Performance and Analysis of Flow between Annular Space Surrounded by a Rotating Coaxial cylinder with Co-axial Cylindrical Porous Medium

Santosh Patil¹ , Ediga Lingappa² , Mothe Rakesh³

1. Dept. of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India.
2. Dept. of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India.
3. Dept. of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India.

Correspondence should be addressed to: santosh.p.shr@gmail.com .

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.5 , pp.10-15, Oct-2017

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.1015

Online published on Oct 30, 2017

Copyright © Santosh Patil, Ediga Lingappa, Mothe Rakesh . 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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Abstract :
Due to latencies, the hadoop map reduce are complicated to scale to multiple clouds. Because of latencies, the hadoop map reduce are difficult to scale to multiple clouds. Because of this problem, to improve the performance at variable load, it provides over-provisioning in internal cloud. Here we propose a Bstream - cloud bursting framework. It consists of two major features. They are Stream-processing in the external cloud. Hadoop in the internal cloud. These two features are used to realize inter-cloud map reduce. Stream processing in external cloud enables parallel uploading; processing and also parallel downloading of data can minimize network latencies. It guarantees service-level objective (SLO) of meeting job deadlines.

Key-Words / Index Term :
Bstream, Map Reduce, stream processing

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