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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Open Access and Multidisciplinary

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A Survey on Dynamic Resource Allocation for Efficient Parallel Data Processing

B. Praveen Kumar, Santhosh Kumar, , ,
Affiliations
M.Tech (CSE), Sreyas Institute of Engineering & Technology
Associate Professor Dept.of Computer Science & Engineering, Sreyas Institute of Engineering & Technology
:NOT ASSIGNED


Abstract
As recently ad-hoc parallel information handling has developed to be one of the executioner applications for Infrastructure as a service (IaaS) cloud. Most of the Distributed computing organizations have begun to coordinate systems for parallel information making ready in their item portfolio, making it straightforward for clients to get to these Infrastructure and to convey their projects. The preparing constitution which are right now utilized have been intended for static, homogeneous group setups and slight the specific way of a cloud. Subsequently, the dispensed figure assets may be lacking for huge parts of the submitted work and superfluously increment handling time and cost. In this paper we discuss the challenges for proficient parallel information handling in cloud and introduce our exploration venture Nephele. Nephele is the first information preparing structure to expressly neglect the dynamic quality portion offered by today's IaaS mists for each, trip booking and execution. In this paper we talk about the open doors and challenges for effective parallel information preparing Specific errands of a handling occupation can be doled out to distinctive sorts of virtual machines which are consequently instantiated and ended amid the employment execution. In view of this new structure, we perform amplified assessments of Map Reduce-roused preparing occupations on an IaaS cloud framework and contrast the outcomes with the mainstream information handling system Hadoop.


Citation
B. Praveen Kumar,Santhosh Kumar."A Survey on Dynamic Resource Allocation for Efficient Parallel Data Processing". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 12,pp.1106-1112, December- 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I1252.pdf,


Keywords : Numerous Assignment Processing, High-Throughput Registering, Approximately Coupled Applications, Distributed computing.

References
[1] Amazon Web Services LLC. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2/, 2009. 
[2] Amazon Web Services LLC. Amazon Elastic MapReduce. http://aws.amazon.com/elasticmapreduce/ , 2009. 
[3] AmazonWeb Services LLC. Amazon Simple Storage Service. http://aws.amazon.com/s3/ , 2009. 
[4] M. Coates, R. Castro, R. Nowak, M. Gadhiok, R. King, and Y. Tsang. Maximum Likelihood Network Topology Identification from Edge-Based Unicast Measurements. SIGMETRICS Perform. Eval. Rev., 30(1):11–20, 2002. 
[5] R. Davoli. VDE: Virtual Distributed Ethernet. Testbeds and Research Infrastructures for the Development of Networks & Communities, International Conference on, 0:213–220, 2005. 
[6] J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI’04: Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation, pages 10–10, Berkeley, CA, USA, 2004. USENIX Association.
 [7] I. Raicu, I. Foster, and Y. Zhao. Many-Task Computing for Grids and Super computers. In ManyTask Computing on Grids and Supercomputers, 2008. MTAGS 2008. Workshop on, pages 1–11, Nov.2008. 
[8] M. Stillger, G. M. Lohman, V. Markl, and M.Kandil. LEO-DB2’s LEarning Optimizer. In VLDB ’01: Proceedings of the 27th International Conference on Very Large Data Bases, pages 19–28, San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc. 
[9] D. Warneke and O. Kao. Nephele: Efficient Parallel Data Processing in the Cloud. In MTAGS ’09: Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers, pages 1– 10, New York, NY, USA, 2009. ACM. 
[10] T. White. Hadoop: The Definitive Guide. O’Reilly Media, 2009.



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DOI:10.22362/ijcert


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