An Improved Ant Colony Optimization Job Scheduling Algorithm in Fog Computing

EasyChair Preprint 4015

Abstract

With the application of internet of things technology, fog computing has provided computing and storage services near the bottom network. It can solve the problem of rapid response and bandwidth consumption of delay sensitive applications at the edge of local network as a highly virtualized platform. However, in the case of large scale service requests, if the job scheduling problem cannot be effectively solved, it will increase service delay, reduce resource utilization and user satisfaction. In this paper, we have improved the basic ant colony optimization (ACO) and developed a new job scheduling strategy improved ant colony optimization named IACO. IACO can assign to the resource with the lowest total cost of all selected tasks, which is always determined by calculating the total cost value of the task on the resource. We have finished some experiments by imitating the foraging process of multiple ants and repeated it iteratively. The optimal task scheduling sequence can be obtained through the different pheromone concentration left by ants. Experimental results show that IACO scheduling algorithm is better than ACO in the total cost, completion time and economic cost.

Keyphrases: Fog Computing, IACO, Job Schedule

@booklet{EasyChair:4015,
  author    = {Chao Yin and Tongfang Li and Xiaoping Qu and Sihao Yuan and Yan Liu},
  title     = {An Improved Ant Colony Optimization Job Scheduling Algorithm in Fog Computing},
  howpublished = {EasyChair Preprint 4015},
  year      = {EasyChair, 2020}}