Intelligent Optimization Strategy for Virtual Machine Placement in Data Center
NI Zhi-Wei, LIANG Ting, WU Zhang-Jun, XIAO Hong-Wang
School of Management, Hefei University of Technology, Hefei 230009 Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education,Hefei University of Technology, Hefei 230009
Abstract:Virtual machine placement problem is one of the key issues affecting the performance of data centers. An intelligent optimization strategy for virtual machine placement in the data center is proposed with comprehensive consideration of resource wastage, power consumption and load balance. Firstly, a multi-objective mathematical model for the virtual machine placement optimization is built by the strategy. Secondly, the placement problem is abstracted as the bin packing problem. Finally, an optimization strategy based on the improved adaptive discrete glowworm swarm optimization is put forward. Simulation experiment results show that the proposed adaptive discrete glowworm swarm optimization has good robustness and convergence rate, and the proposed intelligent optimization strategy solves the virtual machine placement problem effectively.
[1] Feng S C, Di Y Q, Zhu Y C, et al. Approach to Optimize Virtual Machine Deployment in IaaS. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2012, 40(z1): 359-364 (in Chinese) (冯少冲,邸彦强,朱元昌,等. IaaS云计算中虚拟机部署算法研究.华中科技大学学报:自然科学版, 2012, 40(z1): 359-364) [2] Campegiani P, Lo Presti F. A General Model for Virtual Machines Resources Allocation in Multi-tier Distributed Systems // Proc of the 5th International Conference on Autonomic and Autonomous Systems. Valencia, Spain, 2009: 162-167 [3] Xu J, Fortes J A B. Multi-objective Virtual Machine Placement in Virtualized Data Center Environments // Proc of the IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing. Hangzhou, China, 2010: 179-188 [4] Xu J, Fortes J A B. A Multi-objective Approach to Virtual Machine Management in Datacenters // Proc of the 8th ACM International Conference on Autonomic Computing. Karlsruhe, Germany, 2011: 225-234 [5] Chen M, Zhang H, Su Y Y, et al. Effective VM Sizing in Virtua-lized Data Centers // Proc of the 12th IFIP/IEEE International Symposium on Integrated Network Management. Dublin, Ireland, 2011: 594-601 [6] Wei L, Huang T, Chen J Y, et al. Workload Prediction-Based Algorithm for Consolidation of Virtual Machines. Journal of Electronics & Information Technology, 2013, 35(6): 1271-1276 (in Chinese) (魏 亮,黄 韬,陈建亚,等.基于工作负载预测的虚拟机整合算法.电子与信息学报, 2013, 35(6): 1271-1276) [7] Li Q, Hao Q F, Xiao L M, et al. Adaptive Management and Multi-objective Optimization for Virtual Machine Placement in Cloud Computing. Chinese Journal of Computers, 2011, 34(12): 2253-2264 (in Chinese) (李 强,郝沁汾,肖利民,等.云计算中虚拟机放置的自适应管理与多目标优化.计算机学报, 2011, 34(12): 2253-2264) [8] Liu H K, Xu C Z, Jin H, et al. Performance and Energy Modeling for Live Migration of Virtual Machines // Proc of the 20th International Symposium on High Performance Distributed Computing. San Jose, USA, 2011: 171-182 [9] Krishnanand K N, Ghose D. Glowworm Swarm Based Optimization Algorithm for Multimodal Functions with Collective Robotics Applications. Multiagent and Grid Systems: An International Journal, 2006, 2(3): 209-222 [10] Zhang J L, Zhou Y Q. An Artificial Glowworm Swarm Optimization Algorithm Based on Powell Local Optimization Method. Pattern Recognition and Artificial Intelligence, 2011, 24(5): 680-684 (in Chinese) (张军丽,周永权.