A Balanced Scheduling Method of Smart City Enterprise Resource Information Based on Improved Ant Colony Algorithm

Abstract

Abstract In order to address the problems of low resource utilization rate and poor scheduling balance in current enterprise resource information balanced scheduling, an enterprise resource information balanced scheduling method based on an improved ant colony algorithm (ACO) was proposed. The ACO framework is introduced in this algorithm to establish a balanced scheduling model of enterprise resource information. By adding a mapping algorithm of a virtual machine and physical machine, an improved algorithm of load balancing between nodes is proposed based on the ACO. Forward ants detect node types, record node information, and leave foraging pheromones when they encounter load nodes. The backward ants trace back to the load node according to the tracking pheromone and allocate the overloaded node task reasonably. In the search process, the path pheromone is dynamically modified according to the node type, and the analysis of enterprise resource information balance scheduling algorithm is completed. The experimental results show that this method has good balance of resource information scheduling and can effectively improve resource utilization.