QoS-constrained stochastic workflow scheduling in enterprise and scientific Grids

Grid computing infrastructures are inherently dynamic and unpredictable environments shared by many users. Grid schedulers aim to make efficient use of Grid resources while providing the best possible performance to the Grid applications and satisfying the associated performance and policy constraints. Additionally, in commercial Grid settings, where the Grid resource brokering becomes an increasingly important part of Grid scheduling, it is necessary to minimise the cost of application execution on the behalf of the Grid users, while ensuring that the applications meet their QoS constraints. Efficient resource allocation could in turn also allow the resource broker to maximise it's profit by minimising the number of resources procured. Scheduling in such a large-scale, dynamic and distributed environment is a complex undertaking. In this paper, we propose an approach to Grid scheduling which abstracts over the details of individual applications, focusing instead on the global cost optimisation problem and the scheduling of the entire Grid workload. Our model places particular emphasis on the stochastic and unpredictable nature of the Grid, leading to a more accurate reflection of the state of the Grid and hence more efficient and accurate scheduling decisions.