Virtual Machine Allocation Policy In Cloud Computing
Keywords:
Virtualization, Resource Allocation, Service Level Agreement (SLA), Virtual Machine AllocationAbstract
Cloud computing is a very powerful concept that can be used to enhance the next generation data center and allow service provider to use data center capability provided by cloud and develop the application based on user requirement. Data center of this cloud computing has huge number of resources and list of applications (with different architecture, configuration and requirement for deployment) want to use those resource. Cloud computing environment uses virtualization concept and provides resources to application by creating and allocating virtual machine to specific application. There for resource allocation policies and load balance policies play very vital role in allocating and managing the resources among various application in cloud computing life cycle.
The next generation of computation service will be provided by the cloud computing services. Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. Dynamic selection of virtual machines plays an important role in providing services to the consumers. This paper discusses the design and implementation of the dispatcher algorithm for effective utilization of the cloud resources. Also presents a case study which examines the implementation of the dispatcher algorithm, by a server, A proper scheduling and efficient load balancing across the network can lead to improve overall system performance and a lower turnaround time for individual tasks.
References
Lizhewang, JieTao, Kunze M., Castellanos, A.C,Kramer, D.,Karl,w, ”High Performance Computing and Communications”, IEEE International Conference HPCC,2008,pp.825-830.
ZhixiongChen,JongP.Yoon,”International Conference on P2P, Parallel,Grid,Cloud and Internet Computing”,2010 IEEE:pp 250-257.
P. T. Endo, “Resourc alocation for distrbuted cloud: Concept and Research challenges”, IEE, pp. 42-46.
J. Y. Shei, M. Taiefi and A. Khreisheah, “Resource Planing for Paralel Procesing in the Cloud”, IEEE 13th International Conference on High Performance and Computing, (2011).
S. Majumdar, “Resource Management on cloud: Handling uncertainties in Parameters and Policies”, CSI Communication, (2011), pp.16-19.
B. Oiza, “A Proposed Serviece Broker Stratagy in CloudAnalyst for Cost-Effactive Data Centre Selection Dhavael Limbeani”, International Journal of Engineering Research and Applications (IJERA), ISSN: 2248-9622www.ijera.com, vol. 2, no. 1, (2012), pp. 793-797 793.
R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose and R. Buyya, “CloudSim: A Toolkit for the Modeling and Simulation of Cloud Resource Management and Application Provisioning Techniques”.
C. E. L. Duemitresecu and I. R. Fostera, “GangSim: a simulator for grid scheduling studies”, Proceedings of the IEEE International Symposium on Cluster Computing and the Grid, (2005).
“Scheduling distributed applications: the SimGrid simulation framework”, Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, (2003).
R. Buyya and M. Murshed, “GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing.Concurrency and Computation”, Practice and Experience, Wiley Press, vol. 14, no. 13-15, (2002).
I. Foster, C. Kesselman and M. Kaufmann, “The Grid: Blueprint for a New Computing Infrastructure”, (1999).
S. Chaisiri, B. S. Lee and D. Niyato, “Optimal virtual machine placement across multiple cloud Providers”, In: Services computing conference, APSCC, IEEE Asia-Pacific, (2009).
M. E. Frincu and C. Craciun, “Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments”, Fourth IEEE international conference on utility and cloud computing, (2011).
J. Tordsson, R. S. Montero, R. M. Vozmediano and I. M. Llorente, “Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers”, Future Gener Comput Syst., vol. 28, no. 2, (2011), pp. 358–367.
R. N. Calheiros, R. Ranjan and A. Belo, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”.
R. Buyya, R. Ranjan and R. N. Calheiros, “Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities”.
Zhen Kong et.al : Mechanism Design for Stochastic Virtual Resource Allocation in Non-Cooperative Cloud Systems: 2011 IEEE 4th International Conference on Cloud Computing :pp,614-621.
W. E. Walsh, G. Tesauro, J. O. Kephart, and R. Das, “Utility Functions in Autonomic Systems,” in ICAC ’04: Proceedings of the First International Conference on Autonomic Computing. IEEE Computer Society, pp. 70–77, 2004.
Yazir Y.O., Matthews C., Farahbod R., Neville S., Guitouni A., Ganti S., Coady Y., “Dynamic resource allocation based on distributed multiple criteria decisions in computing cloud,” in 3rd International Conference on Cloud Computing, Aug. 2010, pp.91-98.
Goudarzi H., Pedram M., “Multi-dimensional SLA-based Resource Allocation for Multi-tier Cloud Computing Systems,” in IEEEInternational Conference on Cloud Computing, Sep. 2011, pp. 324-331.
Kai Lu, Riky Subrata and Albert Y. Zomaya, Networks & Systems Lab, School of Information Technologies, University of Sydney “An Efficient Load Balancing Algorithm for Heterogeneous Grid Systems Considering Desirability of Grid Sites”.
Chieu T.C., Mohindra A., Karve A.A., Segal A., “Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment,” in IEEE International Conference on e-Business Engineering, Dec. 2009, pp.281-286
