Resource Availability Prediction in Distributed Systems: An Approach for Modeling Non-Stationary Transition Probabilities

Large scale distributed systems employ thousands of resources which inevitably suffer from the unavailability issue. Serious side effects like unexpected delay or failure in the application execution are probable in case of such an issue. The imposed outcome might then be catastrophic consequences for real time applications or penalties for the service providers. Better prediction of the resource unavailability helps diminishing the undesired outcomes. This paper proposes a resource availability prediction algorithm for the mentioned goal. The resource availability variation is modeled as a stochastic process. By analyzing the availability information of NDU resources and both physical and virtual machines of the PlantLab, we found that the transition probabilities among the availability levels are non-stationary. To cope with this characteristic, we introduce Availability Transition Patterns (ATPs); the ATPs are dynamically constructed and the transitions between them are modeled by a Markov chain. The future ATP is then predicted based on the constructed Markov chain, according to which the resource availability-level is predicted. Experimental results confirm the efficiency of the proposed prediction algorithm.