Abstract
As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with cat swarm-based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared with multi-objective particle swarm optimization (MOPSO) technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25, 599–616 (2009)
Dikaiakos, M.D., Pallis, G., Katsaros, D., Mehra, P., Vakali, A.: Distributed internet computing for IT and scientific research. IEEE Internet Comput. 13, 10–13 (2009)
Chaisiri, S., Lee, B.S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. IEEE Trans. Serv. Comput. 5, 164–177 (2012)
Fard, H.M., Prodan, R., Fahringer, T.: A truthful dynamic workflow scheduling mechanism for commercial multicloud environments. IEEE Trans. Parallel Distrib. Syst. 24, 1203–1212 (2013)
Jangra, A., Saini, T.: Scheduling optimization in cloud computing. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3, 62–65 (2013)
Wang, X.J., Zhang, C.Y., Gao, L., Li, P.G.: A survey and future trend of study on multi-objective scheduling. In: Fourth IEEE International Conference on Natural Computation, pp. 382–391 (2008)
Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., Gannon, D., Goble, C., Livny, M., Moreau, L., Myers, J.: Examining the challenges of scientific workflows. IEEE Compu. Soc. 40, 24–32 (2007)
Szabo, C., Sheng, Q.Z., Kroeger, T., Zhang, Y., Yu, J.: Science in the cloud: allocation and execution of data-intensive scientific workflows. J. Grid Comput. (2013)
Singh, L., Singh, S.: A survey of workflow scheduling algorithms and research issues. Int. J. Comput. Appl. 0975–8887(74), 21–28 (2013)
Ramezani, F., Lu, J., Hussain, F.: Task Scheduling Optimization in Cloud Computing Applying Multi-objective Particle Swarm Optimization. Service-Oriented Computing. Lecture Notes in Computer Science 8274, pp. 237–251. Springer, Berlin (2013)
Fard, H.M., Prodan, R., Barrionuevo, J.J.D., Fahringer, T.: A Multi-objective approach for workflow scheduling in heterogeneous environments. In: 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 300–309 (2012)
Wen, Y., Chen, Z., Chen, T., Liu, J., Kang, G.: A particle swarm optimization algorithm for batch processing workflow scheduling. In: Second IEEE International Conference on Cloud and Green Computing, pp. 645–649 (2012)
Shi, Y.H., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 63–69 (1998)
Kumar, P., Anand, S.: An approach to optimize workflow scheduling for cloud computing environment. J. Theor. Appl. Inf. Technol. 57, 617–623 (2013)
Tawfeek, M.A., El-Sisi, A., Keshk, A.E., Torkey, F.A.: An Ant Algorithm for cloud task scheduling. In: Proceedings of International Workshop on Cloud Computing and Information Security (CCIS), pp. 169–172 (2013)
Shojaee, R., Faragardi, H.R., Alaee, S., Yazdani, N.: A new cat swarm optimization based algorithm for reliability-oriented task allocation in distributed systems. In: 6th IEEE International Symposium on Telecommunications, pp. 861–866 (2012)
Sharafi, Y., Khanesar, M.A., Teshnehlab, M.: Discrete binary cat swarm optimization algorithm. In: 3rd IEEE International Conference on Computer, Control & Communication (IC4), pp. 1–6 (2013)
Chu, S.C., Tsai, P.W.: Computational intelligence based on the behavior of cats. Int. J. Innov. Comput. Inf. Control 3, 163–173 (2007)
Pradhan, P.M. Panda, G.: Solving multiobjective problems using cat swarm optimization. Expert Syst. Appl. 39, 2956–2964 (2011)
Santosa, B., Ningrum, M.K.: Cat Swarm optimization for clustering. In: IEEE International Conference on Soft Computing and Pattern Recognition, pp. 54–59 (2009)
Tsai, P.W., Pan, J.S., Chen, S.M., Liao, B.Y., Hao, S.P.: Parallel cat swarm optimization. In: Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, pp. 3328–3333 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Bilgaiyan, S., Sagnika, S., Das, M. (2015). A Multi-objective Cat Swarm Optimization Algorithm for Workflow Scheduling in Cloud Computing Environment. In: Jain, L., Patnaik, S., Ichalkaranje, N. (eds) Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 308. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2012-1_9
Download citation
DOI: https://doi.org/10.1007/978-81-322-2012-1_9
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2011-4
Online ISBN: 978-81-322-2012-1
eBook Packages: EngineeringEngineering (R0)