Load Distribution Design Pattern for Genetic Algorithm Based Autonomic Systems

Abstract

he need for adaptability in software is growing, driven in part by the emergence of pervasive and autonomic computing. In many cases, it is desirable to enhance existing programs with adaptive behaviour, enabling them to execute effectively in dynamic environments. Increasingly, software systems should self-adapt to satisfy new requirements and environmental conditions that may arise after deployment. Due to their high complexity, adaptive programs are difficult to specify, design, verify, and validate. In this paper we propose a new approach for Genetic Algorithm based multi objective evolution in autonomic system using Design Patterns. Proposed system satisfies properties of autonomic system. Proposed system distributes the population of Genetic Algorithm to different clients to execute the population and store population results into database. We use different Design Patterns for this autonomic system those are Case Based Reasoning, Database Access Design Pattern and Master Slave. Main objective of the system is to reduce the load of the system to distribute the population. The pattern is described using a java-like notation for the classes and interfaces. A simple UML class and Sequence diagrams are depicted