A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing

作者：Prado R P; Garcia Galan S^*; Yuste A J; Munoz Exposito J E

来源：Engineering Applications of Artificial Intelligence, 2010, 23(7): 1072-1082.

DOI：10.1016/j.engappai.2010.07.002

• Grid computing
• Scheduling
• Fuzzy rule-based systems
• Evolutionary algorithms
• Genetic fuzzy systems

Grid computing is increasingly emerging as a promising platform for large-scale problems solving in science, engineering and technology. Nevertheless, a major effort is still required to harness the high potential performance of such computational framework and in this sense, an important challenge is to develop new strategies that efficiently address scheduling on the distributed, heterogeneous and shared environment of grids. Fuzzy rule-based systems (FRBSs) models are dynamic and are currently attracting the interest of scheduling research community to obtain near-optimal solutions on grids. However, FRBSs performance is strongly related to the quality of their knowledge bases and thus, with the knowledge acquisition process. Due to the inherent dynamic nature and the typical complex search spaces of grids, automatically finding a high-quality knowledge base that accurately describes the fuzzy system is extremely relevant. In this work, we propose a scheduling system for grids considering a novel learning strategy inspired by Michigan and Pittsburgh approaches that applies genetic algorithms (GAs) to evolve the fuzzy rule bases and improves the classical learning strategies in terms of computational effort and convergence behaviour. In addition, experimental results show that the proposed schema significantly outperforms other extensively used scheduling strategies.

• 出版日期2010-10