DOI: 10.7763/IJCTE.2016.V8.1012
Genetic Algorithm Based Bi-Objective Task Scheduling in Hybrid Cloud Platform
Abstract—Hybrid cloud is a type of the general cloud computing platform, that is composed of both public and private cloud. Scheduling plays a key role in the efficient use of hybrid cloud resources. In this paper, focus is on a scheduling algorithm for hybrid cloud that tries to optimize both execution time and cost. Execution time and cost are conflicting objectives, i.e. when one is made better, the other becomes worse off. Multiobjective evolutionary algorithm is used to find the optimal schedule. The widely used scheduling implementations seen in hybrid cloud try to optimize either execution time or cost, but not both simultaneously. The proposed algorithm is compared with the more widely used scheduling optimization techniques and seen to have much better performance.
Index Terms—Evolutionary optimization algorithms, hybrid cloud, pareto-optimality, scheduling.
Leena V. A. and Ajeena Beegom A. S. are with the College of Engineering, Trivandrum, Kerala, India (e-mail: leena.jaleel@gmail.com, ajeena@cet.ac.in).
Rajasree M. S. is with IIITM-K, Kerala, India (e-mail: rajasree.ms@iiitmk.ac.in).
Cite:Leena V. A., Ajeena Beegom A. S., and Rajasree M. S., "Genetic Algorithm Based Bi-Objective Task Scheduling in Hybrid Cloud Platform," International Journal of Computer Theory and Engineering vol. 8, no. 1, pp. 7-13, 2016.