Estimating Service Resource Consumption From Response Time Measurements

We propose a linear regression method and a maximum likelihood technique for estimating the service demands of requests based on measurement of their response times instead of their CPU utilization. Our approach does not require server instrumentation or sampling, thus simplifying the parameterization of performance models. The benefit of this approach is further highlighted when utilization measurement is difficult or unreliable, such as in virtualized systems or for services controlled by third parties. Both experimental results from an industrial ERP system and sensitivity analyses based on simulations indicate that the proposed methods are often much more effective for service demand estimation than popular utilization based linear regression methods. In particular, the maximum likelihood approach is found to be typically two to five times more accurate than utilization based regression, thus suggesting that estimating service demands from response times can help in improving performance model parameterization.