Prediction of Resource Availability in Fine-Grained Cycle Sharing Systems Empirical Evaluation
Fine-Grained Cycle Sharing (FGCS) systems aim at utilizing the large amount of computational resources available on the Internet. In FGCS, host computers allow guest jobs to utilize the CPU cycles if the jobs do not significantly impact the local users. Such resources are generally provided voluntarily and their availability fluctuates highly. Guest jobs may fail unexpectedly, as resources become unavailable. To improve this situation, we consider methods to predict resource availability. This paper presents empirical studies on resource availability in FGCS systems and a prediction method. From studies on resource contention among guest jobs and local users, we derive a multi-state availability model. The model enables us to detect resource unavailability in a non-intrusive way. We analyzed the traces collected from a production FGCS system for 3 months. The results suggest the feasibility of predicting resource availability, and motivate our method of applying semi-Markov Process models for the prediction. We describe the prediction framework and its implementation in a production FGCS system, named iShare. Through the experiments on an iShare testbed, we demonstrate that the prediction achieves an accuracy of 86% on average and outperforms linear time series models, while the computational cost is negligible. Our experimental results also show that the prediction is robust in the presence of irregular resource availability. We tested the effectiveness of the prediction in a proactive scheduler. Initial results show that applying availability prediction to job scheduling reduces the number of jobs failed due to resource unavailability.
cycle sharing, resource management, resource availability, prediction algorithm
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