This dissertation discusses policy-based scheduling techniques on heterogeneous resource for grid computing. The proposed scheduling algorithm has the following features, which can be utilized on the grid computing environment. First, the algorithm supports the resource usage constrained scheduling. A grid consists of the resources that are owned by decentralized institutions. Second, the algorithm performs the optimization-based scheduling. It provides an optimal solution to the grid resource allocation problem. Third, the algorithm assumes that a set of resources is distributed geographically and is heterogeneous in nature. Fourth, the scheduling dynamically adjusts to the grid status. It tracks the current workload of the resources. The performance of the proposed algorithm is evaluated with a set of predefined metrics. In addition to showing the simulation results for the out-performance of the policy-based scheduling, a set of experiments is performed on open science grid (OSG). In this dissertation we discuss a novel framework for policy based scheduling in resource allocation of grid computing. The framework has several features. First, the scheduling strategy can control the request assignment to grid resources by adjusting resource usage accounts or request priorities. Second, efficient resource usage management is achieved by assigning usage quotas to intended users. Third, the scheduling method supports reservation based grid resource allocation. Fourth, the quality of service (QOS) feature allows special privileges to various classes of requests, users, groups, etc. This framework is incorporated as part of the SPHINX scheduling system that is currently under development at the University of Florida. Experimental results are provided to demonstrate the usefulness of the framework. A grid consists of high-end computational, storage, and network resources that, while known a priori, are dynamic with respect to activity and availability. Efficient scheduling of requests to use grid resources must adapt to this dynamic environment while meeting administrative policies. In this dissertation, we describe a framework called SPHINX that can administer grid policies and schedule complex and data intensive scientific applications. We present experimental results for several scheduling strategies that effectively utilize the monitoring and job-tracking information provided by SPHINX. These results demonstrate that SPHINX can effectively schedule work across a large number of distributed clusters that are owned by multiple units in a virtual organization in a fault-tolerant way in spite of the highly dynamic nature of the grid and complex policy issues. The novelty lies in the use of effective monitoring of resources and job execution tracking in making scheduling decisions and fault-tolerance---something which is missing in today's grid environments.
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