A Comparison Study on the Performance of Population-based Meta-Heuristics for Independent Batch Scheduling in Grid Systems

There has been a lot of research recently devoted to scheduling and resource allocation in Grid systems. Research efforts have been done in particular to the use of heuristic and meta-heuristic approaches in the design of efficient Grid schedulers. In this paper we present a comprehensive study on the performance of different population-based heuristic methods, namely Genetic Algorithms, Memetic Algorithms and Cellular Memetic Algorithms for the problem. The aim is to shed light on the advantages and limitations of different population based methods as well as their hybridization with local search methods, such as Tabu Search, when solving the multi-objective version of the problem under execution time restrictions of Grid schedulers. We considered a set of scenarios that represent a high variation regarding the size of entries and static/dynamic features aiming to judge on the robustness with regard to the quality of the solutions obtained by the considered methods. These scenarios are divided into static, which provides a single set of tasks and resources for each entry, and dynamic, using a grid simulator used to observe the behavior of heuristics in Grid environments in real time.