Dynamic Optimization Scheduling Techniques for Huge Data Centres in Cloud Computing Using QPSO Techniques

Authors(2) :-R. Sundarajan, R. Arveena

Load-balanced scheduling for huge server in clouds, in which a lot of information should be exchanged much of the time among a great many interconnected servers, is a key and testing issue. Existing Openflow based scheduling schemes, be that as it may, statically set up routes only at the initialization stage of data transmissions, which suffers from dynamical flow distribution and changing network states in data centers and often results in poor system performance. A novel dynamical load-balanced scheduling(DLBS) approach for boosting the system throughput while adjusting workload progressively. Here how we calculate the performance of the time delay and then optimize the performance of virtual machine. So in this process we optimizing dynamic scheduling and the process is how efficiently allocate the cloudlet in virtual machine using Quantum behaved particle swarm optimization (QPSO) to provide better and more efficient scheduling routing which is beneficial for both user and service provider. We used cloudsim tool to analyse how it optimized compared then previous result so far.

Authors and Affiliations

R. Sundarajan
Associate professor, IT Department, KalasalingamUniversity, Virudhunagar, Tamilnadu, India
R. Arveena
PG Scholar, IT Department,Kalasalingam University,Virudhunagar, Tamilnadu, India

Dynamic Load balancing, QPSO, Cloud Computing, Optimization

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 219-224
Manuscript Number : ICASCT2535
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

R. Sundarajan, R. Arveena, " Dynamic Optimization Scheduling Techniques for Huge Data Centres in Cloud Computing Using QPSO Techniques", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 5, pp.219-224, May-June-2017.
Journal URL : http://ijsrst.com/ICASCT2535

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