Integrated Resource and Cost Management Scheme for Computational Grids

Abstract

Problem statement: Autonomous decision making and resource scheduling are the main objectives of market-life computational grid. Resource providers and consumers make the scheduling decisions with cost and incentive factors. The two objectives are to maximize the success rate of job execution and to minimize fairness deviation among resources. The challenge is to develop a grid-scheduling scheme that enables individual participants to make autonomous decision while producing a desirable emergent property in the grid system. Approach: An incentive-based scheduling scheme is presented to utilize a peer-to-peer decentralized scheduling framework a set of local heuristic algorithms and three market instruments of job announcement, price and competition degree. The incentive based scheme is enhanced with priority based pricing schemes. The resource availability, job priority and network delay are used for the cost and incentive decisions. Results: The performance of this scheme is evaluated via extensive simulation using synthetic and real workloads. The system achieves efficient cost and incentive optimization for both providers and consumers. Conclusion: The approach outperforms other scheduling schemes in optimizing incentives for both consumers and providers, leading to highly successful job execution and fair profit allocation.