Application of Genetic Algorithms Task Scheduling in Cloud Computing

The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.

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A Survey of Various Task Scheduling Algorithms In Cloud Computing

i-manager s Journal on Computer Science ◽

10.26634/jcom.1.1.2273 ◽

2013 ◽

Vol 1 (1) ◽

pp. 1-7

Author(s):

V. Srikanth Reddy ◽

V. Krishna Reddy

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Scheduling Algorithms

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An Intensified Task Scheduling Algorithm for Balancing Load in Cloud Computing Environment

SSRN Electronic Journal ◽

10.2139/ssrn.3165291 ◽

2018 ◽

Author(s):

R. Barani ◽

S. Suguna

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Scheduling Algorithm ◽

Computing Environment ◽

Cloud Computing Environment ◽

Task Scheduling Algorithm

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

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999200424075719 ◽

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.

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