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# Self-Adaptive PSO Memetic Algorithm For Multi Objective Workflow Scheduling in Hybrid Cloud

Cloud computing is a technology in distributed computing that facilitate pay per model to solve large scale
problems. The main aim of cloud computing is to give optimal access among the distributed resources. Task scheduling in
cloud is the allocation of best resource to the demand considering the different parameters like time, makespan, cost,
throughput etc. All the workflow scheduling algorithms available cannot be applied in cloud since they fail to integrate the
elasticity and heterogeneity in cloud. In this paper, the cloud workflow scheduling problem is modeled considering make span,
cost, percentage of private cloud utilization and violation of deadline as four main objectives. Hybrid approach of Particle
Swarm Optimization (PSO) and Memetic Algorithm (MA) called Self-Adaptive Particle Swarm Memetic Algorithm (SPMA) is
proposed. SPMA can be used by cloud providers to maximize user quality of service and the profit of resource using an
entropy optimization model. The heuristic is tested on several workflows. The results obtained shows that SPMA performs
better than other state of art algorithms.

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[16] Zuo X., Zhang G., and Tan W., “Self Adaptive Learning PSO- Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 2, pp. 564-573, 2014. Self-Adaptive PSO Memetic Algorithm For Multi Objective Workflow Scheduling... 935 Padmaveni Krishnan received ME degree in computer Science from Madurai Kamaraj University in 2002. She is currently an Assistant Professor in Hindustan Institute of Technology and Science and her main interest are in virtualization and scheduling in cloud. John Aravindhar received PhD degree in Data mining from Hindustan University. He is currently an Associate Professor in Computer Science Department of Hindustan Institute of Technology and Science. His area of interest are Data mining and cloud.