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Generalized Spring Tensor Algorithms: with Workflow Scheduling Applications in Cloud Computing

by Shahrzad Aslanzadeh, Zenon Chaczko
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 7
Year of Publication: 2013
Authors: Shahrzad Aslanzadeh, Zenon Chaczko
10.5120/14588-2823

Shahrzad Aslanzadeh, Zenon Chaczko . Generalized Spring Tensor Algorithms: with Workflow Scheduling Applications in Cloud Computing. International Journal of Computer Applications. 84, 7 ( December 2013), 15-17. DOI=10.5120/14588-2823

@article{ 10.5120/14588-2823,
author = { Shahrzad Aslanzadeh, Zenon Chaczko },
title = { Generalized Spring Tensor Algorithms: with Workflow Scheduling Applications in Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 7 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 15-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number7/14588-2823/ },
doi = { 10.5120/14588-2823 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:18.637306+05:30
%A Shahrzad Aslanzadeh
%A Zenon Chaczko
%T Generalized Spring Tensor Algorithms: with Workflow Scheduling Applications in Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 7
%P 15-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In Cloud Computing, designing an efficient workflow scheduling algorithm is considered as a main goal. Load balancing is one of the most sophisticated methodologies, which can optimize workflow scheduling by distributing the load evenly among available resources. A well-designed load balancing algorithm has significant impact on performance and output in Cloud Computing. Therefore, designing robust load balancing techniques to manage the networks' load has always been a priority. Researchers have proposed and examined different load balancing methods; there is, however, a large knowledge gap in adopting an efficient load balancing algorithm in the Cloud system. This paper describes how a generalized spring tensor, an evolutionary algorithm with mathematical apparatus, can be utilized for a more efficient and effective load management in Cloud Computing. Considering the fluctuation and magnitude of the load, a novel application of workflow scheduling is investigated in the context of various mathematical patterns. The preliminary results of the research show that defining the dependency ratio between workflow tasks in Cloud Computing, results in better resource management, maximized performance and minimized response time while dealing with customer's requests.

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Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Load balancing Evolutionary algorithm Workflow scheduling

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