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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 resultsshow that the M-PSO can efficiently reduce total cost compared with other algorithms.

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