Amodified PSO algorithm for task scheduling optimization in cloud computing

摘要

With the increasing scale of tasks in cloud computing, the problem of high energy consumptionbecomes increasingly serious. To deal with the problem, we propose a cloud computing energyconsumption 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 namedmodified particleswarmoptimization (M-PSO) to handle the local optimumand slow convergenceproblem. Different from the PSO,M-PSO can dynamically adjust the inertia weight coefficient toimprove the speed of convergence according to the number of iterations. Finally, the performanceof 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.