Differential evolution algorithm for optimizing virtual machine placement problem in cloud computing

Differential evolution algorithm for optimizing virtual machine placement problem in cloud computing

Автор: Amol C. Adamuthe, Jayshree T. Patil

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 7 vol.10, 2018 года.

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Primary concern of any cloud provider is to improve resource utilization and minimize cost of service. Different mapping relations among virtual machines and physical machines effect on resource utilization, load balancing and cost for cloud data center. Paper addresses the virtual machine placement as optimization problem with resource constraints on CPU, memory and bandwidth. In experimentations, datasets are formed using random data generator. Paper presents random fit algorithm, best fit algorithm based on resource wastage and an evolutionary algorithm- Differential Evolution. Paper presents results of Differential Evolution algorithm with three different mutation approaches. Results show that Differential Evolution algorithm with DE/best/2 mutation operator works efficient than basic DE, best fit and random fit algorithms.

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Differential Evolution Algorithm (DE), Virtual machine placement problem (VMP), Best fit, Random fit

Короткий адрес: https://sciup.org/15016508

IDR: 15016508   |   DOI: 10.5815/ijisa.2018.07.06

Список литературы Differential evolution algorithm for optimizing virtual machine placement problem in cloud computing

  • G. Lee, “Resource Allocation and Scheduling in Heterogeneous Cloud Environments”, Technical Report No. UCB/EECS-2012, 2012.
  • A. Kumar, C. Sathasivam, P. Periyasamy, “Virtual machine placement in cloud computing”, Indian Journal of Science and Technology, 2016, vol. 9, no. 29.
  • M. Masdari, SS. Nabavi, V. Ahmadi, “An overview of virtual machine placement schemes in cloud computing,” Journal of Network and Computer Applications, 2016 , vol. 66, pp. 106-127.
  • Y. Gao, H. Guan, Z. Qi, Y. Hou, L. Liu., “A multi-objective ant colony system algorithm for virtual machine placement in cloud computing,” Journal of Computer and System Sciences, 2013, vol. 79, no .8, pp. 1230-1242.
  • R. Sookhtsaraei, M. Madani, A. Kavian, “A multi objective virtual machine placement method for reduce operational costs in cloud computing by genetic,” International Journal of Computer Networks and Communications Security, 2014, vol. 2, no. 8, pp. 250-256.
  • J. Dong, X. Jin, H. Wang, Y. Li, P. Zhang, S. Cheng, “Energy-saving virtual machine placement in cloud data centers,” In Cluster, Cloud and Grid Computing, 13th IEEE/ACM International Symposium, 2013, pp. 618-624.
  • A. C. Adamuthe, R. M. Pandharpatte, G. T. Thampi, “Multiobjective virtual machine placement in cloud environment,” In Cloud & Ubiquitous Computing & Emerging Technologies, IEEE International Conference, 2013, pp. 8-13.
  • F. Lopez-Pires, B. Baran, “Virtual machine placement literature review”, arXiv preprint arXiv:1506.01509, 2015.
  • A. Choudhary, S. Rana, K. J. Matahai, “A Critical Analysis of Energy Efficient Virtual Machine Placement Techniques and its Optimization in a Cloud Computing Environment,” International Conference on Information Security & Privacy, 2015, Nagpur, INDIA Procedia Computer Science, 2016, pp. 132-138.
  • V. Mann, A. Kumar, P. Dutta, S. Kalyanaraman, “VMFlow: Leveraging VM mobility to reduce network power costs in data centers”, IBM technical report, NETWORKING 2011, 2011, pp. 198-211.
  • K. Shi, H. Yu, F. Luo, G. Fan, “Multi-Objective Biogeography-Based Method to Optimize Virtual Machine Consolidation,” In SEKE 2016, pp. 225-230.
  • S. Wang, Z. Liu, Z. Zheng, Q. Sun, F. Yang, “Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers,” In Parallel and Distributed Systems, IEEE International Conference, 2013, pp. 102-109.
  • D. Chang, G. Xu, L. Hu, K. Yang, “A network-aware virtual machine placement algorithm in mobile cloud computing environment,” IEEE Wireless Communications and Networking Conference Workshops, 2013, pp. 117-122.
  • D. Kakadia, N. Kopri, V. Varma, “Network-aware virtual machine consolidation for large data centers,” In Proceedings of the 3rd ACM International Workshop on Network-Aware Data Management, 2013, pp. 6.
  • J. J. Prevost, K. Nagothu, B. Kelley, M. Jamshidi, “Optimal update frequency model for physical machine state change and virtual machine placement in the cloud,” In System of Systems Engineering, 8th IEEE International Conference, 2013, pp. 159-164.
  • L. Shi, B. Butler, D. Botvich, B. Jennings, “Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds,” In Integrated Network Management, IFIP/IEEE International Symposium, 2013, pp. 499-505.
