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Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique: A Review

Shruti 1 , Meenakshi Sharma2

  1. GRIMT, Kurukshetra University, Haryana, India.
  2. GRIMT, Kurukshetra University, Haryana, India.

Correspondence should be addressed to: shrutigoel63@gmail.com.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-6 , Page no. 329-332, Jun-2017

Online published on Jun 30, 2017

Copyright © Shruti, Meenakshi Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Shruti, Meenakshi Sharma, “Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique: A Review,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.329-332, 2017.

MLA Style Citation: Shruti, Meenakshi Sharma "Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique: A Review." International Journal of Computer Sciences and Engineering 5.6 (2017): 329-332.

APA Style Citation: Shruti, Meenakshi Sharma, (2017). Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique: A Review. International Journal of Computer Sciences and Engineering, 5(6), 329-332.

BibTex Style Citation:
@article{Sharma_2017,
author = {Shruti, Meenakshi Sharma},
title = {Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique: A Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2017},
volume = {5},
Issue = {6},
month = {6},
year = {2017},
issn = {2347-2693},
pages = {329-332},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1350},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1350
TI - Task Scheduling and Resource Optimization in Cloud Computing Using Deadline-Aware Particle Swarm Technique: A Review
T2 - International Journal of Computer Sciences and Engineering
AU - Shruti, Meenakshi Sharma
PY - 2017
DA - 2017/06/30
PB - IJCSE, Indore, INDIA
SP - 329-332
IS - 6
VL - 5
SN - 2347-2693
ER -

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Abstract

Cloud computing is defined as that type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Cloud computing is used to achieve coherence and economy of scale over a network. Basically, cloud computing is a general term for the delivery of hosted services over the internet. Various characteristics which comes under cloud computing includes its location independent, multi-tenancy, its reliability and security, and its on-demand self service etc. Cloud Computing is spreading through IT world with innovative start-ups. Companies in the financial sector are also adopting cloud computing for specific workloads. Various strategies are used for optimization in cloud computing in which particle swarm optimization is one of them. It is used to achieve task scheduling algorithm. To achieve better results and performance, we used Particle Swarm Optimization.

Key-Words / Index Term

Cloud computing, architecture, scheduling, computing in IT sector, Particle Swarm Optimization

References

[1] Dr. M. Sridhar and Dr. G. Rama Mohan Babu, R.V.R & J.C College of
Engineering, Guntur, INDIA, 2015 IEEE International Advance Computing Conference (IACC).
[2] Zhi-hui Zhan, Jun Zhang, Yun Li, and Henry Chung, “Adaptive particle swarm optimization”, IEEE Transactions on System, Man, and Cybernetics, Vol. 39, No. 6, pp. 1362-1381, 2006
[3] Xingquan Zuo, Member, IEEE, Guoxiang Zhang, and Wei Tan, Member, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 2, APRIL 2014.
[4] Nuttapong Netjinda, Booncharoen Sirinaovakul, Tiranee Achalakul Departmet of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok, Thailand, Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (grant no. PHD/0031/2553).
[5] BU Yanping1, 2 ZHOU Wei3 YU Jinshou1 1.Research Institute of Automation, East China University of Science and Technology, Shanghai 200237 China; 2. School of Technology, Shanghai Jiaotong University, Shanghai 201101 China; 3. School of Business, East China University of Science and Technology, Shanghai 2002 37 China, 2008 International Symposium on Computer Science and Computational Technology.
[6] A. Salman, “Particle swarm optimization for task assignment Problem”, Microprocessors and Microsystems, Vol. 26, No.8, pp.363–371, 2009.
[7] Azadi Khalili and Seyed Morteza, School of Electrical and Computer Engineering Kashan University, Babamir, 2015 23rd Iranian Conference on Electrical Engineering (ICEE).
[8] L. Zhang, Y.H. Chen, R.Y Sun, S. Jing, and B. Yang, “A task scheduling algorithm based on PSO for Grid Computing", International Journal of Computational Intelligence Research, pp.37-43, 2008.
[9] Lizheng Guo, Shuguang Zhao, Shigen Shen, and Changyuan Jiang, “Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm”, Journal of Networks, Vol.7, No.3, 2012.
[10] ChienHung Chen, JennWei Lin, and SyYen Kuo, Fellow, IEEE.
[11] Zahraa Tarek, Magdy Zakria and Fatma A. Omara, “PSO Optimization algorithm for Task Scheduling on The Cloud Computing Environment”, international Journal of Computers and Technology, Vol. 13, No. 9, 2014.
[12] Himani, Global Institute of management & emerging technology, Amritsar, India and Harmanbir Singh Sidhu, Chandigarh Group Of Colleges, India, 2015 Second International Conference on Advances in Computing and Communication Engineering.