Adaptive Result Verification Based on Fuzzy Inference Model in Desktop Grid Environments

Abstract

A result verification procedure is required to guarantee the correctness of task results executed by any unspecified resources in desktop grids. Voting-based and trust-based schemes have typically been used for desktop grids. However, these schemes can encounter two problems: waste of resources, due to redundant replications of each task, and increased turnaround time, due to the inability to deal with a dynamic changeable execution environment. To overcome these problems, we propose an adaptive result verification scheme based on fuzzy inference that can determine the number of replications per task needed for result verification. Our scheme is based on the classification of resources according to the trusty degree and the result return probability. Using these two parameters, we develop the fuzzy inference model that allows correct task results to be returned by a task deadline for current desktop grid environments. Simulation results indicate that our scheme is superior to others in terms of turnaround time of entire tasks, quantity of resources consumed, and number of reallocations per task.