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Investigating evolutionary approaches for self-adaptation in large distributed databases

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3 Author(s)
Oates, M.J. ; Dept. of Comput. Sci., Reading Univ., UK ; Corne, D. ; Loader, R.

As the size of typical industrial strength databases continues to rise, particularly in the arena of the Internet and multimedia servers, the issue of managing data distribution over clusters or `farms' to overcome performance and scalability issues is becoming of paramount importance. The general objective is to manage a self-adapting distributed database so as to reliably and consistently provide near optimal performance as perceived by client applications. Such a management system must ultimately be capable of operating over a range of time varying usage profiles and fault scenarios, incorporate considerations for multiple updates and maintenance operations, and be capable of being scaled in a practical fashion to ever larger sized networks and databases. This paper investigates evolutionary computation techniques, comparing a genetic algorithm, simulated annealing, and hillclimbing on a test problem in this field. Major differential algorithm performance is found across two different fitness criteria. Preliminary conclusions are that a genetic algorithm approach seems superior to hillclimbing or annealing when the more realistic (from a quality of service viewpoint) objective function is in force. Further, the genetic algorithm approach displays regions of adequate robustness to parameter variation, which is also critical from a maintained quality of service viewpoint

Published in:

Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Date of Conference:

4-9 May 1998

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