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Time Optimization Workload Management in Hybrid Cloud Computing

K.Karthika 1 , K.Kanakambal 2 , R.Balasubramaniam 3

Section:Review Paper, Product Type: Journal Paper
Volume-3 , Issue-5 , Page no. 332-334, May-2015

Online published on May 30, 2015

Copyright © K.Karthika, K.Kanakambal ,R.Balasubramaniam . 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: K.Karthika, K.Kanakambal ,R.Balasubramaniam, “Time Optimization Workload Management in Hybrid Cloud Computing,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.332-334, 2015.

MLA Style Citation: K.Karthika, K.Kanakambal ,R.Balasubramaniam "Time Optimization Workload Management in Hybrid Cloud Computing." International Journal of Computer Sciences and Engineering 3.5 (2015): 332-334.

APA Style Citation: K.Karthika, K.Kanakambal ,R.Balasubramaniam, (2015). Time Optimization Workload Management in Hybrid Cloud Computing. International Journal of Computer Sciences and Engineering, 3(5), 332-334.

BibTex Style Citation:
@article{_2015,
author = {K.Karthika, K.Kanakambal ,R.Balasubramaniam},
title = {Time Optimization Workload Management in Hybrid Cloud Computing},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2015},
volume = {3},
Issue = {5},
month = {5},
year = {2015},
issn = {2347-2693},
pages = {332-334},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=528},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=528
TI - Time Optimization Workload Management in Hybrid Cloud Computing
T2 - International Journal of Computer Sciences and Engineering
AU - K.Karthika, K.Kanakambal ,R.Balasubramaniam
PY - 2015
DA - 2015/05/30
PB - IJCSE, Indore, INDIA
SP - 332-334
IS - 5
VL - 3
SN - 2347-2693
ER -

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Abstract

There is a need to improve the service reliability, security, availability, privacy and regulation complaint requirements in public cloud along with private cloud. By using hybrid cloud environment we can improve those concerns. If the workload is managed properly in the cloud environment, availability will be automatically increased. A better Load Balancing algorithm should be a fault tolerant one. Good Load Balance technique will improve the performance of the entire Cloud. However, there is no common method that can adapt to all possible different situations. However, all the existing Load Balancing algorithms are applied to the entire Cloud Environment. This creates an overhead in maintaining all the status of the nodes. In the hybrid cloud, the Intelligent workload factoring (IWF) is designed for proactive workload management. The intelligent workload factoring has a three components workload profiling, based load threshold and fast factoring. Based on the internet video workload management streaming, user can divide the workload management as two zones. Base workload as one zone, Flash crowd workload as another zone. The proactive workload management factoring is a fast frequent data item detection algorithm as factorized the data volume and also the data content. This application architecture is increased the Quality of Services (QoS). The workload factoring is mainly concentrate with the smooth workload at all time in data center and the data volume along with the data content.From the real trace driven simulation analysis and evaluation on hybrid cloud of local computing platform the user have a reliable workload prediction and achieve resource efficiency.

Key-Words / Index Term

Time Optimization, Cloud Computing, Cloud System

References

[1] “Amazon web services,” http://aws.amazon.com/.
[2] “Google app engine,” http://code.google.com/appengine/
[3] Hui Zhang, Guofei Jiang, Kenji Yoshihira, and Haifeng Chen(2014), “Proactive Workload Management in Hybrid Cloud Computing”, IEEE Transactions on Network and Service Management, VOL. 11, NO. 1, MARCH 2014
[4] Gaochao Xu, Junjie Pang & Xiaodong Fu(2013), “A Load balancing Model Based on Cloud Partitioning for Public Cloud ”, IEEE Transactions on Cloud Computing, Vol:18, No:1, pp:34-39.
[5] “Youtube,” http://www.youtube.com.
[6] “Gigaspaces,” http://www.gigaspaces.com.
[7] “Yahoo! video,” http://video.yahoo.com.