A PSO Based VM Resource Scheduling Model for Cloud Computing

Cloud computing, a new paradigm for utility computing, has brought a revolutionary change in the IT industry by enabling the elastic on demand resource provisioning of computing resources. As cloud infrastructure heavily relies on virtualization technology, there is a need for an efficient and effective virtual machine scheduling strategy. Virtual machine scheduling problem can be defined as an allocation of a set of virtual machines (VMs) to a set of physical machines (PMs). The proposed work focuses on PSO based VM scheduling strategy for VM placement in cloud infrastructure. The strategy focuses on efficient VM allocation to physical servers in order to minimize the total resource wastage and the number of servers used. Simulation experiments were conducted to observe the allocation of VMs to the servers and to evaluate the proposed algorithm with respect to performance and scalability. The results are compared with Best-Fit, First-Fit and Worst-Fit placement strategies. Simulation study conducted to evaluate the performance reveals the effectiveness of the model.

[1]  A. Rezaee Jordehi,et al.  Parameter selection in particle swarm optimisation: a survey , 2013, J. Exp. Theor. Artif. Intell..

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  Anup Kumar Panda,et al.  Particle Swarm Optimization and Bacterial Foraging Optimization Techniques for Optimal Current Harmonic Mitigation by Employing Active Power Filter , 2012, Appl. Comput. Intell. Soft Comput..

[4]  Michael N. Vrahatis,et al.  Particle Swarm Optimization and Intelligence: Advances and Applications , 2010 .

[5]  Paolo Cremonesi,et al.  A Constraint Programming Approach for the Service Consolidation Problem , 2010, CPAIOR.

[6]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[7]  Bu-Sung Lee,et al.  Optimal virtual machine placement across multiple cloud providers , 2009, 2009 IEEE Asia-Pacific Services Computing Conference (APSCC).

[8]  Gang Yin,et al.  Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers , 2010, 2010 IEEE International Conference on Services Computing.

[9]  Zibin Zheng,et al.  Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers , 2013, 2013 International Conference on Parallel and Distributed Systems.

[10]  Michael A. Rappa,et al.  The utility business model and the future of computing services , 2004, IBM Syst. J..

[11]  Rajkumar Buyya,et al.  Mastering Cloud Computing: Foundations and Applications Programming , 2013 .

[12]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[13]  Avi Wigderson,et al.  P , NP and mathematics – a computational complexity perspective , 2006 .

[14]  Liang Liu,et al.  A multi-objective ant colony system algorithm for virtual machine placement in cloud computing , 2013, J. Comput. Syst. Sci..

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

[16]  Hans Kellerer,et al.  A 5/4 Linear Time Bin Packing Algorithm , 2000, J. Comput. Syst. Sci..

[17]  Dang Minh Quan,et al.  Energy Efficient Resource Allocation Strategy for Cloud Data Centres , 2011, ISCIS.

[18]  Andrzej Kochut,et al.  Dynamic Placement of Virtual Machines for Managing SLA Violations , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.