Bio-inspired techniques applied to meta-schedulers based on fuzzy rules in grid computing

There exists a wide set of scheduling approaches in literature for grid computing. However, it is still necessary to make efforts to obtain scheduling strategies able to manage the inherent uncertainty and dynamism of grids in order to meet QoS requirements of both users and network administrators. In this regard, Fuzzy Rule-Based Systems are expert systems that are increasingly arising as an alternative for the development of grid scheduling systems, mainly due to their adaptability to environments dynamism and capability to cope with uncertainty in systems information. Nevertheless, bearing in mind that these systems performance is strongly related to the quality of their acquired knowledge, new learning strategies are sought. In this work, a collection of learning strategies for knowledge bases in grid computing scheduling systems are presented: strategies based on Genetic Algorithms, Differential Evolution and a novel strategy, Knowledge Acquisition with a Swarm Intelligence Approach founded on Particle Swarm Optimization. Also, simulation results illustrating the feasibility of these strategies in different grid scenarios are shown.