IJITCS Vol. 6, No. 10, 8 Sep. 2014
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Cloud Computing, Network Latency, Energy Efficiency, Particle Swarm Optimization (PSO) and Multi-Job Scheduling
Cloud computing is a large model change of computing system. It provides high scalability and flexibility among an assortment of on-demand services. To imporve the performance of the multi-cloud environment in distributed application might require less energy efficiency and minimal inter-node latency correspondingly. The major problem is that the energy efficiency of the cloud computing data center is less if the number of server is low, else it increases. To overcome the energy efficiency and network latency problem a novel energy-efficient particle swarm optimization representation for multi-job scheduling and Latency representation for the grouping of nodes with respect to network latency is proposed. The scheduling procedure is through on the basis of network latency and energy efficiency. Scheduling schema is the main part of Cloud Scheduler component, which helps the scheduler in scheduling decision on the base of dissimilar criterion. It also works well with incomplete latency information and performs intelligent grouping on the basis of both network latency and energy efficiency. Design a realistic particle swarm optimization algorithm for the cloud servers and construct an overall energy competence based on the purpose of the servers and calculation of fitness value for each cloud servers. Also, in order to speed up the convergent speed and improve the probing aptitude of our algorithm, a local search operative is introduced. Finally, the experiment demonstrates that the proposed algorithm is effectual and well-organized.
Nandhini A., Saravana Balaji B., "Energy-Efficient PSO and Latency Based Group Discovery Algorithm in Cloud Scheduling", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.10, pp.48-55, 2014. DOI:10.5815/ijitcs.2014.10.07
[1]P. Mell and T. Grance, “The NIST definition of cloud computing,” National Institute of Standards and Technology, vol. 53, no. 6, 2009.
[2]J. Hamilton, “Cooperative expendable micro-slice servers CEMS: low cost, low power servers for internet-scale services,” Citeseer.
[3]B. Karrer, E. Levina, M.E.J. Newman, Robustness of community structure in networks, Physical Review E 77 (2008) http://dx.doi.org/10.1103/PhysRevE. 77.046119.
[4]G.W. Flake, S. Lawrence, C.L. Giles, F.M. Coetzee, Self-organization and identification of web communities, IEEE Computer 35 (2007) 66–71. http://dx.doi.org/10.1109/ 2.989932.
[5]L.H. Hartwell, J.J. Hopfield, S. Leibler, A.W. Murray, From molecular to modular cell biology, Nature 402 (1999) C47–C52. http://dx.doi.org/10.1038/35011540.
[6]S. Redner, Citation statistics from more than a century, Physical Review (2004).
[7]C.T. Zahn, Graph-theoretical methods for detecting and describing gestalt cluster, IEEE Transactions on Computers C-20 (1971) 68–86. http://dx.doi.org/ 10.1109/T-C.1971.223083.
[8]S. Malik, F. Huet, D. Caromel, RACS: a framework for resource aware cloud computing, in: Proceedings of the 7th IEEE International Conference for Internet Technology and Secured Transactions, ICITST 2012, 2012.
[9]R. Andersen, F. Chung, K. Lang, Local graph partitioning using pagerank vectors, in: Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS’06, 2006, pp. 475–486. http://dx.doi.org/10.1109/ FOCS.2006.44.
[10]K. Lang, S. Rao, A flow-based method for improving the expansion or conductance of graph cuts, in: D. Bienstock, G. Nemhauser (Eds.), Proceedings of the 10th International IPCO Conference on Integer Programming and Combinatorial Optimization, in: Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2004, pp. 383–400.
[11]R.J. Gil-Garcia, J.M. Badia-Contelles, A. Pons-Porrata, A general framework for agglomerative hierarchical clustering algorithms, in: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, 2006, pp. 569–572. http://dx.doi.org/10.1109/ICPR.2006.69.
[12]A. Beloglazov and R. Buyya, “Energy efficient allocation of virtual machines in cloud data centers,” in Proceedings of the 10th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid ’10), pp. 577–578, Melbourne, Australia, May 2010.
[13]A. Berl, E. Gelenbe, M. Di Girolamo et al., “Energy-efficient cloud computing,” Computer Journal, vol. 53, no. 7, pp. 1045–1051, 2010.
[14]R. Buyya, A. Beloglazov, and J. Abawajy, “Energy-Efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges,” Distributed, Parallel, and Cluster Computing, http://arxiv.org/abs/1006.0308/, pp. 6–17, 2010.
[15]J. Baliga, R. W. A. Ayre, K. Hinton, and R. S. Tucker, “Green cloud computing: balancing energy in processing, storage and transport,” Proceedings of the IEEE, vol. 99, no. 1, pp. 149–167, 2011.
[16]L. A. Barroso and U. H¨ olzle, “The datacenter as a computer: an introduction to the design of warehouse-scale machines,” Synthesis Lectures on Computer Architecture, vol. 4, no. 1, pp. 1–108, 2009.
[17]“Google’s chiller-less data center,” 2009, http://www.datacenterknowledge.com/
[18]S. Srikantaiah, A. Kansal, and F. Zhao, “Energy aware consolidation for cloud computing,” in Proceedings of the Conference on Power aware computing and systems, p. 10, USENIX Association, San Diego, Calif, USA, 2008.