Using swarm intelligence for distributed job scheduling on the grid
| dc.contributor.advisor | Ludwig, Simone A. | en_US |
| dc.creator | Moallem, Azin | en_US |
| dc.date.accessioned | 2009-04-13T12:32:50Z | en_US |
| dc.date.accessioned | 2013-01-04T04:29:04Z | |
| dc.date.available | 2010-04-16T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T04:29:04Z | |
| dc.date.created | 2009 | en_US |
| dc.date.issued | 2009 | en_US |
| dc.date.submitted | 2009 | en_US |
| dc.description.abstract | With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specific time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-04132009-123250 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Ant colony | en_US |
| dc.subject | Swarm intelligence | en_US |
| dc.subject | Grid | en_US |
| dc.subject | particle Swarm | en_US |
| dc.subject | Load balancing | en_US |
| dc.title | Using swarm intelligence for distributed job scheduling on the grid | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Computer Science | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |