
%     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.           


