Improved MOPSO Algorithm Based on Map-Reduce Model in Cloud Resource Scheduling

A multi-objective resource scheduling model with quality of service (QoS) restriction was built to improve the computing efficiency of Map-Reduce resource scheduling. The model considered the scheduling problem of both Map and Reduce phase and a chaotic multi-objective particle swarm algorithm was proposed to solve the model. The information entropy theory was used to maintain nondomination solution set by the algorithm so as to retain the diversity of solution and the uniformity of distribution. By Sigma methods to achieve fast convergence, chaotic disturbance mechanism was introduced to improve the diversity of the population and the ability of global optimization algorithm, which can avoid the algorithm from falling into local extremism. The experiments show that the number of iteration in the algorithm obtaining solutions is little and nondomination solutions distribute equably. In solving Map-Reduce resource scheduling problems, it indicates that the astringency and the diversity of solution set of this algorithm are better than the traditional multi-objective particle swarm algorithm.