Abstract
In this paper, we consider managing service performance starting from the composition time, aiming to reduce the risk of execution failures during service composition. We use ARIMA to predict workloads of the services at the time when they are likely to be invoked and subsequently predict the response time and chances that the requests for accessing the services may be declined due to admission control. The in-depth analysis can help avoid timing failures during service execution. However, these analyses may incur overhead and we introduce a two-phase composition algorithm to reduce the potential overhead. Our system also considers continuous monitoring and service recomposition to greatly increase the probability of completing the service execution within the deadline. Experimental results show that our service management approach can greatly improve the success rate for meeting the deadline.
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Moussa, H., Yen, IL., Bastani, F., Dong, Y., He, W. (2019). Toward Better Service Performance Management via Workload Prediction. In: Ferreira, J., Musaev, A., Zhang, LJ. (eds) Services Computing – SCC 2019. SCC 2019. Lecture Notes in Computer Science(), vol 11515. Springer, Cham. https://doi.org/10.1007/978-3-030-23554-3_7
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DOI: https://doi.org/10.1007/978-3-030-23554-3_7
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