Provide an Energy-aware Markov Based Model for Dynamic Placement of Virtual Machines in Cloud Data Centers
Subject Areas : electrical and computer engineeringmehdi rajabzadeh 1 , Abolfazl Toroghi Haghighat 2 * , Amir Masoud Rahmani 3
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Keywords: Meta heuristic algorithms, cloud computing, absorbing Markov chain, energy consumption,
Abstract :
The use of energy-conscious solutions is one of the important research topics in the field of cloud computing. By effectively using virtual machine placement and aggregation algorithms, cloud suppliers will be able to reduce energy consumption. In this paper, a new model is presented that seeks to achieve the desired results by improving the algorithms and providing appropriate methods. Periodic monitoring of resource status, proper analysis of the data obtained, and prediction of the critical state of the servers using the proposed Markov model have reduced the number of unnecessary migrations as much as possible. The combination of genetic algorithm and simulated annealing in the replacement section along with the definition of the adsorbent Markov chain has resulted in better and faster performance of the proposed algorithm. Simulations performed in different scenarios in CloudSim show that compared to the best algorithm compared, at low, medium and high load, energy consumption has decreased significantly. Violations of service level agreements also fell by an average of 17 percent.
[1] A. Beloglazov and R. Buyya, Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing, Ph.D Thesis, Melbourne University, May 2013.
[2] A. Y. Zomaya and Y. C. Lee, Energy Efficient Distributed Computing Systems, Wiley-IEEE Computer Society Press, Jul. 2016.
[3] A. Khosravi, S. G. Kumar, and R. Buyya, "Energy and carbon-efficient placement of virtual machines in distributed cloud data centers," in Proc. of the 19th In. Conf. on Parallel Processing Euro-Par'13, pp. 317-328, Aachen, Germany, 26-30 Aug. 2013.
[4] J. Yang, C. Liu, and Y. Shang, "A cost-aware auto-scaling approach using the workload prediction in service clouds," Inf Syst Front, vol. 16, pp. 7-18, Oct. 2017.
[5] R. Nathuji, C. Isci, and E. Gorbatov, "Exploiting platform heterogeneity for power efficient data centers," in Proc. of the 4th Int. Conf. on Autonomic Computing, vol. 7, pp. 5-15, May 2017.
[6] E. Feller, et al., "Energy management in IaaS clouds: a holistic approach," in Proc. IEEE 5th Inte. Conf. on, Cloud Computing, pp. 204-212, Honolulu, HI, USA, 24-29 Jun. 2018.
[7] A. Beloglazov and R. Buyya, "Managing overloaded PMs for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints," IEEE Trans. Parallel Distrib Syst, vol. 24, no. 7, pp. 1366-1379, Sep. 2013.
[8] G. Katsaros, et al., "A service framework for energy-aware monitoring and VM management in clouds," Future Generation Computer Systems, vol. 29, no. 8, pp. 2077-2091, Jan. 2015.
[9] I. Takouna, R. Rojas-Cessa, K. Sachs, and C. Meinel, "Communication-aware and energy-efficient scheduling for parallel applications in virtualized data centers," in Proc. 6th IEEE/ACM Int. Conf. on Utility and Cloud Computing, UCC’13, pp. 251-255, Dresden, Germany, 9-12 Dec. 2013.
[10] L. Salimian, F. S. Esfahani, and M. N. Shahraki, "An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines," Computing, vol. 98, no. 6, pp. 641-660, Mar. 2016.
[11] K. G. Saurabh, S. Y. Chee, and R. Buyya, "Green cloud framework for improving carbon efficiency of clouds," in Proc. of the 17th Int. European Conf. on Parallel and Distributed Computing, EuroPar’11, vol. 6853, pp. 193-203, LNCS, Springer, Germany, Dec. 2011.
[12] T. Mahdhi and H. Mezni, "A prediction-based VM consolidation approach in IaaS cloud data centers," The J. of Systems & Software, vol. 146, no. 12, pp. 263-285, Sept. 2018.
[13] I. Mohiuddin and A. Almogren, "Workload aware VM consolidation method in edge/cloud computing for IoT applications," J. Parallel Distrib. Comput., vol. 123, no. 1, pp. 204-214, Sept. 2019.
[14] M. Malekloo, N. Kara, and M. Barachi, "An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments," Sustainable Computing. Informatics and Systems, vol. 17, no. 2, pp. 9-24, Feb. 2019.
[15] H. Xu, Y. Liu, W. Wei, and Y. Xue, "Migration cost and energy-aware virtual machine consolidation under cloud environments considering remaining runtime," International J. of Parallel Programming, vol. 47, no. 1, pp. 481-501 , Mar. 2020.
[16] Z. Luo and Z. Qian, "Burstiness-aware server consolidation via queuing theory approach in a computing cloud," in Proc. IEEE 27th Int. Symp. on Parallel Distributed Processing, IPDPS’16, pp. 332-341, Cambridge, MA, USA, 20-24 May 2016.
[17] S. B. Melhem, A. Agrawal, N. Goel, and M. Zaman, "Markov prediction model for host load detection and VM placement in live migration, " IEEE Access, vol. 6, pp. 7190-7205, 2020.
[18] A. Vasan and K. S. Raju, "Comparative analysis of simulated annealing, simulated quenching and genetic, algorithms for optimal reservoir operation," Appl Soft Comput., vol. 9, no. 1, pp. 274-281, May 2016.
[19] C. Oysu and Z. Bingul, "Application of heuristic and hybrid-GASA algorithms to tool-pathoptimization problem for minimizing airtime during machining," Engineering Applications of Artificial Intelligence, vol. 22, no. 3, pp. 389-396, Apr. 2017.
[20] M. Rajabzadeh, A. T. Haghighat, and A. M. Rahmani, "New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing," J. Supercomput., vol. 76, no. 9, pp. 7438-7457, Dec. 2020.
[21] A. Aryania, H. S. Aghdasi, and L. Mohammad Khanli, "Energy-aware virtual machine consolidation algorithm based on ant colony system," J. Grid Computing, vol. 16, no. 2, pp. 477-491, Jul. 2019.
[22] N. Chaurasia, M. Kumar, and R. Chaudhry, "Comprehensive survey on energy-aware server consolidation techniques in cloud computing," J. Supercomput., vol. 77, no. 10, pp. 11682-11737, May 2021.
[23] M. Ala'anzy and M. Othman, "Mapping and consolidation of VMs using locust-inspired algorithms for green cloud computing," Neural Process Lett., vol. 77, no. 10, Oct. 2021.