Workload prediction using run-length encoding for runtime processor power management

Abstract

In processor power management research, workload prediction is a requisite for adjusting frequency without performance loss, and previous studies have proposed various prediction algorithms. Among them, methods based on the workload history table are lightweight and have high prediction accuracy for a variable workload. However, such prediction algorithms lose their prediction accuracy in the case of repeated workload. An improved workload prediction method using run-length encoding is proposed, which handles workload repetition. Evaluation results show that the proposed algorithm improves the prediction of repeated workload by up to 14% and also improves 4% of energy saving.