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% Registration Using genetic algorithms to autonomically tune the
% kernel.
% Proposal Jake Moilanen (moilanen@austin.ibm.com)
One of the next obstacles in autonomic computing is
having a system self-tune for any workload. Workloads
vary greatly between applications and even during an
application's life cycle. It is a daunting task for a
system administrator to manually keep up with a
constantly changing workload. To remedy this
shortcoming, intelligence needs to be put into a system
to autonomically handle this process. One method is to
take an algorithm commonly used in artificial
intelligence and apply it to the Linux kernel.
This paper will cover the use of genetic-algorithms to
autonomically tune the kernel through the development
of the genetic-library. It will discuss the overall
designed of the genetic-library along with the hooked
schedulers, current status, and future work. Finally,
early performance numbers will be covered to give an
idea as towards the viability of the concept.
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