Performance evaluation of a probabilistic replica selection algorithm

When executing time-sensitive distributed applications, a middleware that provides dependability and timeliness is faced with the important problem of preventing timing failures both under normal conditions and when the quality of service is degraded due to replica failures and transient overload on the server. To address this problem, we have designed a probabilistic model-based replica selection algorithm that allows a middleware to choose a set of replicas to service a client based on their ability to meet a client's timeliness requirements. This selection is done based on the prediction made by a probabilistic model that uses the performance history of replicas as inputs. In this paper, we describe the experiments we have conducted to evaluate the ability of this dynamic selection algorithm to meet a client's timing requirements, and compare it with that of a static and round-robin selection scheme under different scenarios.