Junwei Ge / School of Computer Science and Technology, Chongqing University of Posts and Telecommunications,
Shuo Sheng / School of Computer Science and Technology, Chongqing University of Posts and Telecommunications
Yiqiu Fang / School of Computer Science and Technology, Chongqing University of Posts and Telecommunications,
Cloud computing resource scheduling is a complex NP problem, and is difficult to solve . In order to shorten the time in completing tasks, an improved LDW-PSO (linearly decreasing weight-particle swarm optimization) algorithm is proposed applied in cloud resource scheduling. Due to the fact that PSO algorithm is easy to fall into local convergence, Firstly,based on the linearly decreasing weight strategy, the constant disturbance is added to increase the inertia weight, so as to get rid of the local search and begin the global search.Secondly, in order to avoid the situation that particles highly gather around the optimal particle,resulting in being similar and damaging the diversity of particle swarm,thus,By changing inertia weight by mixed with random individuals self-adaption in a certain probability, it could better maintain the diversity of the population.Finally, through different simulation test on Matlab2010a platform, it proves that the improved LDW-PSO algorithm can get a more accurate solution and optimize the completion time in cloud computing resource scheduling.