Prediction f Based Models for Evaluating Backfilling Scheduling Policies

• Authors: F. Guim;J. Corbalan;J. Labarta
• Venue: PDCAT '07 Proceedings of the Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies
• Year: 2007

The research on the usage of prediction techniques in HPC scheduling policies rather than user estimates has increased it relevance these recent years. In the coming scheduling architectures, like Grids and very heterogeneous computational resources, such techniques are having a cru- cial relevance due to users in most of the cases will not have enough information or enough skills for specify for how long will their jobs run. Many studies have analyzed the impact of the user run- time estimates accuracy in the performance of the schedul- ing policies. Using user runtime estimation models, such as the f-model, researchers have evaluated how the accu- racy of the runtime estimates provided by the user at the job submission can affect the performance of the backfill- ing policies and its variants. However, these traditional es- timation models can not applied to backfilling scheduling policies that use runtime predictions rather than user esti- mates. Clearly, predictions can not be characterized with these models. For instance because the underestimation of the runtime is not considered by them and obviously it can occurs. In this paper we describe and evaluate a set of f-model based prediction models that characterize the behavior that prediction techniques have shown in HPC centers. They have been designed for evaluate scheduling policies that use predictions rather than user estimates.