Resource Allocation in Grid Computing Environment Using Genetic–Auction Based Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Kuppani Satish ◽  
A. Rama Mohan Reddy
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Grid Computing ◽  
Genetic Approach ◽  
Simulation Environment ◽  
Computing Environment ◽  
Cost Constraints ◽  
Proposed Model ◽  
Core Functionality ◽  
Made In

The main core functionality of Grid Computing is resource allocation and scheduling. With the idea of genetic algorithms and microeconomics, it is proposed a Resource allocation method called a genetic-auction based algorithm [GAAB]. This algorithm contains two modules, auction module and genetic approach. Auction module find outs resource-trading price between resource provider and resource buyer, and the resource allocation carried out by Genetic algorithm by considering both time and cost constraints simultaneously. In this article, evaluations are made in the simulation environment and the results show the effectiveness of the proposed model.

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

2017 ◽  
Vol 26 (1) ◽  
pp. 169-184 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Nneoma A. Okoroafor ◽  
Seyed M. Buhari ◽  
Marc E. Frincu ◽  
Sahalu B. Junaidu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Grid Computing ◽  
Resource Selection ◽  
Computing System ◽  
Population Based ◽  
Fine Tuning ◽  
Grid Resource ◽  
Performance Results ◽  
Grid Resources

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

scholarly journals An Improved Resource Allocation Model for Grid Computing Environment

2019 ◽  
Vol 12 (1) ◽  
pp. 104-113
Author(s):  
Anju Shukla ◽  
◽  
Shishir Kumar ◽  
Harikesh Singh ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Grid Computing ◽  
Computing Environment ◽  
Allocation Model ◽  
Resource Allocation Model

scholarly journals Resource allocation model for grid computing environment

2020 ◽  
Vol 6 (2) ◽  
pp. 185
Author(s):  
Ardi Pujiyanta ◽  
Lukito Edi Nugroho ◽  
Widyawan Widyawan
Keyword(s):  
Resource Allocation ◽  
Grid Computing ◽  
Resource Utilization ◽  
Waiting Time ◽  
Resource Use ◽  
Waiting Times ◽  
Allocation Model ◽  
Technical Problems ◽  
Scheduling Policy ◽  
Proposed Model

Grid computing is a collection of heterogeneous resources that is highly dynamic and unpredictable. It is typically used for solving scientific or technical problems that require a large number of computer processing cycles or access to substantial amounts of data. Various resource allocation strategies have been used to make resource use more productive, with subsequent distributed environmental performance increases. The user sends a job by providing a predetermined time limit for running that job. Then, the scheduler gives priority to work according to the request and scheduling policy and places it in the waiting queue. When the resource is released, the scheduler selects the job from the waiting queue with a specific algorithm. Requests will be rejected if the required resources are not available. The user can re-submit a new request by modifying the parameter until available resources can be found. Eventually, there is a decrease in idle resources between work and resource utilization, and the waiting time will increase. An effective scheduling policy is required to improve resource use and reduce waiting times. In this paper, the FCFS-LRH method is proposed, where jobs received will be sorted by arrival time, execution time, and the number of resources needed. After the sorting process, the work will be placed in a logical view, and the job will be sent to the actual resource when it executes. The experimental results show that the proposed model can increase resource utilization by 1.34% and reduce waiting time by 20.47% when compared to existing approaches. This finding could be beneficially implemented in cloud systems resource allocation management.

Location and Distribution Management of Relief Centers: A Genetic Algorithm Approach

2015 ◽  
Vol 14 (04) ◽  
pp. 769-803 ◽  
Author(s):  
Medhi Najafi ◽  
Reza Zanjirani Farahani ◽  
Marisa P. De Brito ◽  
Wout Dullaert
Keyword(s):  
Genetic Algorithm ◽  
Logistic Model ◽  
Location Problem ◽  
Genetic Approach ◽  
Distribution Management ◽  
Management Efficiency ◽  
Proposed Model ◽  
Efficiency And Effectiveness ◽  
Genetic Algorithm Approach ◽  
Network Costs

Humanitarian logistics is regarded as a key area for improved disaster management efficiency and effectiveness. In this study, a multi-objective integrated logistic model is proposed to locate disaster relief centers while taking into account network costs and responsiveness. Because this location problem is NP-hard, we present a genetic approach to solve the proposed model.

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