Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
GPFS: A Shared-Disk File System for Large Computing Clusters
FAST '02 Proceedings of the Conference on File and Storage Technologies
The dawning of the autonomic computing era
IBM Systems Journal
Autonomic resource provisioning for software business processes
Information and Software Technology
Improving Architecture-Based Self-Adaptation through Resource Prediction
Software Engineering for Self-Adaptive Systems
A concise introduction to autonomic computing
Advanced Engineering Informatics
Self-optimization of MPI applications within an autonomic framework
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
Automatic performance tuning for J2EE application server systems
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
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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.