Multi-objective cloud workflow scheduling algorithm based on grid variance - Xiaoan BAO; Yundi CAO; Na ZHANG; Junyan QIAN; Jianwen CAO (Dianxin kexue)
Multi-objective cloud workflow scheduling algorithm based on grid variance and the strategy of bad particles self-learning were presented.Firstly,the characteristics of task scheduling was token into consideration,and particle encoding was discredited.Secondly,the strategy of mapping Pareto optimal workflow scheduling set to self-adaptive grid coordinate system,and calculating the grid distribution value of each Pareto optimal solution was used.Thirdly,grid variance was adopted to evaluate the diversity of current Pareto front and dynamically adjust evolution strategies.Finally,the concept of being dominated times was introduced into bad particles self-learning strategy for filtering out bad particles in population.The simulation experiment shows that workflow scheduling solution set by this algorithm is better than the MOPSO algorithm on both IGD and S performance indexes,and the optimal value is superior to the ε-FDPSO and NSGA-Ⅱ algorithm.