A generative genetic algorithm for evolving adaptation rules of software systems

The Internetware system is a complex and distributed self-adaptive system, which executes in an open, uncertain and dynamic environment, and adapts itself to changes in the environment. We hope that Internetware systems have the ability to automatically evolve in respond to changes. An important problem related to the development of Internetware systems is how to formulate proper adaptation rules. Because of the uncertainty of environment, the adaptation rules may not be suitable to the current system. Adaptation rules always need to be evolved to obtain better results. Some traditional methods can decide adaptation actions in different environmental conditions and evolve adaptation rules. But most of these methods bring about huge computation cost, which are not highly-efficient. To resolve these problems, we propose a method for evolving adaptation rules automatically, based on genetic algorithm and linear regression. We apply this method to evolve adaptation rules for a web application based on a widely used prototype --- RUBiS, which is an auction site similar to eBay. Experiments show that our method can evolve adaptation rules and improve the web application's performance in dynamic environment.