An optimal retry policy based on fault classification

An optimal retry policy in a computer system is usually derived under the unrealistic assumption that fault characteristics are known a priori and remain unchanged throughout the mission lifetime. In such a case, the optimal retry period depends only upon the system's status at the time of fault detection. We propose to remedy this deficiency by formulating the optimal retry problem as a Bayesian decision problem where not only the time of fault detection but also the results of earlier retries are used to estimate the current fault characteristics. Previous knowledge about fault characteristics is represented by the prior distributions of fault-related parameters which are updated whenever new samples are obtained from retry and detection mechanisms. A new fault classification scheme is proposed to assign a temporal fault type (i.e. permanent, intermittent or transient) to each detected fault so that the corresponding fault parameters can be estimated. The estimated fault parameters are then used to derive the optimal retry period that minimizes the mean task completion time. Efficient algorithms are developed to determine the optimal retry period online upon detection of each fault. To evaluate the goodness of the proposed retry policy, it is compared with, and is always found to outperform, a number of fixed retry period policies.<
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