Abstract
Cloud Computing offers users pay as you go model to provision resources as needed. This model helped customers to utilize the cloud to execute tasks that are either independent or dependent. The dependent tasks termed as workflows are to be executed meeting the QoS requirements. Many approaches were presented in the literature to address this challenge. Heuristic and meta-heuristic Algorithms such as HEFT, SHEFT, ILP, GA, PSO and ACO are implemented to address workflow scheduling different QoS parameters. In this paper, we propose to use the particle swarm optimization technique to assign tasks to virtual machines to minimize makespan, cost and Schedule length Ratio (SLR). The makespan and cost are two primary goals in the current workflow scheduling. The proposed method implemented on cloudsim, the makespan time and cost parameters of the proposed approach outperform the other algorithms.
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Sriperambuduri, V.K., Nagaratna, M. (2023). A Modified-PSO Algorithm to Schedule Scientific Workflows in Cloud. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2746-3_48
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DOI: https://doi.org/10.1007/978-981-99-2746-3_48
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