Abstract: To improve the computing efficiency of Map-Reduce resource scheduling,a multi-objective resource scheduling model with QoS restriction was built.The model considers the scheduling problem of both Map and Reduce phase.A chaotic multi-objective particle swarm algorithm was proposed to solve the model.The algorithm uses the information entropy theory to maintain non-dominated solution set so as to retain the diversity of solution and the uniformity of distribution.On the basis of using Sigma methods to achieve fast convergence,chaotic disturbance mechanism was introduced to improve the diversity of population and the ability of algorithm global optimization,which can avoid the algorithm to fall into local extremism.The experiments show that the number of iteration in the algorithm obtaining solutions is little and non-dominated solutions distribute equably.It indicates that the astringency and the diversity of solution set of this algorithm are better than the traditional multi-objective particle swarm algorithm in solving Map-Reduce resource scheduling problems.

Key words: Cloud computing,Map-Reduce,Resource scheduling,Particle swarm algorithm,Information entropy,Chaotic disturbance