Investigating evolutionary approaches for self-adaptation in large distributed databases

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