhead in virtualized computing platforms using decentralized affinity-aware migration. In: 2010 39th International Conference on Parallel Processing. IEEE, pp 228–237

  35. Zhang B, Qian Z, Huang W, Li X, Lu S (2012) Minimizing communication traffic in data centers with power-aware vm placement. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, pp 280–285

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

  37. Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2015) Network-aware virtual machine placement and migration in cloud data centers. In: Bagchi S (ed) Emerging research in cloud distributed computing systems, Chap 2. Information Science Reference, Hershey PA, pp 42–91

  38. Park K, Pai VS (2006) CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Op Syst Rev 40(1):65–74

    Article  Google Scholar 

  39. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50

    Article  Google Scholar 

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Correspondence to Saeid Abrishami.

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Nadjar, A., Abrishami, S. & Deldari, H. Load dispersion-aware VM placement in favor of energy-performance tradeoff. J Supercomput 73, 1547–1566 (2017). https://doi.org/10.1007/s11227-016-1842-4

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