Service composition design pattern for autonomic computing systems using association rule based learning

Abstract

The adaptability in software is the main fascinating concern for which most of the software architects today are really interested in providing the Autonomic computing. In order to provide remedy for the service failures that occurs at the servers of the respective service providers, there is a need to introduce the self-reconfiguration planes to be applied astronomically without the interruption of the administrator to solve the problem manually. Different programming models have been introduced for providing the dynamic behavior of the services being provided. Few among them are the Aspect Oriented Programming (AOP) and Feature Oriented Programming (FOP) both of them having the ability to modularize the crosscutting concerns, where the former is dependent on aspects, advice and lateral one on the collaboration design and refinements. In this paper we will use the design patterns which will satisfy the properties of autonomic computing system: for the Decision-Making phase we will introduce Case-Based Reasoning design pattern, and for Reconfiguration phase we will introduce Reactor design pattern. The most important proposal in our design pattern is that we will use the Association Rule Learning method of Data Mining to learn about new services that can be added along with the requested service to make the service as a dynamic composition of two or more services. Then we will include the new service as an aspectual feature module code without interrupting the user. The pattern is described using a java-like notation for the classes and interfaces. A simple UML and Sequence diagram are depicted. © 2012 Springer-Verlag GmbH.