  • K. Le, R. Bianchini, J. Zhang, Y. Jaluria, J. Meng, T. D. Nguyen, “Reducing electricity cost through virtual machine placement in high performance computing clouds,” In Proceedings of 2011 ACM International Conference for High Performance Computing, Networking, Storage and Analysis, 2011, pp. 22.
  • M. Sun, W. Gu, X. Zhang, H. Shi, W. Zhang, “A matrix transformation algorithm for virtual machine placement in cloud,” In Trust, Security and Privacy in Computing and Communications, 12th IEEE International Conference, 2013, pp. 1778-1783.
  • M. Malekloo, N. Kara, “Multi-objective ACO Virtual Machine Placement in Cloud Computing Environments,” IEEE workshop on Cloud Computing systems, Networks, and Applications, 2014, pp. 112-116.
  • G. Wu, M. Tang, Y.C. Tian, W. Li, “Energy-Efficient virtual machine placement in data centers by genetic algorithm,” 19th international conference on Neural Information Processing, 2012, vol. 3, pp. 315-323.
  • K. Li, H. Zheng, J. Wu, “Migration-based virtual machine placement in cloud systems,” In Cloud Networking, 2nd IEEE International Conference, 2013, pp. 83-90.
  • J. T. Piao, J. Yan, “A network-aware virtual machine placement and migration approach in cloud computing,” In Grid and Cooperative Computing, 9th IEEE International Conference, 2010, pp. 87-92.
  • C. C. Mark, D. Niyato, T. Chen-Khong, “Evolutionary optimal virtual machine placement and demand forecaster for cloud computing,” In Advanced Information Networking and Applications, 2011 IEEE International Conference, 2011, pp. 348-355.
  • Z. Zheng, R. Wang, H. Zhong, X. Zhang, “An approach for cloud resource scheduling based on Parallel Genetic Algorithm,” In Computer Research and Development, 3rd IEEE International Conference, 2011, vol. 2, pp. 444-447.
  • J. Zhao, L. Hu, Y. Ding, G. Xu, M. Hu, “A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment,” PloS one. 2014, vol. 9, no. 9.
  • V. Kachitvichyanukul, “Comparison of three evolutionary algorithms: GA, PSO, and DE,” Industrial Engineering and Management Systems, 2012, vol. 11, no. 3, pp. 215-223.
  • H. Kazemipoor, R. Tavakkoli-Moghaddam, P. Shahnazari-Shahrezaei, A. Azaron, “A differential evolution algorithm to solve multi-skilled project portfolio scheduling problems,” The International Journal of Advanced Manufacturing Technology, 2013, pp. 1099-1111.
  • V. Santucci, M. Baioletti, A. Milani, “Algebraic differential evolution algorithm for the permutation flowshop scheduling problem with total flowtime criterion,” IEEE Transactions on Evolutionary Computation, 2016, vol. 20, no. 5, pp. 682-694.
  • J. T. Tsai, J. C. Fang, J. H. Chou, “Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm,” Computers & Operations Research, 2013, vol. 40, no.12, pp. 3045-3055.
  • D. Libao, W. Sha, J. Chengyu, H. Cong, “A Hybrid Mutation Scheme-Based Discrete Differential Evolution Algorithm for Multidimensional Knapsack Problem,” In Instrumentation & Measurement, Computer, Communication and Control, 6th IEEE International Conference, 2016, pp. 1009-1014.
  • A. Glotić, P. Kitak, J. Pihler, I. Tičar, “Parallel self-adaptive differential evolution algorithm for solving short-term hydro scheduling problem,” IEEE Transactions on Power Systems, 2014, vol. 29, no. 5, pp. 2347-2358.
  • T. Liu, M. Maeda, “Set-Based Differential Evolution for Traveling Salesman Problem,” In Intelligent Networks and Intelligent Systems, 6th IEEE International Conference, 2013, pp. 107-110.
  • K. Suresh, D. Kundu, S. Ghosh, S. Das, A. Abraham, S. Y. Han,” Multi-objective differential evolution for automatic clustering with application to micro-array data analysis,” Sensors, 2009, vol. 9, no. 5, pp. 3981-4004.
  • F. Lezama, A. Y. Rodríguez-González, E. M. de Cote, “Load pattern clustering using differential evolution with Pareto Tournament,” In Evolutionary Computation, IEEE Congress, 2016, pp. 241-248.
  • M. Zhao, R. Liu, W. Li, H. Liu, “Multi-objective optimization based differential evolution constrained optimization algorithm,” In Intelligent Systems, 2010 2nd IEEE WRI Global Congress, 2010 D, vol. 1, pp. 320-326.
  • B. Rieck, “Basic Analysis of Bin-Packing Heuristics,” Publicado por Interdisciplinary Center for Scientific Computing. Heildelberg University, 2010.
  • J. Xu, J. A. Fortes, “Multi-objective virtual machine placement in virtualized data center environments,” In Proceedings of the 2010 IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, 2010 Dec 18, pp. 179-188.
  • R. Storn, K. Price, “Differential Evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997, vol. 11, no. 4, pp. 341-59.
  • J. Adeyemo, F. Bux, F. Otieno, “Differential evolution algorithm for crop planning: Single and multi-objective optimization model,” International Journal of Physical Sciences, 2010, vol. 5, no. 10, pp. 1592-1599.
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