Open Access
Ayushi Bhargava
Manish Bhardwa
- Student Department of Computer Science & Engineering, Poornima Institute of Engineering & Technology, Jaipur Rajasthan India
- Assistant Professor Department of Computer Science & Engineering, Poornima Institute of Engineering & Technology, Jaipur Rajasthan India
Abstract
The components associated with distributed computing are customers, datacenter and appropriated server. One of the principal issuesin distributed computing isload adjusting. Adjusting the heap intends to circulate the outstanding task at hand among a few hubs uniformly so no single hub will be over- burden. Burden can be of any kind that is it very well may be CPU load, memory limit or system load. Right now, introduced a design of burden adjusting and calculation which will additionally improve the heap adjusting issue by limiting the reaction time. Right now, have proposed the improved variant of existing managed load adjusting approach for distributed computing by comping the Randomization and covetous burden adjusting calculation. To check the presentation of proposed approach, we have utilized the cloud investigator test system (Cloud Analyst). Through reenactment examination, it has been discovered that proposed improved form of controlled burden adjusting approach has indicated better execution as far as cost, reaction time and information preparing time.
Keywords: CC–Cloud Computing, CA–Cloud Analyst, GA–genetic algorithm, ACO–ant colony optimization, SHC–stochastic algorithm, FCFS–first come first serve
[This article belongs to Recent Trends in Parallel Computing(rtpc)]
Browse Figures
References
1. Zhong Xu, Rong Huang, (2009) “Performance Study of Load Balancing Algorithms in Distributed Web Server Systems”, CS213 Parallel and Distributed Processing Project Report.
2. P. Warstein, H. Situ and Z. Huang (2010), “Load balancing in a cluster computer” In proceeding of the seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, IEEE.
3. Ms. Nitika, Ms. Shaveta, Mr. Gaurav Raj; “Comparative Analysis of Load Balancing Algorithms in Cloud Computing”, International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May 2012.
4. Y. Fang, F. Wang, and J. Ge, “A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing”, Web Information Systems and Mining, Lecture Notes in Computer Science, Vol. 6318, 2010, pages 271–277.
5. T.R.V. Anandharajan, Dr. M.A. Bhagyaveni” Co-operative Scheduled Energy Aware Load- Balancing technique for an Efficient Computational Cloud” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, March 2011.
6. T. Kokilavani J.J. College of Engineering & Technology and Research Scholar, Bharathiar University, Tamil Nadu, India” Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing” International Journal of Computer Applications (0975–8887) Volume 20–No. 2, April 2011.
7. Peter Mell, Timothy Grance, “The NIST Definition of Cloud Computing”, NIST Special Publication 800–145, September 2011.
8. Zenon Chaczko, Venkatesh Mahadevan, Shahrzad Aslanzadeh, Christopher Mcdermid (2011) “Availabity and Load Balancing in Cloud Computing” International Conference on Computer and Software Modeling IPCSIT vol.14 IACSIT Press, Singapore 2011.
9. A. Khiyaita, M. Zbakh, H. El Bakkali and Dafir El Kettani, “Load Balancing Cloud Computing: State of Art”, 9778-1-4673-1053-6/12/$31.00, 2012 IEEE.
10. Wayne Jansen Timothy Grance” Guidelines on Security and Privacy in Public Cloud Computing” NIST Draft Special Publication 800–144.
Recent Trends in Parallel Computing
| Volume | 8 |
| Issue | 2 |
| Received | September 30, 2021 |
| Accepted | October 22, 2021 |
| Published | January 22, 2023 |