Balancing Risk and Price: An Opportunity-Cost Approach for Job Scheduling in the Grid Market

The previous deadline and budget constrained (DBC) algorithms were proposed to optimize the user´s cost in market-based job assignment. However those algorithms are not suitable for unreliable grid environment. An opportunity-cost guided algorithm is proposed for job scheduling to bridge this gap. By employing trust mechanism into the grid market, this approach takes both of resource´s explicit cost and the successful opportunity into considerations. Von Neumann-Morgenstern utility function (VNM-UF) from the traditional economy theory is applied to characterize the user´s risk bias. A quantitative method is given. Simulations show our approach is more suitable for unreliable environment than many other existing approaches. The job failure rate is reduced to 30% and the total cost saves by 8%~10% averagely.