The Multi-objective VM Resource Scheduling by Using an Improved PSO Algorithm in the Cloud Data Center

With the exponential increase of the cloud business volume, Data center occurs load imbalance caused by some physical machine inefficiency due to the diversity of users requirements. Therefore the cloud datacenter need an appropriate algorithm to balance the PMs load and ensure the resource utilization in the cloud datacenter. The paper defines and formulates the problem parameters and proposes a Multi-objective Discrete Particle Swarm Optimization (MDPSO) to schedule the resources to the VMs requests according to the requirements. The simulation shows that the MDPSO algorithm not only guarantees the resource utilization, but also insures the PMs Load balance.

[1]  Rajkumar Buyya,et al.  Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation , 2009, CloudCom.

[2]  Jian Wang,et al.  A Discrete Particle Swarm Optimization for Solving Multiple Knapsack Problems , 2009, 2009 Fifth International Conference on Natural Computation.

[3]  Albert Y. Zomaya,et al.  A survey on resource allocation in high performance distributed computing systems , 2013, Parallel Comput..

[4]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[5]  Eddy Caron,et al.  Cloud Computing Resource Management through a Grid Middleware: A Case Study with DIET and Eucalyptus , 2009, 2009 IEEE International Conference on Cloud Computing.

[6]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[7]  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.

[8]  Zhang Li.  Solutions of Multi-Objective Optimization Problems Based on Particle Swarm Optimization , 2004 .

[9]  Mladen A. Vouk,et al.  Cloud computing — Issues, research and implementations , 2008, ITI 2008 - 30th International Conference on Information Technology Interfaces.

[10]  Alexandru Iosup,et al.  A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing , 2009, CloudComp.

[11]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[12]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[13]  Yi Peng,et al.  The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment , 2011, The Journal of Supercomputing.

[14]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[15]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

[17]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[18]  Junaid Shuja,et al.  Data center energy efficient resource scheduling , 2014, Cluster Computing.

[19]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.

[20]  Chi-Ying Tsui,et al.  Stable round-robin scheduling algorithms for high-performance input queued switches , 2002, Proceedings 10th Symposium on High Performance Interconnects.

[21]  Emmanouel A. Varvarigos,et al.  A framework for providing hard delay guarantees and user fairness in Grid computing , 2009, Future Gener. Comput. Syst..