Abstract
Energy consumption is a big problem for cloud computing. Different task-scheduling algorithms in cloud computing take a different amount of energy, resources, and time for executing. In this paper, our goal is to minimize the consumption of energy within a short executing time and make a high resource utilizing a task scheduling approach which will help to make sustainable green cloud computing. The common scheduling is particle swarm optimization (PSO) and ant colony optimization (ACO). This algorithm is used in the cloud. In this paper, our goal is to combine these two algorithms and make a new approach for scheduling which performance is better than the algorithms. So, our new approach is the PSAC algorithm which is a combination of PSO and ACO. PSAC approach reduces energy consumption and takes less time for execution. It also uses more resources than two algorithms. The result of the new hybrid algorithm takes less energy so energy consumption is reduced and less time for task execution. Moreover, it can utilize more resources. The result shows that the hybrid PSAC algorithm can perform the same number of tasks using lower energy and execution time than the PSO and the ACO algorithm and also utilizes more resources than the two algorithms.
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Akter, S., Khan, M.H., Nishat, L., Alam, F., Reza, A.W., Arefin, M.S. (2023). A Hybrid Approach for Improving Task Scheduling Algorithm in the Cloud. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-031-50151-7_18
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