Affiliations: [a] Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| [b] Department of Geography, San Diego State University, San Diego, CA, USA
Correspondence:
[*]
Corresponding author. Peng Liang, Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China. E-mail: [email protected].
Abstract: When the network resource information is scheduled with the current algorithm, the execution time of the resource scheduling task cannot be improved. The utilization of network resources is reduced in the case of the heavy scheduling task. To address this problem, a network resource information scheduling based on non-convex function optimization algorithm is proposed in this paper. The network resource is modeled as a non-convex function. The execution interval of task is divided into subspaces of multiple units. Task density is introduced into network resource scheduling model. In this model, computing resources and storage resources of the network are considered. Ant colony particle swarm optimization algorithm is used for scheduling with the built network resource scheduling model. The initial solution is obtained by initial search with the particle swarm algorithm. Then the initial solution is transformed into the initial pheromone distribution of the ant colony. The resource information is searched by using ant colony algorithm until the optimal solution is found, so as to achieve network resource information scheduling. Experimental results show that the proposed algorithm can reduce the execution time of task and improve the utilization rate of network resource information.
Keywords: Non-convex function, network resource, information scheduling, joint scheduling algorithm
