An effective scheduling strategy based on hypergraph partition in geographically distributed datacenters

Abstract

As the complexity of workflow applications increase, the scheduling and execution of workflow incur more waste of resources. In order to achieve load balancing and reduce the task execution time in the cloud system, an effective scheduling strategy based on hypergraph partition for workflow application in the geographically distributed datacenters is proposed. Firstly, a workflow job scheduling algorithm is proposed, which aims to reduce the response time and considers the cloud state. Besides, the task scheduling algorithm based on the hypergraph partition is designed with the goal of reducing the completion time and energy consumption for the tasks. In addition, the optimal task scheduling strategy can be obtained according to the Dijkstra shortest path algorithm based on Fibonacci heap. Finally, in the experiment, the workflow task scheduling algorithm can optimize the task execution performance and maintain the load balance of the computing nodes in each cloud, so that the average execution time of the tasks and the total energy consumption of the system are minimized.