Abstract
A grid-computing service, united by numerous distributed and heterogeneous resources, supplies various advanced and cumbersome problems in terms of high-performance computing. Based on reciprocal transactions of a Grid Bank [24], this dissertation presents an economics-driven resource allocation model to determine the grid-computing service reliability for the service level agreement and to evaluate grid-computing service expenditure for the free rider problem. In terms of the probability of completing the task, this paper initially converts the grid system into a multi-state unreliable network and then estimates the service reliability in a tree topology using a simulation method (i.e., cellular automata Monte-Carlo simulation, CA-MCS) and in star topology using an analytic method (i.e., universal generating function methodology, UGFM). This paper also proposes virtual payment assessment to appraise the rental-time cost for each resource’s contribution. In order to determine the best resource allocation for a given rental-time cost and guaranteed reliability, this paper presents two revised meta-heuristic algorithms (i.e., GA and PSO), wherein Elite-selected and Reborn (ER) mechanisms improve the optimization effectiveness and a Pareto-set Cluster evolves the Pareto frontier. Accordingly, the economics-driven resource model saves total rental-time cost and ensures that the grid-computing service is reliable.