Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are required or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high. This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. The proposed algorithm can overcome stagnation problem, minimize processing time, match jobs with suitable resources, and balance entire resources in grid environment. This research follows the experimental research methodology that consists of problem analysis, developing the proposed framework, constructing the simulation environment, conducting a set of experiments and evaluating the results. There are three new mechanisms in this proposed framework that are used to 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 modeled 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 job scheduling. 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 existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. Experimental results show that EACO produced better grid resource management solution compared to other algorithms

GRID COMPUTING FOR COLLABORATIVE NETWORKS: A LITERATURE REVIEW

This paper describes the methodology and results of a literature review targeting the distinct interpretations of the Grid Computing paradigm within the context of Collaborative Networks. The review is based on the analysis of contributions published in selected scientific journals between 2002 and today. The analysis was performed taking into account the assumptions, scopes and solutions provided to approach the challenges for SMEs’ collaborative networks. The research questions driving this literature review have been the following: (1) How is the concept of Grid Computing associated with the concept of Collaborative Network? (2) How the Grid computing supports Collaborative Networks? (3) What are the business implications in Grid supported Collaborative Networks

Ant colony optimization algorithm for load balancing in grid computing

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

Allocation strategies for utilization of space-shared resources in Bag of Tasks grids

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Allocation Strategies for Utilization of Space Shared Resources in Bag of Tasks Grids ⋆

As the adoption of grid computing in organizations expands, the need for wise utilization of different types of resources also increases. A volatile resource, such as a desktop computer, is a common type of resource found in grids. However, using efficiently other types of resources, such as space-shared resources, represented by parallel supercomputers and clusters of workstations, is extremely important, since they can provide great amount of computation power. Using space-shared resources in grids is not straightforward since they require jobs to a priori specify some parameters, such as allocation time and amount of processors. Current solutions (e.g. GRAM) are based on the explicit definition of these parameters by the user. On the other hand, good progress has been made in supporting Bag-of-Tasks applications on grids. This is a restricted model of parallelism on which tasks do not communicate among themselves, making recovering from failures a simple matter of reexecuting tasks. As such, there is no need to specify a maximum number of resources, or a period of time that resources must be executing the application, such as required by space-shared resources. Besides, this state of affairs make it hard for Bag-of-Tasks applications running on grid to leverage from space-shared resources. This paper presents th