Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing

摘要

To solve the problems of time consuming and high energy consumption of task scheduling in mobile cloud computing environment, a task scheduling strategy based on fruit fly optimization algorithm was proposed. First, establish a mobile cloud computing task scheduling model; second, in the fruit fly optimization algorithm, orthogonal arrays and quantization techniques are used to initialize the population. The exploration step is used to dynamically adjust to avoid individuals falling into a local optimum. Finally, in each iteration of the fruit fly optimization algorithm, a global search update is performed by introducing an artificial bee colony algorithm. In the simulation experiments, compared with the basic fruit fly optimization algorithm, the improved particle swarm algorithm, and the improved artificial bee colony algorithm, the algorithm in this paper has certain advantages in the comparison of the four indicators: completion time, cost, bandwidth and energy consumption. Besides, this algorithm can effectively improve the task scheduling efficiency under mobile cloud computing.