Abstract

Cloud computing refers to Internet based distributed computing where the physical resources are pooled at one end and users across the globe can have access to unlimited resources as payas- you-go utility computing model. Cloud service users requests computing resources and cloud service provider provides them as virtual machine (VM) instances. The problem addressed here is dynamic VM creation and allocation which benefit users in terms of response time and Cloud Service Providers (CSP) in terms of reduced energy and management cost by increasing the utilization of physical resources which are powered up for the time being and reduce the number of machines which need to be turned on. The proposed system include a demand forecast module which helps provisioning sub system, that manages the dynamic provisioning, in VM creation and management decisions.

References

• Mladen A Vouck, Cloud Computing - Issues, Research and Implementation, Journal of Computing and Information Technology, Vol -16, 2008.
• T. Chieu and H. Chan, Dynamic resource allocation via distributed decisions in cloud environment, IEEE eighth International Conference on e-Business Engineering (ICEBE), pp 125-130, 2011.
• D Tammaro, E A Doumith, S A Zahr, J Smets andMGagnaire, Dynamic resource allocation in cloud environment under time variant job requests, Proc. of IEEE third International Conference on cloud computing technology and science, pp 592-598, 2011.
• Daniel Warneke, Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud, IEEE Transactions on Parallel and Distributed Systems, pp 985-987, 2011.
• J. Dean and S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, Proceedings of Sixth Conference Symposium on Operating Systems Design and Implementation (OSDI 04), pp 10, 2004.
• F. Wuhib, R. Stadler and M. Spreitzer, A gossip protocol for dynamic resource management in large cloud environments, IEEE transactions on network and service management vol 9, pp 213 - 225, 2012.
• Kyle Chard and Kris Bubendorfer, High performance resource allocation strategies for computational economies, IEEE Transactions on Parallel and Distributed Systems,2012.
• A. Leivadeas, C. Papagianni and A. Papavassiliou, Efficient resource mapping framework over networked clouds via iterated local search based request partitioning, IEEE Transactions on Parallel and Distributed Systems-Special edition on Cloud Computing, 2012.
• M. Feng, X. Wang, Y. Zhang and J. Li, Multi-objective particle swarm optimization for resource allocation in cloud computing, IEEE International conference, CCIS2012, 2012
• S. Nakrani, C. Tovey, On honey bees and dynamic allocation in an Internet Server Colony, 2nd International workshop on mathematics and algorithms of social insects, CCIS2012, 2003
• A. Beloglazov, J. Abawajy, R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centres for cloud computing, Elsevier journal of Future generation computer systems, 2012
• M. Andreolini, S. Casolari, Load prediction models in web based systems, ACM conference on performance evaluation methodologies and tools, 2006