Grid Process Scheduling Optimization using the Tabu Search

Abstract

Process scheduling problems present a large solution space, which exponentially increases according to the number of computers and processes. In this context, exact approaches are, therefore, infeasible. This limitation motivated several works to consider meta-heuristics to optimize the search for good solutions. In that sense, this work proposes a new approach based on the Tabu Search to improve process scheduling by considering application knowledge and the logical partitioning of distributed environments. Such knowledge comprises historical application events (captured during execution) which allow a better parametrization of the optimizer and, consequently, generates better results. Simulation results confirm the contributions of this new approach, which outperforms other techniques when dealing with large and heterogeneous environments, such as Grids.