A job scheduling method based on expected probability of completion of voting in volunteer computing

This paper addresses the problem of job scheduling in volunteer computing (VC) systems where each computation job is replicated and distributed to multiple participants (workers) to remove incorrect results. In the job scheduling of VC, the number of assigned workers to complete a job is an important factor for the system performance, however, it cannot be fixed because some of the workers may not return results in real VC. We propose a job scheduling method which considers the expected probability of completion (EPC) for each job based on the worker's history information. The key idea of the proposed method is to assign jobs so that EPC is always greater than a specified value (SPC). By setting SPC as a reasonable value, any job in the proposed method can be completed without excess allocations, which leads to the higher performance of VC systems. Simulation results show that the performance of the proposed method is up to 5 times higher than that of the conventional method, while keeping the error rate lower than a required value.