Maximizing availability for task scheduling in computational grid using genetic algorithm

2014 ◽  
Vol 27 (1) ◽  
pp. 193-210 ◽  
Author(s):  
Shiv Prakash ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Computational Grid

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.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

2021 ◽  
Vol 58 (5) ◽  
pp. 102676
Author(s):  
Samira Kanwal ◽  
Zeshan Iqbal ◽  
Fadi Al-Turjman ◽  
Aun Irtaza ◽  
Muhammad Attique Khan
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Task Scheduling ◽  
And Task

Adaptive Task Scheduling in Computational GRID Environments

2005 ◽  
pp. 880-890
Author(s):  
Manuel Hidalgo-Conde ◽  
Andrés Rodríguez ◽  
Sergio Ramírez ◽  
Oswaldo Trelles
Keyword(s):  
Task Scheduling ◽  
Computational Grid ◽  
Grid Environments
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