Abstract
Nowadays, diverse areas in science as high energy physics, astronomy or climate research are increasingly relying on experimental studies addressed with hard computing simulations that cannot be faced with traditional distributed systems. In this context, grid computing has emerged as the new generation computing platform based on the large-scale cooperation of resources. Furthermore, the use of grid computing has also been extended to several technology, engineering or economy areas such as financial services and construction engineering that demand high computer capabilities. Nevertheless, a major issue in the sharing of resources is the scheduling problem in a high-dynamic and uncertain environment where resources may become available, inactive or reserved over time according to local policies or systems failures. In this paper, a review of scheduling strategies dealing with uncertainty in systems information by the application of techniques such as fuzzy logic, neural networks or evolutionary algorithms is presented. Furthermore, this work is centered on the study of scheduling strategies based on fuzzy rulebased systems given their flexibility and ability to adapt to changes in grid systems. These knowledge-based strategies are founded on a fuzzy characterization of the system state and the application of the scheduler knowledge in the form of fuzzy rules to cope with the imprecise environment. Obtaining good rules also arises as a challenging problem. Hence, the main learning methods that allow the improvement and adaptation of the expert schedulers are introduced.
Comparative Analysis of Job grouping based Scheduling Strategies in Grid Computing
