KernTune: Self-tuning Linux Kernel Performance Using Support Vector Machines

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dc.contributor.advisorConnan, James
dc.contributor.authorYi, Long
dc.date.accessioned2022-03-17T12:23:18Z
dc.date.accessioned2024-10-30T14:00:35Z
dc.date.available2022-03-17T12:23:18Z
dc.date.available2024-10-30T14:00:35Z
dc.date.issued2006
dc.description>Magister Scientiae - MScen_US
dc.description.abstractSelf-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 thesis 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 thesis presents design and implementation details for KernTune. It shows how KernTune identifies a system class and tunes the operating system for improved performance.en_US
dc.identifier.urihttps://hdl.handle.net/10566/16900
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.subjectSupport Vector Machineen_US
dc.subjectLinux Kernelen_US
dc.subjectOperating Systemen_US
dc.subjectOptimisationen_US
dc.subjectPerformanceen_US
dc.subjectBenchmarken_US
dc.subjectMachine Learningen_US
dc.subjectWorkloaden_US
dc.subjectOpen Sourceen_US
dc.subjectSystem Profileren_US
dc.titleKernTune: Self-tuning Linux Kernel Performance Using Support Vector Machinesen_US

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Magister Scientiae - MSc (Computer Science)