Abstract
In Cloud computing environments, computing resources are available for users, and they only pay for used resources The most important issues in cloud computing are scheduling and energy consumption which many researchers worked on them. In these systems a scheduling mechanism has two phases: task prioritization and processor selection. Different priorities may cause to different makespan and for each processor which assigned to the task, the energy consumption is different. So a good scheduling algorithm must assign priority to each task and select the best processor for them, in such a way that makespan and energy consumption be minimized. In this paper, we proposed a two phase’s algorithm for scheduling, named TETS, the first phase is task prioritization and the second phase is processor assignment. We use three prioritization methods for prioritize the tasks and produce optimized initial chromosomes and assign the tasks to processors which is an energy-aware model. Simulation results indicate that our algorithm is better than previous algorithms in terms of energy consumption and makespan. It can improve the energy consumption by 20 % and makespan by 4 %.
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Shojafar, M., Kardgar, M., Hosseinabadi, A.A.R., Shamshirband, S., Abraham, A. (2016). TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_9
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DOI: https://doi.org/10.1007/978-3-319-27221-4_9
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