Clockwork: A newmovement in autonomic systems

Abstract

Statically tuned computing systems may perform poorly when running time-varying workloads. Current work on autonomic tuning largely involves reactive autonomicity, based on feedback control. This paper identifies a new way of thinking about autonomic tuning, that is, predictive autonomicity, based on feedforward control. A general method, called Clockwork, for constructing predictive autonomic systems is proposed. The method is based on statistical modeling, tracking, and forecasting techniques borrowed from econometrics. Systems employing the method detect and subsequently forecast cyclic variations in load, estimate the impact on future performance, and use these data to self-tune, dynamically, in anticipation of need. The paper describes a prototype network-attached storage system that was built using Clockwork, demonstrating the feasibility of the method, and presents key performance measurements of the prototype, demonstrating the practicality of the methods.