Cooperative resource sharing enables distinct organizations to form a federation of computing resources. The motivation behind cooperation is that organizations are likely to serve each other by trading unused CPU cycles given the existence of irregular usage patterns of their local resources. In this way, resource sharing would enable organizations to purchase resources at a feasible level while meeting peak computational throughput requirements. This federation results in community grid that must be managed. A functional broker is deployed to facilitate remote resource access within the community grid. A major issue is the problem of correlations in job arrivals caused by seasonal usage and/or coincident resource usage demand patterns. These correlations incur high levels of burstiness in job arrivals causing the job queue of the broker to grow to an extent such that its performance becomes severely impaired. Since job arrivals cannot be controlled, management strategies must be employed to admit jobs in a manner that can sustain a fair level of resource allocation performance at all participating organizations in the community. In this paper, we present a theoretical analysis of the problem of job traffic burstiness on resource allocation performance in order to elicit the general job management strategies to be employed. Based on the analysis, we define and justify a job management strategies for the resource broker to cope with overload conditions caused by job arrival correlations.

SUMMARYA virtual organization is established when physical organizations collaborate to share their computing resources with the aim of serving each other when there is a likelihood of insufficient local resources during peak resource usage periods at any organization. Contention becomes a potential problem when a large number of requests, which can overwhelm the aggregate capacity of shared resources, are submitted from the participating organizations coincidentally at the same period. In particular, when a small number of requests that require large amounts of computing resources are admitted in place of a large number of requests that require less computing resources, the overall system performance, in terms of admission ratio, can deteriorate significantly. Hence, admission control is necessary to reduce resource oversubscription. Because domain-shared computing resources are likely to be combined to form a large-scale system, it is not possible to define a fixed admission policy solely based on the request's CPU and execution time requirements. In this paper, we introduce an admission control framework, based on a pricing model, for a multi-domain-shared computing infrastructure. The performance of the admission control framework is evaluated under different scenarios that contribute to the overall degree of competition for shared resources. The results are presented and analyzed in this paper.

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