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Smart city article details
| Title | Performance And Energy-Aware Bi-Objective Tasks Scheduling For Cloud Data Centers |
|---|---|
| ID_Doc | 41699 |
| Authors | Materwala H.; Ismail L. |
| Year | 2021 |
| Published | Procedia Computer Science, 197 |
| DOI | http://dx.doi.org/10.1016/j.procs.2021.12.137 |
| Abstract | Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021 The Authors. Published by Elsevier B.V. |
| Author Keywords | Autonomous agents; Cloud computing; Energy-efficiency; Evolutionary algorithm; Genetic algorithm; Multi-objective optimization; Performance; Quality of service |
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