Grid computing has emerged as the next generation computing platform. Because of the resource heterogeneity that exists in the grid environment, user jobs experience variable performance. Grid job scheduling, or selection of appropriate mappings between resources and the application, with the goal of leveraging available capacity and imposed requirements is thus at the heart of successful grid utilization. Grid job scheduling can be viewed as a function of resource heterogeneity, resource and application availability, and application options. This chapter presents work that incorporates all of these factors to provision and present individual users with alternative job options in terms of cost and time tradeoffs. Inherently, this leads to more effective scheduling policies. To support these aims, a framework is introduced with a novel scheduling methodology that introduces new user-scheduler interaction levels and a new layer of scheduling that includes application parameter selection and parameter value optimization.
Parallel Job Scheduling Policies to Improve Fairness: A Case Study
