Analyses toward a prediction system for a large-scale volunteer computing system

Computing resources in volunteer computing grid represent a big under-used reserve of processing capacity. However, a task scheduler has no guarantees regarding the deliverable computing power of these resources. Predicting CPU availability can help to better exploit these resources and make effective scheduling decisions. In this paper, we draw up the main guidelines to develop a scalable method to predict CPU availability in a large-scale volunteer computing system. To reduce solution time and ensure precision, we use simple prediction techniques precisely Autoregressive models and tendency-based strategy. To address the limitations of autoregressive models, we propose an automated approach to check whether time series satisfy the assumptions of the models and to construct a prediction model by identifying its appropriate order value. At each prediction, we consider autoregressive models over three different past analyses: first over the recent hours, second during the same hours of the previous days and third during the same weekly hours of the previous weeks. We analyze the performance of multivariate vector autoregressive models (VAR) and pure autoregressive models (AR), constructed according to our approach, against the tendency prediction technique using traces of a large-scale Internet-distributed computing system, termed seti@home.