iosr-JCE   Volume-2 ~ Issue-3

Abstract: In recent years ad-hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project Nephele. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution.
Key words: Cloud computing, Nephele, Sharing, Fish Algorithm, Feistel Networks, VMs.

[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. ttp://aws.amazon.com/elasticmapreduce/, 2009.
[3] AmazonWeb Services LLC. Amazon Simple Storage Service. http://aws.amazon.com/s3/, 2009.
[4] D. Battr´e, S. Ewen, F. Hueske, O. Kao, V. Markl, and D. Warneke.Nephele/PACTs: A Programming Model and Execution Framework for Web-Scale Analytical Processing. In SoCC '10: Proceedingsof the ACM Symposium on Cloud Computing 2010, pages 119–130, New York, NY, USA, 2010. ACM.
[5] R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib,S. Weaver, and J. Zhou. SCOPE: Easy and EfficientParallel Processing of Massive Data Sets. Proc. VLDB Endow., 1(2):1265–1276, 2008.
[6] H. Chih Yang, A. Dasdan, R.-L. Hsiao, and D. S. Parker. Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters. In SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Man

Abstract : Cluster computing is one of the most interesting innovations in the field of parallel computing in the recent past. Due to record low prices on hardware and the availability of free, open source software, massive amounts of computing power are available to the general population. This availability allows for the implementation of cluster computing that can serve as the function of one PC based computer. This paper focuses on the implementation and the use of small cluster computing of three computers. It is quite impossible for us to effort a costly supercomputer and knows its performance. To minimize cost we have to consider some issues, these are software choice, installation, configuration and networking. In addition to the implementation of the cluster computing here uses LINUX operating System and OSCAR (Open Source Cluster Application Resources) & MPICH (Implementation of MPI, the Standard for the message passing libraries) that is fully free.
Keywords - Cluster Computing, MPI, OSCAR, Parallel System, Super Computer

[1] Buyya, High performance Cluster Computing ( NJ: Prentice-Hall,1999).
[2] Message Passing Interface Forum. MPI: A Message Passing Interface. In Proc. Of Supercomputing '93, pages 878–883. IEEE Computer Society Press, November 1993.
[3] Benoît des Ligneris, Stephen L. Scott, Thomas Naughton, and Neil Gorsuch. Open Source Cluster Application Resources (OSCAR) : design, implementation and interest for the [computer] scientific community. In Proceeding of 17th Annual International Symposium on High Performance Computing Systems and Applications (HPCS 2003), pages 241–246, Sherbrooke, Canada, May 11-14, 2003.
[4] Benoît des Ligneris, Stephen L. Scott, Thomas Naughton, and Neil Gorsuch. Open Source Cluster Application Resources (OSCAR) : design, implementation and interest for the [computer] scientific community. In Proceeding of 17th Annual International Symposium on High Performance Computing Systems and Applications (HPCS 2003), pages 241–246, Sherbrooke, Canada, May 11-14, 2003.
[5] System Installation Suite (SIS),http://www.sisuite.org/.
[6] NSF/TFCC Workshop on Teaching Cluster Computing Wednesday, July 11th - Friday July 13th, 2001 Department of Computer Science University of North Carolina at Charlotte
[7] Richard Ferri. The OSCAR revolution. Linux Journal, (98), June 2002. http://www.linuxjournal.com/article.ph p?sid=5559.

Abstract: Noises are the unwanted information in an image, so they should be removed before further processing. Existing methods consider histogram based analysis which is globally varied one. A modified statistical measured based automatic noise type recognition technique is proposed in this paper. This has 2 phases including training phase and testing phase. The key role involves deduction of noise samples using filters like wiener, lee, median and extracts the statistical measures like kurtosis and skewness from samples. Kurtosis and skewness values exhibit behavior based on noise type. By using the statistical information and trained data we can classify the type of noise. Finally the noise type is identified and corresponding filter is applied. Thus noise eliminated image would give the desirable results during further processing. Experimental results show that the method is capable of accurately classifying the types of noise.
Keywords: Enhanced Noise Type Recognition, Kurtosis, Noise type identification, Skewness, Statistical features.

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