Abstract
Dynamic consolidation of virtual machines (VMs) is an effective technique, which can lead to improvement of energy efficiency and resource utilization in cloud data centers. However, due to varying workloads in applications, consolidating the virtual machines can cause a violation in Service Level Agreement. The main goal of the dynamic VM consolidation is to optimize the energy-performance trade-off. Detecting when a host is being overloaded or underloaded are two substantial sub-problems of dynamic VM consolidation, which directly affects the utilization of resources, Quality of Service, and energy efficiency as well. In this paper, an adaptive fuzzy threshold-based algorithm has been proposed to detect overloaded and under-loaded hosts. The proposed algorithm generates rules dynamically and updates membership functions to adapt to changes in workload. It is validated with real-world PlanetLab workload. Simulation results demonstrate that the proposed algorithm significantly outperforms the other competitive algorithms.
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Belady CL (2007) In the data center, power and cooling costs more than the it equipment it supports. Electron Cool 13:24
Brown R (2008) Report to congress on server and data center energy efficiency: Public law 109–431. Lawrence Berkeley National Laboratory
Gartner I (2007) Estimates, Industry Accounts for 2 Percent of Global CO2 Emissions. In: Gartner CP (ed.) (press release)
Barroso LA, Hölzle U (2007) The case for energy-proportional computing. IEEE Comput 40:33–37
Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: ACM SIGARCH Computer Architecture News, pp 13-23
Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Proceedings of the 2008 conference on Power aware computing and systems
Beloglazov A, Buyya R, Lee YC, Zomaya A (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv Comput 82:47–111
Devadas S, Malik S (1995) A survey of optimization techniques targeting low power VLSI circuits. In: Proceedings of the 32nd annual ACM/IEEE Design Automation Conference, pp 242–247
Hlavacs H, Treutner T (2012) Genetic algorithms for energy efficient virtualized data centers. In: Network and service management (cnsm), 2012 8th international conference and 2012 workshop on systems virtualiztion management (svm), pp 422–429
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. 28:755–768
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 24:1397–1420
Beloglazov A, Buyya R (2013) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. Parallel Distrib Syst IEEE Trans 24:1366–1379
Masoumzadeh SS, Hlavacs H (2013) An intelligent and adaptive threshold-based schema for energy and performance efficient dynamic VM consolidation. In: Energy efficiency in large scale distributed systems. Springer, pp 85–97
Maurya K, Sinha R (2013) Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center. Int J Comput Sci Mobile Comput
Salimian L, Safi F (2013) Survey of energy efficient data centers in cloud computing. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pp. 369–374
Beloglazov A, Buyya R (2010) 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
Sugeno M, Kang G (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15–33
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 41:23–50
Cingolani P, Alcalá-Fdez J (2013) jFuzzyLogic: a Java library to design fuzzy logic controllers according to the standard for fuzzy control programming. Int J Comput Intell Syst 6:61–75
Park K, Pai VS (2006) CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper Syst Rev 40:65–74
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Salimian, L., Safi Esfahani, F. & Nadimi-Shahraki, MH. An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines. Computing 98, 641–660 (2016). https://doi.org/10.1007/s00607-015-0474-5
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DOI: https://doi.org/10.1007/s00607-015-0474-5