Advertisement

Computing

pp 1–20 | Cite as

Nash equilibrium based replacement of virtual machines for efficient utilization of cloud data centers

  • Hammad ur Rehman QaiserEmail author
  • Gao Shu
Article
  • 35 Downloads

Abstract

Workload uncertainty has been increased with the integration of the Internet of Things to the computing grid i.e. edge computing and cloud data centers. Therefore, efficient resource utilization in cloud data centers become more challenging. Dynamic consolidation of virtual machines on optimal number of processing machines can increase the efficiency of resource utilization in cloud data centers. This process requires the migration of virtual machines from the under-utilized and over-utilized processing machines to other suitable machines. In this work, the problem of efficient replacement of virtual machines is solved using a game theory based well known technique, Nash Equilibrium (NE). We designed a nash equilibrium based dual on two players, over-load manager and under-load manager, to deduce the dominant strategy profiles for various scenarios during consolidation cycles. Dominant strategy profile is the set of strategies where every player has no incentive in deviation, thus leading to equilibrium position. A virtual machines redeployment algorithm, Nash Equilibrium based Virtual Machines Replacement (NE-VMR), has been proposed on the basis of the dominant strategy profiles for efficient consolidation. Experiment results show that NE-VMR is a more efficient server consolidation technique, saved 30% energy and improved 35% quality of service as compared to baselines.

Keywords

Virtual machine consolidation Virtual machine replacement policies Energy efficient computing Cloud computing Efficient resource management system 

