Estimating computational requirements in multi-threaded applications
Performance models provide effective support for managing quality-of-service (QoS) and costs of enterprise applications. However, expensive high-resolution monitoring would be needed to obtain key model parameters, such as the CPU consumption of individual requests, which are thus more commonly estimated from other measures. However, current estimators are often inaccurate in accounting for scheduling in multi-threaded application servers. To cope with this problem, we propose novel linear regression and maximum likelihood estimators. Our algorithms take as inputs response time and resource queue measurements and return estimates of CPU consumption for individual request types. Results on simulated and real application datasets indicate that our algorithms provide accurate estimates and can scale effectively with the threading levels.
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