Open Access Open Access  Restricted Access Subscription Access

A load balancing model based on Cloud partitioning For GIS Data And Remote Sensing Data Management

DAVID JONES

Abstract


In present day days distributed computing is one of the biggest stage which gives stockpiling of information in ease and accessible forever finished the web. Be that as it may, the distributed computing has more basic issue like Security, stack adjusting. Distributed computing empower universal access shared pools of configurable assets like systems, servers, applications, stockpiling and administrations. Distributed computing permits clients and endeavors with different registering abilities to store and process information and give numerous administrations. Distributed computing is negligible administration exertion, productive and dependable. Load adjusting assume an essential part in dispersion of workload over numerous assets like PCs, CPUs, Network joins. Load adjusting plans to least assets utilize and most extreme throughput[3]. It enhances execution, increment effectiveness and unwavering quality. Load adjusting in distributed computing enhance execution. Better load adjusting in distributed computing gives client fulfillment. The calculation applies to stack adjusting system to enhance the effectiveness out in the open cloud.


Full Text:

PDF

References


2012,Techniques http://www.rightscale. com/infocenter/whitepapers/Load-Balancing-in-the-Cloud.pdf

Anju Baby and JenoLovesum,December 2013,“A Survey on Honey Bee Inspired Load Balancing of tasks in Cloud Computing.” International Journal of Engineering Research & Technology (IJERT), 2(12).

Dadi Sanyasi Naidu (2018). Big Data “Waht-How-Why” And Analytical Tools For Hydroinformatics. International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR ) ISSN:2581-4281 Vol 1, Issue4, June 2018, #Art.214, pp37-47

Dadi Sanyasi Naidu (2018). Soft Artificial Computing In GIS and Remote Sensing. International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR ) ISSN:2581-4281 Vol 1, Issue4, June 2018, #Art.222, pp122-127.

GaochaoXu, Junjie Pang, and XiaodongFu , February 2013,“A Load Balancing Model Based on Cloud Partitioning for the Public Cloud” Tsinghua Science And Technology ISSN 1007 - 0214 04 /12, 18(1), pp 34-39.

Google App Engine documentation link http://code.google.com/app engine/docs/what is Google app engine.html

MehakChoudhary, Dr. Deepti Gupta, Dimple Chandra, Chitvan Gupta, Feb 2017, A Comparative Study of different Load Balancing Algorithms for Cloud Computing. [11]Aayushi Sharma, AnshiyaTabassum, G.L. Vasavi, ShreyaHegde, Madhu B.R, , April 2017, A Comparative Study of Load Balancing Algorithms In Cloud Computing.

Ms. Parin V. Patel, Mr. Hitesh. D. Patel, Pinal. J. Patel, November- 2012,A Survey On Load Balancing In Cloud Computing, International Journal of Engineering Research & Technology (IJERT),1(9) ISSN: 2278-0181

Naidu, Dadi Sanyasi, and Peddada Jagadeeswara Rao. "Study on Sustainable Management of Groundwater Resources in Greater Visakhapatnam Municipal Corporation, Visakhapatnam District, India—A Hydro Informatics Approach." Proceedings of International Conference on Remote Sensing for Disaster Management. Springer, Cham, 2019.

Naidu, Dadi Sanyasi. "BIG DATA “WHAT-HOW-WHY” AND ANALYTICAL TOOLS FOR HYDROINFORMATICS." International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR) 1.1 (2018): 2.


Refbacks

  • There are currently no refbacks.