Tasks Scheduling Based on Neural Networks in Grid
Jingbo Yuan, Shunli Ding, Cuirong Wang · 2007
Grid infrastructures have been used to solve large scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. Predicting the runtime of a task, an important component of the resource management, plays an important role in the task scheduling and the resource using in computational grid. This paper presents a predicting model of task's runtime based on BP neural networks considering several factors which is the heart of any scheduling and resource allocation algorithm. The method has many advantages including small network structure, quick learning and use conveniently etc. This paper presents also a scheduling algorithm considering task's user deadline. The experiment results indicate that the method is effective and has higher accuracy.