Mathematics Subject Classification

68-02 

Notes

References

  1. 1.
    Pan J, McElhannon J (2017) Future edge cloud and edge computing for internet of things applications. IEEE Internet Things J 5(1):439–449CrossRefGoogle Scholar
  2. 2.
    Khosravi A, Rajkumar B (2018) Energy and carbon footprint-aware management of geo-distributed cloud data centers: a taxonomy, state of the art. In: Advancing cloud database systems and capacity planning with dynamic applications. IGI Global, pp 1456–1475Google Scholar
  3. 3.
    Kaushal S, Gogia D, Kumar B (2019) Recent trends in green cloud computing. In: International conference on communication computing and networking, pp 947–956Google Scholar
  4. 4.
    Barroso LA, Holzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37CrossRefGoogle Scholar
  5. 5.
    Sun X, Hu C, Yang R, Garraghan P, Wo T, Xu J, Zhu J, Li C (2018) ROSE: cluster resource scheduling via speculative over-subscription. In: IEEE 38th international conference on distributed computing systems (ICDCS), pp 949–960Google Scholar
  6. 6.
    Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr Comput Pract Exp 29(12):e4123CrossRefGoogle Scholar
  7. 7.
    Wu S, Garg K, Buyya R (2015) Service level agreement (SLA) based SaaS cloud management system. In: International conference on parallel and distributed systems (ICPADS), pp 440–447Google Scholar
  8. 8.
    Ismail L, Materwala H (2018) Energy-aware VM placement and task scheduling in cloud-IoT computing: classification and performance evaluation. IEEE Internet Things J 5(6):5166–5176CrossRefGoogle Scholar
  9. 9.
    Ullah A, Li J, Shen Y, Hussain A (2018) A control theoretical view of cloud elasticity: taxonomy, survey and challenges. Clust Comput 21(4):1735–1764CrossRefGoogle Scholar
  10. 10.
    De-Assuncao MD, da-Silva VA, Buyya R (2018) Distributed data stream processing and edge computing: a survey on resource elasticity and future directions. J Netw Comput Appl 103:1–17CrossRefGoogle Scholar
  11. 11.
    Khattar N, Sidhu J, Singh J (2019) Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques. J Supercomput 75:4750–4810CrossRefGoogle Scholar
  12. 12.
    Mehta JS (2017) Concept drift in streaming data classification: algorithms. Platf Issues Procedia Comput Sci 122:804–811CrossRefGoogle Scholar
  13. 13.
    Kratzke N (2018) A brief history of cloud application architectures. Appl Sci 8(8):1368CrossRefGoogle Scholar
  14. 14.
    Liang X, yan Z (2019) A survey on game thearatic methods in human-machine networks. Fut Gener Comput Syst 92:674–693CrossRefGoogle Scholar
  15. 15.
    Khan MA, Paplinski A, Khan AM, Murshed M, Buyya R (2018) Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: a review. Sustain Cloud Energy Serv, pp 135–654 (Chapter of a book) Google Scholar
  16. 16.
    Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. FGCS 28(5):755–768CrossRefGoogle Scholar
  17. 17.
    Beloglazov A, Buyya R (2016) Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the 8th international workshop on middleware for grids, clouds and e-science, pp 1–6Google Scholar
  18. 18.
    Abadi RM, Rahmani AM, Alizadeh SH (2018) Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in cloud computing. Clust Comput 21(3):1711–1733CrossRefGoogle Scholar
  19. 19.
    Farahnakian F, Pahikkala T, Liljeberg P, Plosila J, Hieu NT, Tenhunen H (2019) Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans Cloud Comput 7(2):524–536CrossRefGoogle Scholar
  20. 20.
    Farahnakian F, Pahikkala T, Liljeberg P, Plosila J (2013) Energy aware consolidation algorithm based on k-nearest neighbor regression for cloud data centers. In: 6th international conference on utility and cloud computing, pp 256–259Google Scholar
  21. 21.
    Li Z (2019) An adaptive overload threshold selection process using Markov decision processes of virtual machine in cloud data center. Clust Comput 22(2):3821–3833CrossRefGoogle Scholar
  22. 22.
    Chiang ML, Huang YF, Hsieh HC, Tsai WC (2018) Highly reliable and efficient three-layer cloud dispatching architecture in the heterogeneous cloud computing environment. Appl Sci 8(8):1385CrossRefGoogle Scholar
  23. 23.
    Chen T, Zhu Y, Gao X, Kong L, Chen G, Wang Y (2018) Improving resource utilization via virtual machine placement in data center networks. Mob Netw Appl 23(2):227–238CrossRefGoogle Scholar
  24. 24.
    Abbasi A, Jin H (2018) v-Mapper: an application-aware resource consolidation scheme for cloud data centers. Future Internet 10(9):90CrossRefGoogle Scholar
  25. 25.
    Guo W, Xu T, Tang K, Yu J, Chen S (2018) Online sequential extreme learning machine with generalized regulation and adaptive forgetting factor for time-varying system prediction. Math Probl Eng 2018:1–22zbMATHGoogle Scholar
  26. 26.
    Marotta A, Avallone S (2015) A simulated annealing based approach for power efficient virtual machines consolidation. In: Cloud computing (CLOUD), pp 445–452Google Scholar
  27. 27.
    Fatima A, Javaid N, Anjum AB, Sultana T, Hussain W, Bilal M, Akbar M, Ilahi M (2019) An enhanced multi-objective gray wolf optimization for virtual machine placement in cloud data centers. Electronics 8(2):218CrossRefGoogle Scholar
  28. 28.
    Zheng Q, Li J, Dong B, Li R, Shah N, Tian F (2015) Multi-objective optimization algorithm based on bbo for virtual machine consolidation problem. In: International conference on parallel and distributed systems, pp 414–421Google Scholar
  29. 29.
    Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H (2014) Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans Serv Comput 8(2):187–98CrossRefGoogle Scholar
  30. 30.
    Li H, Zhu G, Cui C (2016) Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3):303–317MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Ye D, Chen J (2013) Non-cooperative games on multidimensional resource allocation. Future Gener Comput Syst 29:1345–1352CrossRefGoogle Scholar
  32. 32.
    Ardagna D, Panicucci B, Passacantando M (2015) Generalized nash equilibria for the service provisioning problem in cloud systems. IEEE Trans Serv Comput 10(3):381–395CrossRefGoogle Scholar
  33. 33.
    Gokulnath K, Uthariaraj R (2015) Game theory based trust model for cloud environment. Sci World J 2015:1–10CrossRefGoogle Scholar
  34. 34.
    Nezarat A, Dastghaibifard GH (2015) Efficient nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction. PLoS ONE 10(10):e0138424CrossRefGoogle Scholar
  35. 35.
    Han K, Cai X, Rong H (2015) An evolutionary game theoretic approach for efficient virtual machine deployment in green cloud. In: International conference on computer science and mechanical automation, pp 1–4Google Scholar
  36. 36.
    Li Z, Yu X, Zhao L (2019) A strategy game system for QoS-efficient dynamic virtual machine consolidation in data centers. IEEE Access 7:104315–104329CrossRefGoogle Scholar
  37. 37.
    Rockafellar RT (2018) Variational analysis of nash equilibrium. Vietnam J Math 46(1):73–85MathSciNetzbMATHCrossRefGoogle Scholar
  38. 38.
    Rubinstein A (2016) Settling the complexity of computing approximate of two-player Nash equilibria. In: 57th annual symposium on foundations of computer science (FOCS), pp. 258–265Google Scholar
  39. 39.
    Calheiros R, Ranjan R, Beloglazov A, De-Rose C, Buyya R (2011) cloudsim: a toolkit for modeling and simulation of cloud computingenvironments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50CrossRefGoogle Scholar
  40. 40.
    Barbierato E, Gribaudo M, Iacono M, Jakóbik A (2019) Exploiting cloudsim in a multiformalism modeling approach for cloud based systems. Simul Model Pract Theory 93:133–147CrossRefGoogle Scholar
  41. 41.
    Abro JH, Li C, Qaiser HR (2019) Adaptive threshold detection based on current demand for efficient resource utilization of cloud resources. Int Conf Comput Commun Syst 1(1):341–346Google Scholar
  42. 42.
    Qaiser H, Shu G (2018) Efficient VM selection heuristics for dynamic VM consolidation in cloud data centers. In: International conference on parallel and distributed processing with applications, pp 832–839Google Scholar
  43. 43.
    Zhou Z, Zhigang H, Keqin L (2016) Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers. Sci Program 2016:1–15Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Wuhan University of TechnologyYujiatou, WuhanChina

Personalised recommendations