Resource Scheduling Based on Ant Colony Optimization Algorithm in Grid Computing Environments

2013 ◽  
Vol 12 (24) ◽  
pp. 8010-8014
Author(s):  
Lei Chen
Keyword(s):  
Grid Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Resource Scheduling ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Environments

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.

scholarly journals A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

2021 ◽  
Vol 13 (14) ◽  
pp. 7933
Author(s):  
Jianguo Zheng ◽  
Yilin Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Resource Scheduling ◽  
Bat Algorithm ◽  
Ant Colony ◽  
Particle Swarm Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Multi Objective

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling

2014 ◽  
Vol 989-994 ◽  
pp. 2192-2195 ◽  
Author(s):  
Hai Yang
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

This paper introduces PSO algorithm into ant colony optimization algorithm so that an improved ant colony optimization algorithm named ACA-PSO is proposed. The ACA-PSO algorithm can get more effective optimal solutions by using PSO algorithm to do crossover operation and mutation operation so as to avoid trapping in local optimum. Finally, the simulation experiment reflects that the ACA-PSO algorithm speeds the convergence up which is more suitable for resource scheduling in cloud computing.

scholarly journals Load Balancing in Computational Grids Using Ant Colony Optimization Algorithm

2014 ◽  
pp. 262-265
Author(s):  
SOWMYA SURYADEVERA ◽  
JAISHRI CHOURASIA ◽  
SONAM RATHORE ◽  
ABDUL JHUMMARWALA
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Computational Grids ◽  
Ant Colony Optimization Algorithm ◽  
Critical Issues ◽  
Computer Resources ◽  
Administrative Domains

Grid computing is the combination of computer resources from multiple administrative domains for a common goal. Load balancing is one of the critical issues that must be considered in managing a grid computing environment. It is complicated due to the distributed and heterogeneous nature of the resources. An Ant Colony Optimization algorithm for load balancing in grid computing is proposed which will determine the best resource to be allocated to the jobs, based on resource capacity and at the same time balance the load of entire resources on grid. The main objective is to achieve high throughput and thus increase the performance in grid environment.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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