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Public Cloud Server partition based on location and Load

Authors: Megha Birthare, Rasna Sharma

Published in: IJRRETAS, Volume 2, Issue 9, pp. 1-4 (01 Sep 2016)

Impact Factor: 2.3 (2016)

License: CC BY 4.0 CC BY 4.0 (Open Access)

Certificate: View Certificate

Abstract

With growth of cloud computing load balancing is important impact on performance. Cloud computing efficiency depends on good load balancer. Many type of situation occur that time cloud partitioning is done by load balancer. Different type of situation needed different type of strategies for public cloud portioning using load balancer.in this paper we work on, partition of public cloud using two type of situation first is load status evaluation and second is cloud division rules. Load status evaluation is measure in number of cloudlets arrives at datacenter and cloud divisions rules are based on cloudlet come from which geographical location. On the basis of geographical location we partition public cloud and improve performance of load balancing in cloud computing. We implement pro

Keywords
  • Load Balancing
  • Public Cloud
  • Cloud Partition
  • Round Robin
References
  1. .Gaochao Xu, Junjie Pang, and Xiaodong Fu “ A Load Balancing Model Based on Cloud Partitioning for the Public Cloud”[Tsinghua Science and Technology, February 2013].
  2. . Abhijeet G Purohit et. ”Load balancing in public cloud by division of cloud based on the geographical location”[ International Journal of Research in Engineering and Technology ] Volume:03/2014 http://www.ijret.org
  3. . Ms.Shilpa D.More et.”Reviews of Load Balancing Based on Partitioning in Cloud Computing” [International Journal of Computer Science and Information Technologies, Vol. 5
  4. Yogita, DurgaToshniwala, “A Framework for Outlier Detection in Evolving Data Streams by Weighting Attributes in Clustering,” 2ndInternational Conference on Communication, Computing & Security, pp. 214–222, (ICCCS-2012).
  5. Yogita,DurgaToshniwal, “Unsupervised Outlier Detection in Streaming Data Using Weighted Clustering,” World Academy of Science, Engineering and Technology, Vol:6, Nov 2012.
  6. F. Angiull, F. Fassetti, “Distance-based outlier queries in data streams: the novel task and algorithms,” Data Mining and Knowledge Discovery, 20(2),2010, pp. 290–324.
  7. DimitriosGeorgiadis, Maria Kontaki, AnastasiosGounaris, Apostolos Papadopoulos, Kostas Tsichlas, YannisManolopoulos, “Continuous
How to Cite
APA: Birthare, M., & Sharma, R. (2016). Public Cloud Server partition based on location and Load. International Journal for Rapid Research in Engineering Technology & Applied Science, 2(9), 1–4. https://ijrretas.com/research-paper/public-cloud-server-partition-based-on-location-and-load
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Download Paper

Paper Id: IJRRETAS47

Publish Date: 2016-09-01

ISSN: 2455-4723

Publisher Name: ijrretas

About IJRRETAS

ijrretas is a leading open-access, peer-reviewed journal dedicated to advancing research in applied sciences and engineering. We provide a global platform for researchers to disseminate innovative findings and technological breakthroughs.

ISSN
2455-4723
Established
2015

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