Grid Resource Management by means of Ant Colony Optimization

Pavani, Gustavo Sousa; Waldman, Hélio

doi:10.1109/broadnets.2006.4374427

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…ACO has been applied in solving many problems in scheduling such as Job Shop Problem, Open Shop Problem, Permutation Flow Shop Problem, Single Machine Total Tardiness Problem, Single Machine Total Weighted Tardiness Problem, Resource Constraints Project Scheduling Problem, Group Shop Problem and Single Machine Total Tardiness Problem with Sequence Dependent Setup Times [10]. A recent approach of ACO researches in the use of ACO for scheduling job in grid computing [2]. ACO algorithm has been used in grid computing because it is easily adapted to solve both static and dynamic combinatorial optimization problems and job scheduling in grid computing is an example.…”

Section: Related Work On Aco In Grid Computing Environmentmentioning

confidence: 99%

“…Assuming PV ij is selected then job j will be processed by resource r. The local pheromone update is performed after job j has been assigned to resource r. This formula can only be applied to unassigned jobs in the PV matrix. The local pheromone update is formulated as follow: (2) where ξ,0< ξ<1 and 0 τ are two parameters. The value of 0 τ is set to be the same as the initial value for the pheromone trails.…”

Section: Proposed Aco For Grid Load Balancingmentioning

confidence: 99%

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Ant Colony Algorithm for Job Scheduling in Grid Computing

Ku-Mahamud

Nasir

2010

2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation

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Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources. This will lead to resources having high workload and stagnation may occur if computational times of the processed jobs are high. This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing. The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max -Min Ant System. The algorithm focuses on local pheromone trail update and the trail limit values. A matrix is used to record the status of the available resources. The agent concept is also integrated in this algorithm for the purpose of updating the grid resource table. Experimental results obtained showed that this is a promising ant colony algorithm for job scheduling in grid environment.

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Section: Related Work On Aco In Grid Computing Environmentmentioning

confidence: 99%

Section: Proposed Aco For Grid Load Balancingmentioning

confidence: 99%

Ant Colony Algorithm for Job Scheduling in Grid Computing

Ku-Mahamud

Nasir

2010

2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation

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“…The problem of balancing resources is defined as Nondeterministic Polynomial (NP)-complete problem [12]. Grid resource management is a very challenging task since the resources that need to be shared are distributed and heterogeneous [2]. Resource management involves the process of scheduling jobs to suitable resources as illustrated in Fig.…”

Section: Grid Load Balancingmentioning

confidence: 99%

Grid Load Balancing Using Ant Colony Optimization

Nasir

Ku-Mahamud

2010

2010 Second International Conference on Computer and Network Technology

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An enhanced ant colony optimization technique for jobs and resources scheduling in grid computing is proposed in this paper. The proposed technique combines the techniques from Ant Colony System and Max -Min Ant System and focused on local pheromone trail update and trail limit. The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table . This facilitates in scheduling jobs to available resources efficiently which will enable jobs to be processed in minimum time and also balance all the resource in grid system.

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“…Anycast routing can be dealt with in several ways. In this paper, we will present a set of algorithms based on the principles of ant colony optimization (ACO) [16][17][18][19], a form of swarm intelligence that has proven its effectiveness in many business and routing problems [20]. ACO is a technique inspired by the natural behavior of ants in a colony.…”

Section: A Conceptsmentioning

confidence: 99%

Ant colony optimization for the routing of jobs in optical grid networks

et al. 2008

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͑Doc. ID 86109͒ Grid networks provide users with a transparent way to access computational and storage resources. The introduction of (dense) wavelength division multiplexing techniques have made optical networks the technology of choice for data-intensive grid traffic. In a grid network scenario, users are generally more interested in the successful completion of their jobs than in the location where the actual processing occurs. Job routing and scheduling in current generation grid networks are managed by resource brokers, which assign each job to a resource and route the job in a unicast way. An anycast approach using grid-aware network algorithms would bypass the need for a resource broker and increase scalability. We propose several anycast algorithms for job routing in optical grid networks, based on the concept of ant colony optimization, which draws parallels between the behavior of ants gathering food and the routing of packets inside a network. Simulation results show an increased performance of our algorithms over more classical unicast-based protocols, even though this is accompanied by a slight increase in complexity.

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Grid Resource Management by means of Ant Colony Optimization

Cited by 9 publications

References 18 publications

Ant Colony Algorithm for Job Scheduling in Grid Computing

Ant Colony Algorithm for Job Scheduling in Grid Computing

Grid Load Balancing Using Ant Colony Optimization

Ant colony optimization for the routing of jobs in optical grid networks

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