Name
Title | ||
|---|---|---|
UML modeling of load optimization for distributed computer systems based on genetic algorithm |
Abstract | ||
|---|---|---|
Distributed computing has now become one of the most efficient network system configurations to exhibit parallelism in loosely coupled systems. These systems are known for better reliability, availability, scalability and robustness, intended to provide high performance computing in a very efficient manner. The composition of distributed systems consists of multiple autonomous computers that can be geographically dispersed and interconnected with each other to provide optimum resource utilization. The degree of resource utilization is one of the key criteria for evaluating the performance of such systems. We propose a genetic-algorithm-based approach to load optimization in a distributed computing environment. Genetics algorithm has been adapted from the biological gene theory. Since it shows the existence of the fittest chromosome from the sample chromosomes population, it may be used to find the most optimum solution for any problem. This research work demonstrates the implication of genetic algorithms to optimize the overall waiting time for a set of processes to be executed on a set of servers. In order to understand the design complexity, we modeled the proposed approach using UML class and sequence diagrams. The results of the proposed model have been found beneficial when implemented and tested under various test scenarios using C++. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2413038.2413043 | ACM SIGSOFT Software Engineering Notes |
Keywords | Field | DocType |
efficient network system configuration,genetic algorithm,genetic-algorithm-based approach,resource utilization,optimum solution,optimum resource utilization,efficient manner,uml modeling,high performance computing,uml class,computer system,load optimization,chromosomes,distributed computing,population | Population,Distributed Computing Environment,Supercomputer,Computer science,Server,Robustness (computer science),Distributed algorithm,Genetic algorithm,Scalability,Distributed computing | Journal |
Volume | Issue | Citations |
38 | 1 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vipin Saxena | 1 | 17 | 4.05 |
| Deepak Arora | 2 | 17 | 3.71 |
| Nimesh Mishra | 3 | 0 | 0.34 |