Performance Implications of Task Scheduling by Predicting Network Throughput on the Internet

A meta-scheduler is used to efficiently assign tasks to distributed resources. Additionally, the Internet is often used to share the data of tasks on the resources. Throughput prediction will play a crucial role in overcoming the instability of network resource on the Internet for the meta-scheduler. However, it is unclear that higher prediction accuracy should always guarantee better scheduling. In this paper, we focus on how predictors can affect the overall processing time of given tasks through meta-scheduler simulation. Real traces of throughput on the Internet are used to build CDF-based and SVR-based predictors, and these are adopted by the meta-scheduler. Through the simulation, the meta-scheduler using the predictor clearly reduces the processing time. Moreover, the meta-scheduler using the SVR-based, which performed better than CDF-based did in terms of throughput prediction, was observed to result in a reduction of up to 13.3% in the processing time compared to the meta-scheduler without any predictions. The expected value of performance improvement with the SVR-based predictor was calculated as 3.73%, while the value of CDF-based was 2.53%. On the other hand, the meta-scheduler using the predictor can seldom assign tasks to inappropriate sites due to only a few of inaccurate prediction results. As a result, the processing time is drastically increased in comparison with the meta-scheduler without any predictions.