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A cost-effective critical path approach for service priority selections in grid computing economy [An article from: Decision Support Systems]
Book Details
Author(s)M. Lin, Z. Lin
PublisherElsevier
ISBN / ASINB000PBZWFC
ISBN-13978B000PBZWF2
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Decision Support Systems, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The increasing demand for grid computing resources calls for an incentive-compatible pricing mechanism for differentiated service qualities. This paper examines the optimal service priority selection problem for a grid computing services user, who is submitting a multi-subtask job for the priced services in a grid computing network. We conceptualize the problem into a prioritized critical path method (CPM) network, identify it as a time-cost tradeoff problem, and differentiate it from the traditional problem by considering a delay cost associated to the total throughput time. We define the optimal solution for the prioritized CPM network as the globally cost-effective critical path (GCCP), the optimal critical path for the solution that minimizes the total cost. As the exponential time complexity of GCCP makes the problem practically unsolvable, we propose a locally cost-effective critical path (LCCP) based approach to the prioritized CPM problem with a heuristic solution. The locally optimized priority constituting the configuration for LCCP can provide a lower bound for the throughput time of GCCP with the same time complexity as that for a traditional CPM problem. To further improve the quality of the solution, we conceive a priority adjustment algorithm named Non-critical Path Relaxation (NPR) algorithm, to refine the priority selections of the nodes on the non-critical paths. A discussion of the effects of the users' priority selections on the grid network pricing is provided to elicit future research on the computing resource pricing problem on the service-side.
Description:
The increasing demand for grid computing resources calls for an incentive-compatible pricing mechanism for differentiated service qualities. This paper examines the optimal service priority selection problem for a grid computing services user, who is submitting a multi-subtask job for the priced services in a grid computing network. We conceptualize the problem into a prioritized critical path method (CPM) network, identify it as a time-cost tradeoff problem, and differentiate it from the traditional problem by considering a delay cost associated to the total throughput time. We define the optimal solution for the prioritized CPM network as the globally cost-effective critical path (GCCP), the optimal critical path for the solution that minimizes the total cost. As the exponential time complexity of GCCP makes the problem practically unsolvable, we propose a locally cost-effective critical path (LCCP) based approach to the prioritized CPM problem with a heuristic solution. The locally optimized priority constituting the configuration for LCCP can provide a lower bound for the throughput time of GCCP with the same time complexity as that for a traditional CPM problem. To further improve the quality of the solution, we conceive a priority adjustment algorithm named Non-critical Path Relaxation (NPR) algorithm, to refine the priority selections of the nodes on the non-critical paths. A discussion of the effects of the users' priority selections on the grid network pricing is provided to elicit future research on the computing resource pricing problem on the service-side.
