Research on cloud computing resource scheduling based on improved ant colony optimization algorithm
Chenyue Xia, Rui Wang, Zhuofu Deng, Zheng Yingnan · 2022
With the rise of cloud computing services around the world, how to reduce the load balancing degree of cloud computing resource scheduling and improve the utilization rate of cloud computing resources has gradually attracted the attention of the academic community. In order to meet these needs, I proposed to use ant colony optimization algorithm scheduling to deal with large-scale cloud computing resource data sets. Meanwhile, in order to prevent the ant colony optimization algorithm I proposed from falling into the local optimal solution, I proposed to improve the probability and heuristic factor of selecting the next node of ant colony optimization algorithm as well as the update of pheromone, so as to give full play to the guiding role of pheromone to achieve the optimal cloud computing resource scheduling scheme. Examples have proved that the algorithm proposed by me has low relative standard deviation and load balancing degree. Meanwhile, the proposed scheduling scheme can evenly allocate resources, and the total utility value of scheduling is high, which can improve the scheduling ability of cloud computing resources to the greatest extent. At the same time, the fewer iterations required to schedule resources, the better the convergence of the algorithm. It can be seen that this algorithm can significantly improve the potential of cloud computing resource scheduling.