一种用Powell方法局部优化的人工萤火虫算法.模式识别与人工智能, 2011, 24(5): 680-684) [11] Krishnanand K N, Ghose D. Glowworm Swarm Optimization for Simultaneous Capture of Multiple Local Optima of Multimodal Functions. Swarm Intelligence, 2009, 3(2): 87-124 [12] Ni Z W, Xiao H W, Wu Z J, et al. Attribute Selection Method Based on Improved Discrete Glowworm Swarm Optimization and Fractal Dimension. Pattern Recognition and Artificial Intelligence, 2013, 26(12): 1169-1178 (in Chinese) (倪志伟,肖宏旺,伍章俊,等.基于改进离散型萤火虫群优化算法和分形维数的属性选择方法.模式识别与人工智能, 2013, 26(12): 1169-1178) [13] Krishnanand K N, Ghose D. A Glowworm Swarm Optimization Based Multi-robot System for Signal Source Localization // Liu D K, Wang L F, Tan K C, eds. Design and Control of Intelligent Robotic Systems. Berlin, Germany: Springer, 2009: 49-68 [14] Cheng K, Ma L. Artificial Glowworm Swarm Optimization Algorithm for 0-1 Knapsack Problem. Application Research of Compu-ters, 2013, 30(4): 993-998 (in Chinese) (程 魁,马 良.0-1背包问题的萤火虫群优化算法.计算机应用研究, 2013, 30(4): 993-998) [15] Gong Q Q, Zhou Y Q, Luo Q F. Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Multi-dimensional Knapsack Problem. Procedia Engineering, 2011, 15: 2880-2884 [16] Cheng K, Ma L. Artificial Glowworm Swarm Optimization Algorithm for Location Problem. Journal of University of Shanghai for Science and Technology, 2013, 35(3): 205-208 (in Chinese) (程 魁,马 良.平面选址问题的萤火虫算法.上海理工大学学报, 2013, 35(3): 205-208) [17] Zhou Y Q, Huang Z X, Liu H X. Discrete Glowworm Swarm Optimization Algorithm for TSP Problem. Acta Electronica Sinica, 2012, 40(6): 1164-1170 (in Chinese) (周永权,黄正新,刘洪霞.求解TSP问题的离散型萤火虫群优化算法.电子学报, 2012, 40(6): 1164-1170) [18] Lin C, Tian Y, Yao M. Green Network and Green Evaluation: Mechanism, Modeling and Evaluation. Chinese Journal of Compu-ters, 2011, 34(4): 593-612 (in Chinese) (林 闯,田 源,姚 敏.绿色网络和绿色评价:节能机制、模型和评价.计算机学报, 2011, 34(4): 593-612) [19] Andrews M, Anta A F, Zhang L, et al. Routing for Energy Minimization in the Speed Scaling Model [EB/OL]. [2014-03-01]. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5462071 [20] Liu Z Y, Hacgümüs H, Moon H J, et al. PMAX: Tenant Placement in Multitenant Databases for Profit Maximization // Proc of the 39th International Conference on Very Large Data Bases. Riva del Garda, Italy, 2013: 442-453 [21] Yong L Q. Differential Evolution Algorithm for Solving Multiobjective Optimization Problem Based on Maximum Entropy Function Methods. Journal of Central South University: Science and Technology, 2013, 44(z2): 160-164 (in Chinese) (雍龙泉.求解一类多目标优化问题的极大熵差分进化算法.中南大学学报:自然科学版, 2013, 44(z2): 160-164) [22] Liu H Y, Chen G B, Peng C. Solving Multi-objective Programming Problems with Maximal Entropy Method and Genetic Algorithm. Journal of Southwest Jiaotong University, 2003, 38(1): 8-11 (in Chinese) (刘海燕,陈高波,彭 川.遗传算法与极大熵相结合解多目标规划问题.西南交通大学学报, 2003, 38(1): 8-11) [23] Krishnanand K N, Ghose D. Glowworm Swarm Optimisation: A New Method for Optimizing Multi-modal Functions. International Journal of Computational Intelligence Studies, 2009, 1(1): 93-119