Abstract
In cloud computing, the data of processing and the data of transfering is charged at for the service of the provider. So, it is important to reduce the cost and to improve the performance for the consumer of the cloud computing. At present, the existing optimization algorithms only focus on one aspect , such as reducing the move of data, the processing time, the transferring time, the processing cost or the transferring cost. This paper makes a model for the multi-objective data placement and uses a particle swarm optimization algorithm to optimize the time and cost in cloud computing. The mode applied processors interaction graph to map the data of the task and the data center. The simulation experimental result manifests that the proposed method is more effective in time and cost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
NIST Definition of Cloud Computing v15, http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc
Hayes, B.: Cloud computing. Communications of the ACM (7), 9–11 (2008)
Armbrust, M., et al.: Above the Clouds: A Berkeley View of Cloud Computing, Technical Report, http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf
Yuan, D., Yang, Y., Liu, X.: A data placement strategy in scientific cloud workflows. Future Generation Computer Systems, 1200–1214 (2010)
Pandey, S., Barker, A., Gupta, K.K., Buyya, R.: Minimizing Execution Costs when Using Globally Distributed Cloud Services. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 222–229 (2010)
Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, vol. i(1), pp. 400–407. IEEE (2010)
Tordssona, J., Monterob, R.S., Moreno-Vozmedianob, R., Llorenteb, I.M.: Cloud brokeringmechanisms for optimizedplacement of virtualmachinesacross multiple providers. Future Generation Computer Systems 28(2), 358–367 (2012)
Myint, J.: A data placement algorithm with binary weighted tree on PC cluster-based cloud storage system. In: 2011 International Conference on Cloud and Service Computing (CSC), December 12-14 (2011)
Zhang, L., Chen, Y.H., Sun, R.Y., Jing, S., Yang, B.: A Task Scehduling Algorithm Based on PSO fro Grid Computing. International Jouranal of Computational Intelligence Research, 37–43 (2008)
Yin, P.Y., Yu, S.S., Wang, P.P., Wang, Y.T.: A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems. Computer Standards & Interfaces 28, 441–450 (2006)
Guo, L.Z., Zhao, S.G., Shen, S.G., Jiang, C.Y.: Task Scheduling Optimization. Cloud Computing Based on Heuristic Algorithm Journal of Networks 7(3), 547–553 (2012)
Chang, C.K., Jiang, H., Di, Y., Zhu, Y., Ge, D.: Time-line based model for software project scheduling with genetic algorithms. Information and Software Technology, 1142–1154 (2008)
Gharooni-fard, G., Moein-darbari, F., Deldari, H., Morvaridi, A.: Procedia Computer Science. In: ICCS 2010, vol. 1(1), pp. 1445–1454 (May 2010)
Salman, A.: Particle swarm optimization for task assignment Problem. Microprocessors and Microsystems 26(8), 363–371 (2002)
Amazon EC2 Pricing, http://aws.amazon.com/ec2/pricing/ (visited:November 4, 2012)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. IEEE Congr. Evol. Comput., pp. 1945–1950 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, L., He, Z., Zhao, S., Zhang, N., Wang, J., Jiang, C. (2012). Multi-objective Optimization for Data Placement Strategy in Cloud Computing. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-34041-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34040-6
Online ISBN: 978-3-642-34041-3
eBook Packages: Computer ScienceComputer Science (R0)