Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the strategy proposed in this paper is compared with other four strategies through simulation experiments. The results show that our strategy for VM placement has better performance in reducing energy consumption than the other four strategies, and it can use less active hosts than others.

[1]  Abhishek Chandra,et al.  Exploiting Spatio-Temporal Tradeoffs for Energy-Aware MapReduce in the Cloud , 2012, IEEE Transactions on Computers.

[2]  Yang Guangyou,et al.  A Modified Particle Swarm Optimizer Algorithm , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[3]  Henri Casanova,et al.  Resource allocation algorithms for virtualized service hosting platforms , 2010, J. Parallel Distributed Comput..

[4]  Dan Lin,et al.  A competitive genetic algorithm for resource-constrained project scheduling problem , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[5]  Tim Cerling,et al.  Mastering Microsoft® Virtualization: Cerling/Mastering , 2009 .

[6]  Hidemoto Nakada,et al.  Toward Virtual Machine Packing Optimization Based on Genetic Algorithm , 2009, IWANN.

[7]  Jie Wu,et al.  Let's stay together: Towards traffic aware virtual machine placement in data centers , 2012, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[8]  Jingyu Wang,et al.  Ant colony optimization for the nonlinear resource allocation problem , 2006, Appl. Math. Comput..

[9]  Li Qiang,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .

[10]  Brian J. Watson,et al.  Autonomic Virtual Machine Placement in the Data Center , 2008 .

[11]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[12]  Zheng Qin,et al.  A Modified Particle Swarm Optimizer for Tracking Dynamic Systems , 2005, ICNC.

[13]  Hannu Tenhunen,et al.  Using Ant Colony System to Consolidate VMs for Green Cloud Computing , 2015, IEEE Transactions on Services Computing.

[14]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Wu Qing AN ANT COLONY ALGORITHM WITH MUTATION FEATURES , 1999 .

[17]  Guofei Jiang,et al.  Effective VM sizing in virtualized data centers , 2011, 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops.

[18]  Tae Young Kim,et al.  The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing , 2012 .

[19]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[20]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[21]  Chen-Khong Tham,et al.  Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing , 2011, 2011 IEEE International Conference on Advanced Information Networking and Applications.

[22]  Qiang Li,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .