Load Distribution Composite Design Pattern for Genetic Algorithm-Based Autonomic Computing Systems

TL;DR

This paper introduces a novel composite design pattern approach for autonomic computing systems, utilizing evolutionary algorithms to optimize load distribution and improve system efficiency.

Contribution

It proposes a pattern-oriented architecture combining multiple design patterns and evolutionary algorithms for self-optimization in autonomic systems, which is a novel approach in this domain.

Findings

Composite design patterns improve load distribution efficiency.

Quantitative evaluation shows enhanced system performance.

Architecture effectively reduces server load.

Abstract

Current autonomic computing systems are ad hoc solutions that are designed and implemented from the scratch. 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 for autonomic computing domain. In this paper we propose pattern-oriented software architecture for self-optimization in autonomic computing system using design patterns composition and multi objective evolutionary algorithms that software designers and/or programmers can exploit to drive their work. Main objective of the system is to reduce the load in the server by distributing the population to clients. We used Case Based Reasoning, Database Access, and Master Slave design patterns. 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.