Research on Optimal Scheduling in Cloud Computing

Abstract

Aiming at the problem of unbalanced load and slow convergence speed of task scheduling based on ant colony algorithm, an improved task scheduling optimization algorithm is proposed. The pheromone update rules of ant colony algorithm are optimized by giving weight to speed up the solution speed. The comprehensive performance of the algorithm is optimized by dynamically updating the volatilization coefficient, and in the updating process of local pheromone. The load weight coefficient of virtual machine is introduced to ensure the load balance of virtual machine. The experimental results show that the task scheduling strategy of the improved algorithm can not only ensure the reasonable allocation of tasks, but also improve the convergence speed and shorten the total execution time.