CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Enhanced Load Balancing Architecture using EE-GA

by Jaskirat Singh, Brahmaleen Kaur Sidhu
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 7
Year of Publication: 2015
Authors: Jaskirat Singh, Brahmaleen Kaur Sidhu
10.5120/ijca2015905703

Jaskirat Singh, Brahmaleen Kaur Sidhu . Enhanced Load Balancing Architecture using EE-GA. International Journal of Computer Applications. 131, 7 ( December 2015), 1-6. DOI=10.5120/ijca2015905703

@article{ 10.5120/ijca2015905703,
author = { Jaskirat Singh, Brahmaleen Kaur Sidhu },
title = { Enhanced Load Balancing Architecture using EE-GA },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 7 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number7/23458-2015905703/ },
doi = { 10.5120/ijca2015905703 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:26:52.354533+05:30
%A Jaskirat Singh
%A Brahmaleen Kaur Sidhu
%T Enhanced Load Balancing Architecture using EE-GA
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 7
%P 1-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is the long dreamed vision of computing as a utility, where data owners can remotely store their data in the cloud to enjoy on-demand high-quality applications and services from a shared pool of configurable computing resources. In the meantime, the cloud environment represents various difficulties. Two players in distributed computing situations, cloud suppliers and cloud clients, seek after diverse objectives; suppliers need to amplify income by accomplishing high asset usage, while clients need to minimize costs while meeting their execution prerequisites. Nonetheless, it is hard to allot resources in a commonly ideal manner because of the absence of data sharing between them. In addition, continually expanding heterogeneity and variability of the surroundings poses considerably harder difficulties for both sides. This paper describes the work which mainly aimed at enhancing the load balancing architecture where firstly genetic algorithm is been implemented with simple architecture [1]. Secondly, genetic algorithm is been implemented with enhanced architecture named as E-GA where job grouping is done according to job’s requirements. Finally the whole architecture is been enhanced by using job grouping method with enhanced genetic algorithm named as EE-GA. In enhanced genetic algorithm, artificial bee colony algorithm uses the output given by genetic algorithm as their input and provides efficient resources. Both E-GA and EE-GA have been successful in better resource utilization so that the jobs are handled in a more efficient manner and also time is saved [3]. All the comparison results prove that the EE-GA provides a more efficient way as compared to the others.

