A new task scheduling algorithm using dynamic prediction adjustment and task flow shaping for grid computing

According to the autonomy, heterogeneity, and distributed nature of grid computing system, we propose a new grid scheduling algorithm based to reduce average task response time with dynamic prediction adjustment and task flow shaping. The algorithm uses history data and the recent task request time, task completion time, and network communication delay, to predict the future task response time at each computing node. Based on the prediction result, tasks will be assigned to computing nodes which are predicted to have less work load and better. performance in the near future. The resource utilization can be improved with both the dynamic adaptation algorithm and the task flow shaping algorithm. Experimental results show that our approach outperforms existing scheduling algorithms (e.g. random scheduling) in terms of task response time and throughput.