Fuzzy scheduling with swarm intelligence-based knowledge acquisition for grid computing

In spite of the existence of a large diversity in literature related to scheduling algorithms in computational grids, there are only a few efficiently dealing with the inherent uncertainty and dynamism of resources and applications of these systems. Further, the need to meet both users and providers QoS requirements, such as tardiness or resource utilization, calls for new adaptive scheduling strategies that consider current and future status of the grid. Fuzzy Rule-Based Systems (FRBSs) are knowledge based systems that are recently emerging as an alternative for the development of grid scheduling middleware. Their main strength resides in their adaptability to changes in environment and their ability to model vagueness. However, since their performance strongly depends on the quality of their acquired knowledge, new automatic learning strategies are pursued. In this work, a FRBS meta-scheduler for scheduling jobs in computational grids is suggested which incorporates a novel knowledge acquisition method based on Swarm Intelligence. Simulations results show that the fuzzy meta-scheduler improves six classical queued-based and scheduled-based approaches present in today's production systems and it is able to easily adapt to changes in the grid conditions.