Journal
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Volume 30, Issue 24, Pages -Publisher
WILEY
DOI: 10.1002/cpe.4970
Keywords
cloud computing; inertia weight; particle swarm optimization; task scheduling
Funding
- National Natural Science Foundation of China [61572525, 61772088, 61373042, 61602525, 61404213]
- State Open Foundation of State Key Laboratory of Networking and Switching Technology [KLNST-2016-2-23]
- Science and Technology Plan Project of Changsha City [k1705036]
- Next Generation Internet Innovation Project of CERNET [NGII20160204]
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With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M-PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M-PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M-PSO can efficiently reduce total cost compared with other algorithms.
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