Abstract
Task scheduling in Cloud computing is a challenging aspect due to the conflicting requirements of end users of cloud and the Cloud Service Provider (CSP). The challenge at the CSP’s end is to schedule tasks submitted by the cloud users in an optimal way such that it should meet the quality of service (QoS) requirements of the user at one end and the running costs of the infrastructure to a minimum level at the other end for better profit. The focus is on two objectives, makespan and cost, to be optimized simultaneously using meta heuristic search techniques for scheduling independent tasks. A new variant of continuous Particle Swarm Optimization (PSO) algorithm, named Integer-PSO, is proposed to solve the bi-objective task scheduling problem in cloud which out performs the smallest position value (SPV) rule based PSO technique.
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References
Szabo, C., Kroeger, T.: Evolving multi-objective strategies for task allocation of scientific workflows on public clouds. In: Proc. of IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2012)
Farahabady, R.H., Lee, Y.C., Zomaya, A.Y.: Pareto optimal cloud bursting. Accepted for publication in IEEE Transactions on Parallel and Distributed Systems (2013)
Feng, M., Wang, X., Zhang, Y., Li, J.: Multi-objective particle swarm optimization for reseource allocation in cloud computing. In: Proc. of 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS), vol. 3, pp. 1161–1165 (2012)
Guo, L., Shao, G., Zhao, S.: Multi-objective task assignment in cloud computing by particle swarm optimization. In: Proc. of 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 1–4 (2012)
Jin, J., Luo, J., Song, A., Dong, F., Xiong, R.: Bar: An efficient data locality driven task scheduling algorithm for cloud computing. In: Proc. of 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 295–304 (2011)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Rodrignez Sossa, M., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Transactions on Cloud Computing (2014)
Liang, Y.-C., Tasgetiren, M.F., Sevkli, M., Gencylmaz, G.: Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: IEEE Congress on Evolutionary Computation (CEC2004), vol. 2, pp. 1412–1419 (2004)
Marler, T., Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Springer (2009)
Sidhu, M.S., Thulasiraman, P., Thulasiram, R.K.: A load-rebalnce pso heuristic for task matching in heterogeneous computing systems. In: IEEE Symposium on Swarm Intelligence (SIS), pp. 180–187 (2013)
Sadhasivam, G.S., Selvarani, S.: Improved cost-based algorithm for task scheduling in cloud computing. In: Proc. of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5 (2010)
Ivan, P.: Stanimirovic, Milan Lj. Zlatanovic, and Marko D Petkovic: On the linear weighted sum method for multi-objective optimization. FACTA UNIVERSITATIS (NIS), Ser. Math. Inform, 49–63 (2011)
Wu, Z., Ni, Z., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: Proc. of International Conference on Computational Intelligence and Security (CIS), pp. 184–188 (2010)
Zhang, L., Chen, Y., Sun, R., Jing, S., Yang, B.: A task scheduling algorithm based on pso for grid computing. International Journal of Computational Intelligence Research 4(1), 37–43 (2008)
Zhu, L., Wu, J.: Hybrid particle swarm optimization algorithm for flexible task scheduling. In: Proc. of 3rd Internatinal Conference on Genetic and Evolutionary Computing (WGEC 2009), pp. 603–606 (2009)
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Beegom, A.S.A., Rajasree, M.S. (2014). A Particle Swarm Optimization Based Pareto Optimal Task Scheduling in Cloud Computing. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_10
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DOI: https://doi.org/10.1007/978-3-319-11897-0_10
Publisher Name: Springer, Cham
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