Resource selection in grid: a taxonomy and a new system based on decision theory, case‐based reasoning, and fine‐grain policies

Resource selection on current brokers still presents challenges for achieving the best solution in the decision‐making process, especially when considering many factors. We approach this problem considering user preference for specific resource selection objectives such as expected performance for an application execution, resource access restriction, execution application cost, and resource reliability. For each objective, we employ different techniques and combine them in a decision theory model. By considering performance in the selection process, we use the case‐based reasoning technique based on similar past job executions to predict a new job time execution. With the resource access restriction as a selection factor, we develop a fine‐grain policy‐based model for distributed resource access verification. Unlike a global access policy, which applies to all resources in a virtual organization, a fine‐grain policy establishes rules for specific resources and users. In this case, a previous access restriction verification prevents a resource selection, which may deny access to a requisition, resulting in an unsuccessful submission. The decision model uses the multi‐attribute utility theory, which relates the important objectives above and allows different proportions of user preferences for each objective. The complete solution is distributed and implemented using a multi‐agent system, acting as a resource broker. All models of this paper are analyzed in a real environment, presenting appropriate functional behaviors. Results show that our prediction model is accurate and efficient in the prediction process and our distributed model runs faster than centralized approaches and considers access restriction heterogeneities. Copyright © 2008 John Wiley & Sons, Ltd.