CASSANDRA: An Online Failure Prediction Strategy for Dynamically Evolving Systems
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Dynamically evolving systems are characterized by components that can be inserted or removed while the system is being operated leading to unsafe run-time changes that may compromise a correct execution. To mitigate the effects of such a failure we propose an online analysis technique that admit an integration “a-priori” and a monitoring of the run-time behaviour to provide information about possible errors when these can happen. Our Cassandra technique proposes a novel run-time monitoring and verification algorithm with the ability to predict potential failures that can happens in future states of the systems. Cassandra combines design-time and run-time information. Both are used to identify the current execution state, and to drive the construction of predictions that look to a number k of steps ahead of the current execution state. This paper provides a detailed formalization of the technique then it introduces a formal definition of the Cassandra algorithms and reports some complexity measures. Finally the paper closes with a description of a first concrete implementation of the approach, and its evaluation.
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