Task Scheduling with Multi-strategy Improved Sparrow Search Algorithm in Cloud Datacenters

Abstract

How to efficiently schedule tasks is the focus of cloud computing. Combining the task scheduling characteristics of the cloud computing environment, a multi-strategy improved sparrow search algorithm (MISSA) that takes into account task completion time, task completion cost and load balancing is proposed. First, the initialization of the population using piecewise linear chaotic map (PWLCM) enhances the degree of individual dispersion. After that, the global search phase in the marine predator algorithm (MPA) is incorporated to increase the scope of the search space. The introduction of dynamic adjustment factors in the joiner part strengthens the search ability of the algorithm in the early stage and the convergence ability in the late stage. Finally, the greedy strategy is used to update the joiner’s position so that the information of the optimal solution and the worst solution can be uesd to guide the next generation of position updates. Using CloudSim for simulation, the experimental results show that the proposed algorithm has a shorter task completion time and a more balanced system load. Compared with the ant colony optimization (ACO), MPA, and sparrow search algorithm (SSA), the MISSA improves the integrated fitness function values by 20$\%$, 22$\%$, and 17$\%$, confirming the feasibility of the proposed algorithm.