Name
Abstract | ||
|---|---|---|
Grids are an emerging infrastructure providing distributed access to computational and storage resources. Handling many incoming requests at the same time and distributing the workload efficiently is a challenge which load balancing algorithms address. Current load balancing implementations for the Grid are central in nature and therefore prone to the single point of failure problem. This paper introduces two distributed artificial life-inspired load balancing algorithms using Ant Colony Optimization and Particle Swarm Optimization. Distributed load balancing stands out as a robust algorithm in regard to any topology changes in the network. The implementation details are given and evaluation results show the efficiency of the two distributed load balancing algorithms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1529282.1529522 | SAC |
Keywords | Field | DocType |
ant colony optimization,particle swarm optimization,current load,artificial life technique,load balancing,failure problem,evaluation result,grid job scheduling,incoming request,artificial life-inspired load,implementation detail,algorithms address,artificial life,load balance,job scheduling | Particle swarm optimization,Ant colony optimization algorithms,Single point of failure,Load balancing (computing),Computer science,Multi-swarm optimization,Job scheduler,Grid,Metaheuristic,Distributed computing | Conference |
Citations | PageRank | References |
7 | 0.44 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Azin Moallem | 1 | 36 | 1.39 |
| Simone A Ludwig | 2 | 1309 | 179.41 |