Cloud resource scheduling method based on estimation of distirbution shuffled frog leaping algorithm

Focusing on the problem of high efficiency resource scheduling in cloud computing environment, since current resource scheduling algorithm has been less given consideration to the shortest completion time and the least cost of the services, the fitness function which can comprehensive response the time and cost was designed, and an estimation of distribution-shuffled frog leaping algorithm (EDSFLA) was proposed. This algorithm redefined the evolutional operators of shuffled frog leaping algorithm (SFLA), utilizing the cross operators of genetic algorithm, targeted in applying to scheduling problem with integer-coded. EDSFLA introduced evolutionary strategy of estimation of distribution to break the confine of search pattern in the standard SFLA, and made the learning ability of this algorithm more comprehensive. Experimental results show that this algorithm has more capability in convergence and searching ability compared with the standard SFLA and EDA in solving cloud resource scheduling problems.