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.
Highlights
In a Genetic Algorithm (GA) application, many individuals derive, independently and concurrently, competing solutions to a problem
The flow of a typical GA simulation is as follows [5]: First, a GA server creates many individuals randomly. Each of these individuals is tested for fitness. Based on their fitness, measured by a fitness function that quantifies the optimality of a solution, the server selects a percentage of the individuals that are allowed to crossover with each other, analogous to gene sharing through
Using Design Patterns helps designers exploit the community’s collective wisdom and experience as captured in the patterns, it enables others studying the system in question to gain a deeper understanding of how the system is structured, and why it behaves in particular ways
Summary
In a Genetic Algorithm (GA) application, many individuals derive, independently and concurrently, competing solutions to a problem. These solutions are evaluated for fitness and individuals survive and reproduce based upon their fitness. The flow of a typical GA simulation is as follows [5]: First, a GA server creates many individuals randomly. Each of these individuals is tested for fitness. Based on their fitness, measured by a fitness function that quantifies the optimality of a solution, the server selects a percentage of the individuals that are allowed to crossover with each other, analogous to gene sharing through
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