Long-Term CPU Load Prediction System for Scheduling of Distributed Processes and its Implementation

Abstract

There exist distributed processing environments composed of many heterogeneous computers. It is required to schedule distributed parallel processes in an appropriate manner. For the scheduling, prediction of execution load of a process is effective to exploit resources of environments. We propose long-term load prediction methods with references of properties of processes and of runtime predictions. Since an appropriate prediction method is different according to the situation, we propose a prediction module selection to select an appropriate prediction method according to a state of changing CPU load using a neural network. We also discuss about the implementation of a long-term CPU load prediction system, which provides information including prediction of load for schedulers, system administrators and users.