Multimodal Pattern-Oriented Software Architecture for Self-Optimization and Self-Configuration in Autonomic Computing System using Multi Objective Evolutionary Algorithms

Current autonomic computing systems are ad hoc solutions that are designed and implemented from the scratch, and there are no universal standard (or well established) software methodologies to develop. There are also significant limitations to the way in which these systems are validated. When designing software, in most cases two or more patterns are to be composed to solve a bigger problem. A composite design patterns shows a synergy that makes the composition more than just the sum of its parts which leads to ready-made software architectures. As far as we know, there are no studies on composition of design patterns and pattern languages for autonomic computing domain. In this paper we propose multimodal pattern-oriented software architecture for self-optimization and self-configuration in autonomic computing system using design patterns composition, multi objective evolutionary algorithms, and service oriented architecture (SOA) that software designers and/or programmers can exploit to drive their work. We evaluate the effectiveness of our architecture with and without design patterns compositions. The use of composite design patterns in the architecture and quantitative measurements are presented. A simple UML class diagram is used to describe the architecture.