While volunteer computing (VC) systems reach the most powerful computing platforms, they still have the problem of guaranteeing computational correctness, due to the inherent unreliability of volunteer participants. Spot-checking technique, which checks each participant by allocating spotter jobs, is a promising approach to the validation of computation results. The current spot-checking is based on the implicit assumption that participants never distinguish spotter jobs from normal ones; however generating such spotter jobs is still an open problem. Hence, in the real VC environment where the implicit assumption does not always hold, spot-checking-based methods such as well-known credibility-based voting become almost impossible to guarantee the computational correctness. In this paper, we generalize spot-checking by introducing the idea of imperfect checking. This generalization allows to guarantee the computational correctness under the situation that spot-checking is not fully-reliable and participants may distinguish spotter jobs. Moreover, we develop a generalized formula of the credibility, which enables credibility-based voting to utilize check-by-voting technique. Simulation results show that check-by-voting improves the performance of credibility-based voting, while guaranteeing the same level of computational correctness.

This paper presents a proposal of an expected-credibility-based job scheduling method for volunteer computing (VC) systems with malicious participants who return erroneous results. Credibility-based voting is a promising approach to guaranteeing the computational correctness of VC systems. However, it relies on a simple round-robin job scheduling method that does not consider the jobs' order of execution, thereby resulting in numerous unnecessary job allocations and performance degradation of VC systems. To improve the performance of VC systems, the proposed job scheduling method selects a job to be executed prior to others dynamically based on two novel metrics: expected credibility and the expected number of results for each job. Simulation of VCs shows that the proposed method can improve the VC system performance up to 11%; It always outperforms the original round-robin method irrespective of the value of unknown parameters such as population and behavior of saboteurs.