Abstract
Effective scheduling of cloud tasks is very much essential for a cloud computing environment. The cloud tasks are the user requests to be processed in a cloud environment. A number of cloud resources are consumed to process cloud tasks. Task scheduling optimizes the consumption of cloud resources and reduces the makespan time. The paper is aimed at reducing the makespan time in a cloud environment by introducing the new method Modified Greedy Particle Swarm Optimization with Clustered Approach (MGPSOC). The MGPSOC algorithm makes use of clustering with bio-inspired techniques. The proposed method showed good results when compared with the existing algorithm Greedy Particle Swarm Optimization Algorithm (G&PSO).
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Home Prasanna Raju, Y., Devarakonda, N. (2021). Greedy-Based PSO with Clustering Technique for Cloud Task Scheduling. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_12
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DOI: https://doi.org/10.1007/978-981-15-8767-2_12
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