Abstract

There are an increasing number of Cloud Services available in the Internet. Cloud services can be a component of a system and different Cloud Servers that would provide different services. In this present work we have defined a multiple cloud environment. Each cloud server is defined with certain limits in terms of memory and the CPU specifications. Now as the users enter to the system, the user request is performed in terms of processes. To represent the parallel user requests, n number of requests are been generated by the users. All these requests are to be handled by the cloud servers in parallel by using the multiple cloud concept. A middle layer is defined between the cloud servers and the client requests that will perform the allocation of the processes to different clouds in under load and over load conditions. As user requests are performed, some parameters are also defined with each request. These parameters are the process time, deadline, input output specifications etc. In the general case, the allocations of the processes are performed in a sequential order. Each process must be executed within the deadline limit. But if more than one processes occur at same time and not get executed before the deadline, in such case the processes is switched from one cloud server to other called the process migration. In this present work, a parametric analysis is performed to identify the requirement of process migration and based on this analysis the migration will be performed on these processes. The effectiveness of the work is identified in terms of successful execution of the processes within the time limits

References

1. T. Hirofuchi, H. Ogawa, H. Nakada, S. Itoh, and S. Sekiguchi, ―A Storage Access Mechanism for Wide-Area Live Migration of Virtual Machines,‖ In Summer United Workshops on Parallel, Distributed and Cooperative Processing,2008, pp. 19–24
2. I. Foster, T. Freeman, K. Keahey, D. Scheftner, B. Sotomayor, and X. Zhang, ―Virtual Clusters for Grid Communities,‖ In IEEE Int. Symp. On Cluster Computing and the Grid, 2006, pp 513– 520.
3. M. Strasser and H. Stamer, ―A Software-Based Trusted Platform Module Emulator,‖ In Trust ’08 Proc. of the 1st Int. Conf. on Trusted Computing and Trust in Information Technologies, 2008, pp. 33–47.
4. Y. Luo, B. Zhang, X. Wang, Z. Wang, Y. Sun, and H. Chen, ―Live and incremental wholesystem migration of virtual machine using block-bitmap,‖ In IEEE Int. Conf. on Cluster Computing, 2008.
5. H. Nishimura, N. Maruyama, and S. Matsuoka, ―Virtual Clusters on the Fly - Fast Scalable and Flexible Installation,‖ In IEEE Int. Symp. On Cluster Computing and the Grid, 2007,pp. 549–556.
6. M. Tatezono, H. Nakada, and S. Matsuoka, ―Mpi environment with load balancing using virtual machine,‖ In Symp. On Advanced Computing Systems and Infrastructures SACSIS, 2006, pp. 525–532
7. S. Venugopal, R. Buyya, and K. Ramamohanarao. A taxonomy of Data Grids for distributed data sharing, management, and processing. In ACM Computing Surveys, volume 38,2006.
8. A. Gopalakrishnan. 2009. Cloud Computing Identity Management. SET Labs Briefings. Vol. 7, No. 7. pp. 45-55
9. M. K. Srinivasan, P. Rodrigues, ―A roadmap for the comparison of identity management solutions based on state-of-the-art IdM taxonomies,‖ Springer Communications in Computer and Information Science,2010, pp. 349-358. Springer-Verlag Berlin Heidelberg, New York, USA.
10. M. K. Srinivasan, P. Rodrigues, ―Analysis on identity management systems with extended state- of-theart IdM taxonomy factors,‖ International Journal of Ad hoc, Sensor & Ubiquitous Computing. (December 2010), Vol.1, No.4. pp. 62-70. DOI=10.5121/ijasuc.2010.1406

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