Research on Balanced Scheduling Algorithm of Big Data in Network Under Cloud Computing

Abstract

In order to find the optimal big data balanced scheduling scheme under cloud computing and reduce the completion time of the task, an improved ant colony algorithm based algorithm for large data equalization scheduling under cloud computing was proposed. Firstly, a balanced scheduling algorithm structure was established, then the equilibrium problem to be explored was described, finally, the ant colony algorithm was used to simulate the ant search food process to solve the objective function. And the local and global information deep update methods was introduced to improve, speed up the search speed, and finally the performance test experiments on CloudSim simulation platform was performed. The results show that compared with the discrete particle swarm optimization (DPSO), the algorithm not only greatly reduces the execution time of cloud computing tasks (2.5 s), but also solves the problem of unbalanced data load, and achieves the balanced scheduling of large network data under cloud computing.