Balanced Job Assignment Based on Ant Algorithm for Computing Grids

Chang, Ruay-Shiung; Chang, Jih-Sheng; Lin, Po-Sheng

doi:10.1109/apscc.2007.4414473

Cited by 1 publication

(5 citation statements)

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“…The submitted jobs must be matched between the available resources in terms of their job characteristics and resources capacity. Current algorithms (Xu et al, 2003, Chang et al, 2007 have also considered the jobs characteristics and resources capacity in scheduling of jobs but did not take into considerations of Million Instruction per Second (MIPS) and CPU time needed for each job.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Balanced Ant Colony Optimization (BACO) algorithm proposed by Chang et al (2007) chooses optimal resources to process jobs based on resource status and size of submitted job. The researchers aimed to balance the entire resources and at the same time minimize the makespan of the jobs in grid computing system.…”

Section: Grid Load Balancingmentioning

confidence: 99%

“…Balanced job assignment based on ant algorithm for computing grids called BACO was proposed by Chang et al (2007). The research aimed to minimize the computation time of job executing in Taiwan's UniGrid environment which also focused on load balancing factors of each resource.…”

Section: Ant Based Grid Load Balancing Algorithmmentioning

confidence: 99%

“…There are various issues in grid resource management such as resource discovery, resource scheduling, resource monitoring, resources inventories, resource provisioning, load balancing, fault isolation, autonomic capabilities and service level management system (Sharma & Bawa, 2008). However, grid scheduling and grid load balancing are the main issues that are often discussed by many researchers (Moallem, 2009;Chang et al 2007;Liu & Wang, 2008) Grid scheduling is an essential function provided by the grid infrastructure (Kousalya & Balasubramanie, 2008). The scheduling problem in grid computing is classifi ed as Nondeterministic Polynomial (NP) -complete problem (Kousalya & Balasubramanie, 2009) which means that there is no exact algorithm that can solve the problem in a polynomial time.…”

Section: Introductionmentioning

confidence: 99%

“…Research by Chang et al (2007) and Yan et al (2005) used the ACO algorithm to solve the load balancing problem. ACO is inspired by a colony of ants that work together in foraging behavior.…”

Section: Introductionmentioning

confidence: 99%

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Ant colony optimization algorithm for load balancing in grid computing

Ku-Mahamud¹,

Din²

2012

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Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. The proposed algorithm is known as the enhance ant colony optimization (EACO). The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modelled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job.Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization. Experimental results show that EACO produced better grid resource management solution.

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Section: Research Backgroundmentioning

confidence: 99%

Section: Grid Load Balancingmentioning

confidence: 99%

Section: Ant Based Grid Load Balancing Algorithmmentioning

confidence: 99%