KernTune: Self-tuning Linux kernel performance using support vector machines
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Date
2007
Authors
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Journal ISSN
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Publisher
Association for Computing Machinery
Abstract
Self-tuning has been an elusive goal for operating systems and is becoming a pressing issue for modern operating systems. Well-trained system administrators are able to tune an operating system to achieve better system performance for a specific system class. Unfortunately, the system class can change when the running applications change. Our model for self-tuning operating system is based on a monitor-classify-adjust loop. The idea of this loop is to continuously monitor certain performance metrics, and whenever these change, the system determines the new system class and dynamically adjusts tuning parameters for this new class. This paper describes KernTune, a prototype tool that identifies the system class and improves system performance automatically. A key aspect of KernTune is the notion of Artificial Intelligence (AI) oriented performance tuning. It uses a support vector machine (SVM) to identify the system class, and tunes the operating system for that specific system class. This paper presents design and implementation details for KernTune. It shows how KernTune identifies a system class and tunes the operating system for improved performance.
Description
Keywords
Linux kernel optimization, Support vector machines, Performance tuning, Machine learning, Server classification
Citation
Yi, L. and Connan, J. (2007) KernTune: Self-tuning Linux kernel performance using support vector machines. In Proceedings of the 2007 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries (Port Elizabeth, South Africa, October 02 - 03, 2007). SAICSIT '07, vol. 226. ACM, New York, NY, 189-196. http://doi.acm.org/10.1145/1292491.1292513