CoSL: A coordinated statistical learning approach to measuring the capacity of multi-tier websites

Website capacity determination is crucial to measurement-based access control, because it determines when to turn away excessive client requests to guarantee consistent service quality under overloaded conditions. Conventional capacity measurement approaches based on high-level performance metrics like response time and throughput may result in either resource over-provisioning or lack of responsiveness. It is because a website may have different capacities in terms of the maximum concurrent level when the characteristic of workload changes. Moreover, bottleneck in a multi-tier website may shift among tiers as client access pattern changes. In this paper, we present an online robust measurement approach based on statistical machine learning techniques. It uses a Bayesian network to correlate low level instrumentation data like system and user cpu time, available memory size, and I/O status that are collected at run-time to high level system states in each tier. A decision tree is induced over a group of coordinated Bayesian models in different tiers to identify the bottleneck dynamically when the system is overloaded. Experimental results demonstrate its accuracy and robustness in different traffic loads.