References
  1. David C. Wyld, “Moving to the cloud: An Introduction to Cloud Computing in Government”, IBM centre for The Business of Government e-Goverment Series, 2009.Mell, P. and Grance, T. (2010), “The NIST Definition of Cloud Computing”, NIST.
  2. Rajkumar Buyya and Karthik Sukumar, “Platforms for Building and Deploying Applications for Cloud Computing”, CSI Communications | May 2011, pp.6-11.
  3. X. Wang,B. Wang and J. Huang,”Cloud Computing and its Key Techniques”, in Proc. Of IEEE International Conference, pp.404-410, June 10-12, 2011
  4. N. Carr and F.Y. Yu, “IT is no longer important: the Internet great change of the high ground Cloud Computing”, CITIC Publishing House, October 2008.
  5. J.Srinivas, K.Venkata Subba Reddy, Dr.A.Moiz Qyser, “Cloud Computing Basics”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 5, July 2012
  6. Qi Zhang · Lu Cheng · Raouf Boutaba, “Cloud computing: state-of-the-art and research challenges”, J Internet Serv Appl (2010) 1: pp.7–18
  7. Dimpi Rani, “ A Comparative Study of SaaS, PaaS and IaaS in Cloud Computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 6, June 2014, pp. 458-461
  8. Pooja, Asmita Pandey, “Cloud Computing – An on Demand Service Platform”, International Conference on Advances in Management and Technology (iCAMT - 2013), pp. 5-9
  9. Introduction to Cloud Computing architecture, White Paper, 1st Edition, June 2009, Sun Microsystems, Inc.
  10. J. Yu and R. Buyya, “A Taxonomy of Workflow Management Systems for Grid Computing, Journal of Grid Computing”, vol. 34,no.3, pp.171-200, September 2005.
  11. R.P. Padhy, “Load Balancing in Cloud Computing Systems”, B.Tech Thesis, NIT, Rourkela, Computer Science and Engineering Department, Rourkela, 2011.
  12. R. Buyya,J. Broberg and A. M. Goscinski, “Cloud Computing:Principles and Paradigms”, pp. 664,February, 2011. ISBN: 978-0-470-88799-8.
  13. S. Haider et.al., “Fault Tolerance in Distributed Paradigms”, in Proc. of Fifth International Conference on Computer Communication and Management, IACSIT Press, Singapore,2011.
  14. Yogita Chawla and Mansi Bhonsle, “A Study on Scheduling Methods in Cloud Computing”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 1, Issue 3, September – October 2012, pp.12-17
  15. Harpreet Kaur, Amritpal Kaur, “A Survey on Fault Tolerance Techniques in Cloud Computing” in the International Journal of Science, Engineering and Technology (IJSET) (ISSN: 2348-4098), Volume 3 Issue 2 (April 2015), Pages Number – 411-415.
  16. T. A. Dumitras and P. Narsimhan, ”Fault-Tolerant Middleware and the Magical 1%”, ACM/IFIP/USENIX Conference on Middleware, Grenhole, France, November-December 2005.
  17. Vishonika Kaushal, Anju Bala, “Autonomic fault tolerance using haproxy in cloud environment,”I nternational Journal of Advanced Engineering Sciences and Technologies, vol. 7, 2010.
  18. Zaipeng Xie, Hongyu Sun and Kewal Saluja, “A Survey of Software Fault Tolerance Techniques”. 2004
  19. R. Buyya and M. Murshed, "GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. Concurrency and Computation: Practice and Experience‖, Wiley Press, 14(13- 15), Nov.-Dec.,2002.
  20. Sheheryar Malik, Fabrice Huet, “Adaptive Fault Tolerance in Real Time Cloud Computing”, IEEE World Congress on Services,2011.
  21. Alain Tchana, Laurent Broto, Daniel Hagimont, ―Fault Tolerant Approaches in Cloud Computing Infrastructures‖, The Eighth International Conference on Autonomic and Autonomous Systems, ICAS, 2012.
  22. Anju Bala, Inderveer Chana, “Fault Tolerance- Challenges, Techniques and Implementation in Cloud Computing”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 1, January 2012.
  23. T. C. Bressoud and F. B. "Schneider. Hypervisor-Based Fault Tolerance". In Proceedings of the 15th ACM Symposium on Operating Systems Principles (SOSP 1995), pages 1–11, Dec. 1995.
  24. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, " Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility. Future Generation Computer Systems", 25(6): Elsevier Science, Amsterdam, the Netherlands, pages 599-616, June 2009.
  25. Dilbag Singh, Jaswinder Singh, Amit Chhabra, “ Evaluating Overheads of Integrated Multilevel Checkpointing Algorithms in Cloud Computing Environment”, I. J. Computer Network and Information Security, 2012, 5, pp. 29-38
  26. Ekpe Okorafor, “A Fault-tolerant High Performance Cloud Strategy for Scientific Computing”, 2011 IEEE International Parallel & Distributed Processing Symposium, pp.1525-1532
  27. Kassian Plankensteiner, Radu Prodan, Thomas Fahringer, “A New Fault Tolerance Heuristic for ScientificWorkflows in Highly Distributed Environments based on Resubmission Impact”, 2009 Fifth IEEE International Conference on e-Science, pp.313-320
  28. Haimantee Mahato, Anjali Munjal, Shreya Chinchalikar, “ A System for Task Scheduling and Task Migration in Cloud Environment”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. IX (Mar-Apr. 2014), PP 115-118
  29. Engelmann, G. R. Vall´ee, T. Naughton, and S. L.Scott. Proactive fault tolerance using preemptive migration. In Euromicro International Conference on Parallel, Distributed, and network‐based Processing (PDP), pages 252–257, 2009.
  30. Y. Huang, C. Kintala, N. Kolettis, and N. Fulton, “Software rejuvenation: Analysis, module and applications,” in Proceedings of Fault‐Tolerant Computing Symposium FTCS‐25, june 1995
  31. Ravi Jhawar, Vincenzo Piuri and Marco Santambrogio, Member of IEEE, “Fault Tolerance Management in Cloud Computing: A System‐Level Perspective “, IEEE, 2012.
  32. Anjali D. Meshram, A.S.Sambare and S. D. Zade, “Fault Tolerance Model for Reliable Cloud Computing “, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN 2321 – 8169, Vol. 1, Issue: 7, July 2013.
  33. Prasenjit Kumar Patra, Harshpreet Singh & Gurpreet Singh, “Fault Tolerance Techniques and Comparative Implementation in Cloud Computing” International Journal of Computer Applications, Vol. 64, No.14, February 2013.
  34. A. Tchana and L. Broto and D. Hagimont, “Approaches to Cloud Computing Fault Tolerance,” Computer, Information and Telecommunication Systems (CITS), pp 1‐6, 2012.
  35. Yue Gao, Sandeep K. Gupta, Yanzhi Wang, Massoud Pedram, “An Energy-Aware Fault Tolerant Scheduling Framework for Soft Error Resilient Cloud Computing Systems”, 978-3-9815370-2-4/DATE14/©2014 EDAA IEEE
  36. Jing Liu, Xing-Guo Luo, Xing-Ming Zhang, Fan Zhang and Bai-Nan Li, “Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 3, January 2013, pp134-139
  37. Giuseppe Portaluri, Stefano Giordano, Dzmitry Kliazovich, Bernab´e Dorronsoro, “A Power Efficient Genetic Algorithm for Resource Allocation in Cloud Computing Data Centers”, 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), pp 58-63
Index Terms

Computer Science
Information Sciences

Keywords

Load Balancing Enhanced Cloud Architecture GA Resource Utilization.

Powered by PhDFocusTM