Resource Discovery for Grid Computing Environment Using Ant Colony Optimization by Applying Routing Information and LRU Policy

2012 ◽  
pp. 124-133 ◽  
Author(s):  
S. Nirmala Devi ◽  
A. Pethalakshmi
Keyword(s):  
Grid Computing ◽  
Ant Colony Optimization ◽  
Resource Discovery ◽  
Ant Colony ◽  
Computing Environment

Ant colony optimization algorithm for load balancing in grid computing

2012 ◽  
Author(s):  
Ku Ruhana Ku-Mahamud ◽  
Aniza Mohamed Din
Keyword(s):  
Resource Management ◽  
Load Balancing ◽  
Grid Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Computing System ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Grid Resource ◽  
Grid Resource Management

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.

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

2015 ◽  
Vol 14 (10) ◽  
pp. 6176-6183
Author(s):  
S.J. Mohana ◽  
Dr.M. Saroja ◽  
Dr.M. Venkatachalam
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Ant Colony Optimization Algorithm ◽  
Computing Environment ◽  
Shared Resources ◽  
Cloud Computing Environment ◽  
Computing Model

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

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