scholarly journals Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing

2017 ◽  
pp. 177-189 ◽  
Author(s):  
Muhanad Tahrir Younis ◽  
Shengxiang Yang ◽  
Benjamin Passow
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling

scholarly journals Exploration based Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 5 (3) ◽  
pp. 91-100
Author(s):  
Hanaa Abdelrahman ◽  
Mohammed Bakri Bashir ◽  
Adil Yousif
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Large Scale ◽  
Job Scheduling ◽  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Adaptive Method ◽  
Virtual Organizations ◽  
Good Effect ◽  
Exploration Process

Grid computing presents a new trend to distribute and Internet computing to coordinate large scale heterogeneous resources providing sharing and problem solving in dynamic, multi- institutional virtual organizations. Scheduling is one of the most important problems in computational grid to increase the performance. Genetic Algorithm is adaptive method that can be used to solve optimization problems, based on the genetic process of biological organisms. The objective of this research is to develop a job scheduling algorithm using genetic algorithm with high exploration processes. To evaluate the proposed scheduling algorithm this study conducted a simulation using GridSim Simulator and a number of different workload. The research found that genetic algorithm get best results when increasing the mutation and these result directly proportional with the increase in the number of job. The paper concluded that, the mutation and exploration process has a good effect on the final execution time when we have large number of jobs. However, in small number of job mutation has no effects.

scholarly journals Genetic Algorithm for Independent Job Scheduling in Grid Computing

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Muhanad Tahrir Younis ◽  
Shengxiang Yang
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling ◽  
Computational Power ◽  
New Mutation ◽  
Complete Problem ◽  
Geographically Distributed ◽  
Np Complete ◽  
And Storage ◽  
Better Than

Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the literature.

High Exploitation Genetic Algorithm for Job Scheduling on Grid Computing

2016 ◽  
Vol 9 (3) ◽  
pp. 221-228 ◽  
Author(s):  
Walaa AbdElrouf ◽  
Adil Yousif ◽  
Mohammed Bakri Bashir
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Job Scheduling

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 Job Scheduling in Grid Computing

2013 ◽  
Vol 69 (22) ◽  
pp. 13-16 ◽  
Author(s):  
Khushboo Yadav ◽  
Deepika Jindal ◽  
Ramandeep Singh
Keyword(s):  
Grid Computing ◽  
Job Scheduling
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