scholarly journals Tuning Struggle Strategy in Genetic Algorithms for Scheduling in Computational Grids

2008 ◽  
Author(s):  
Fatos Xhafa ◽  
Bernat Duran ◽  
Ajith Abraham ◽  
Keshav P. Dahal
Keyword(s):  
Genetic Algorithms ◽  
Computational Grids

Using Genetic Algorithm for Scheduling Tasks of Computational Grids in the Gridsim Simulator

2014 ◽  
pp. 521-535
Author(s):  
João Phellipe ◽  
Carla Katarina ◽  
Francisco das Chagas ◽  
Dario Aloise
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Distributed System ◽  
Task Scheduling ◽  
Computational Grid ◽  
Computer Processing ◽  
Computational Grids ◽  
Processing Power

Computer processing power has evolved considerably in recent years. However, there are problems that still require many machines to perform a large amount of processing in a parallel and distributed way. In this context, the task scheduling in a distributed system present many algorithms. In this chapter, the authors present a scheduler based on genetic algorithms in order to distribute tasks more efficiently in a computational grid; it has been implemented in GRIDSIM, a computational grid simulator with the features and attributes of a real grid.

scholarly journals USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS

2006 ◽  
Vol 12 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Javier Carretero ◽  
Fatos Xhafa
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Job Scheduling ◽  
Practical Interest ◽  
Fine Tuning ◽  
Computational Grids ◽  
Grid Applications ◽  
Large Scale Grid ◽  
Improved Performance ◽  
Scale Grid

In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid‐based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments.

scholarly journals 2206 Genetic Algorithms on the Computational Grids

2001 ◽  
Vol 2001.11 (0) ◽  
pp. 126-129
Author(s):  
Yusuke TANIMURA ◽  
Tomoyuki HIROYASU ◽  
Mitsunori Miki
Keyword(s):  
Genetic Algorithms ◽  
Computational Grids

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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