Run-time resource allocation requires the availability of system performance models that are both accurate and inexpensive to solve. We here propose a new methodology for run-time performance evaluation based on a class of closed queueing networks. Compared to exponential product-form models, the proposed queueing networks also support the inclusion of resources having first-come first-served scheduling under non-exponential service times. Motivated by the lack of an exact solution for these networks, we propose a fixedpoint algorithm that approximates performance indexes in linear time and linear space with respect to the number of requests considered in the model. Numerical evaluation shows that, compared to simulation, the proposed models solved by fixed-point iteration have errors of about 1% - 6%, while, on the same test cases, exponential product-form models suffer errors even in excess of 100%. Execution times on commodity hardware are of the order of a few seconds or less, making the proposed methodology practical for runtime decision-making.
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