A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid

An efficient function of a complicated or dynamic high performance computing environment requires the scheduler to dispatch the submitted tasks according to the identification of the idling resources. A derivative problem is to provide accurate forecasts of the tasks runtimes. This is usually needed to assist scheduling policies and fine tune scheduling decisions, and also used for future planning of resource allocation when conducting advance reservation. However, the characteristics of the existing prediction strategies determine that the sole strategy is not appropriate for all kinds of heterogeneous tasks. Aiming at this problem, a multi-strategy collaborative prediction model (MSCPM) for the runtime of online tasks is proposed, and a novel concept named Prediction Accuracy Assurance (PAA) as a criterion is introduced to quantitatively evaluate the precision of the prediction runtime provided by a specific prediction strategy. MSCPM uses the existing strategies of prediction runtime to generate multiple collaborative prediction schemes and takes the prediction result of the scheme which provides the optimal PAA. We evaluate the performance of the proposed model which recently integrates four simple yet widely used time series prediction strategies based on the gathered traces of three different tasks. The analysis results show that MSCPM can aggregate the superiority of the various existing prediction strategies and the evaluation criterion can pick out the near-optimal one within the prediction results provided by the integrated strategies. MSCPM provides an enhanced accuracy assurance for the prediction runtime of the online tasks in the computing environments.