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7th International Conference on Advanced Computing (ICoAC)

作者： Govindarajan, Kannan Somasundaram, Thamarai Selvi Boulanger, David Kumar, Vivekanandan Suresh Kinshuk Athabasca Univ Edmonton AB Canada Anna Univ Madras Tamil Nadu India

ISBN: (纸本)9781509019335

Nowadays, big data has received attention from researchers, business industries, education, and scientific communities. Big data analytics has to deal with large scale data that consist of both structured and unstructured data. These data are to be handled properly, that is extracting, processing, and analyzing those data to obtain meaningful information from them in a limited time. To yield insightful information, the processing of big data analytics requires high performance computing system, storage, and network resources. Hence, it is essential to design a high performance computing infrastructure with sufficient bandwidth which is capable to handle the big data processing in an efficient manner. However, the current network architectures in those infrastructures, with predefined network policies, do not allow for just-in-time reconfiguration of the networking infrastructure as demanded by big data analytics. In addressing these limitations, Software-Defined Networking (SDN) offers the means to dynamically configure the network parameters, dynamically provision the networks, and the network itself can be sliced in an on-demand manner. This research aims to characterize SDN with respect to the demands of big data analytics in Cluster, Grid, and Cloud Computing resources. The main motivation behind this research study is to design and develop an intelligent framework named as Big Data Analytics Management system (BDAMS) for collectively managing the compute, storage, and network resources in Cluster, Grid, and Cloud infrastructure for big data analytics.

关键词： big data grid computing cloud computing cluster computing software defined networking distributed processing hadoop distributed file system

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A Novel Authentication Framework for hadoop

A Novel Authentication Framework for Hadoop

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International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering systems (ICAEES)

作者： Rahul, P. K. Kumar, T. Gireesh Amrita Vishwa Vidyapeetham Tifac Core Cyber Secur Coimbatore Tamil Nadu India

ISBN: (纸本)9788132221265;9788132221258

hadoop is an open-source file system used to process and carry Big data which is out of the reach of normal softwares to be work on. This paper analyses the data security issues in hadoop and proposes a new authentication framework for clients. The framework uses cryptographic functions such as public key cryptography, private key cryptography, hashing functions, and random number generator. This framework will define a new key for each client and authenticate all clients and services using this key. The authentication agent offers user data protection, way for privilege separations, and basic security needs for data stored in hadoop file system.

关键词： hadoop distributed file system Authentication protocols Unix Cryptographic functions

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A Survey of Tools and Applications in Big Data 9

A Survey of Tools and Applications in Big Data

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IEEE 9th International Conference on Intelligent systems and Control (ISCO)

作者： Menon, Sindhu P. Hegde, Nagaratna P. KLE Inst Technol Dept CSE Hubli India Vasavi Coll Engn Dept CSE Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479964802

Big data brings with it the promise of data-driven decision-making. Complexity, Privacy, Heterogeneity impede the progress of making valuable decisions from this voluminous data. In this paper, we have reviewed the challenges related to big data, the technologies available for the same. Data Analysis and Retrieval, Spurious data, Noise Accumulation are the other challenges. The discussions here are aimed at providing a comprehensive overview to this new age data. We have concluded this survey by throwing light on the open problems and research issues.

关键词： Big Data Dryad hadoop hadoop distributed file system MapReduce

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An Enhanced DACHE Model for the MapReduce Environment

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Procedia Computer Science 2015年 50卷 579-584页

作者： S. Sangavi A. Vanmathi R. Gayathri R. Raju P. Victer Paul P. Dhavachelvan Department of Information Technology Sri Manakula Vinayagar Engineering College Pondicherry Department of Computer Science Pondicherry University Puducherry India.

Big data refers to processing of enormous amount of unstructured data. The MapReduce and hadoop are open-source softwares for bigdata *** unstructured data are processed using MapReduce framework and hadoop as an environment, providing distributed file storage for absolute blobs of data. The intermediate data generated are left unstored and hindered in MapReduce framework. Dache refers a cache imbibing the data that is accessed through a request and reply protocol. In this paper, we propose an effective technique for enhancing the performance of Dache by introducing a cache optimization algorithm and a better page replacement technique.

关键词： hadoop distributed file system Data aware cache Database Management system,MapReduce,WorkingSet

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Toward A New HDFS Functionalities Using A Probabilistic distributed Algorithm

Toward A New HDFS Functionalities Using A Probabilistic Dist...

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SCIEI 2015 Paris Conference

作者： Ismail Hind Ali Dahmani FS–Abdelmalek Essaadi University

distributed systems are computing systems where a number of components cooperate by communicating over a *** systems are still working in a stand-alone system where the user interface,persistent data and applications resided in one ***,the majority of nowadays systems are designed to work in distributed *** mechanisms for the storage and the manipulation of large amount of data is one of the largest technological challenges in software systems research *** media and web services produce an impressive amount of data *** this context,hadoop distributed file system(HDFS) is an open source software framework for distributed storage and processing of very large data(Big Data).In this paper we introduce new functionalities for hadoop distributed file system using probabilistic distributed algorithms,our proposition is working in both homogenous and heterogeneous HDFS nodes which can reduce the communication cost.

关键词： distributed systems Big Data hadoop distributed file system Probabilistic distributed algorithms

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SCALABLE EVOLUTIONARY COMPUTATION FOR EFFICIENT INFORMATION EXTRACTION FROM REMOTE SENSED IMAGERY

SCALABLE EVOLUTIONARY COMPUTATION FOR EFFICIENT INFORMATION ...

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ISPRS Technical Commission I Symposium

作者： Almutairi, L. M. Shetty, S. Momm, H. G. Tennessee State Univ Dept Elect & Comp Engn Nashville TN 37209 USA Middle Tennessee State Univ Dept Geosci Murfreesboro TN 37132 USA

ISBN: (纸本)9781629935201

Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multi-channel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems;(i) data preparation: the generation of random spectral indices;and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.

关键词： genetic programming cloud computing hadoop MapReduce hadoop distributed file system

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Prominence of MapReduce in BIG DATA Processing

Prominence of MapReduce in BIG DATA Processing

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4th International Conference on Communication systems and Network Technologies (CSNT)

作者： Pandey, Shweta Tokekar, Vrinda Shri Vaishnav Inst Tech & Sci Indore India Inst Engn & Technol Indore India

ISBN: (纸本)9781479930708

Big Data has come up with aureate haste and a clef enabler for the social business;Big Data gifts an opportunity to create extraordinary business advantage and better service delivery. Big Data is bringing a positive change in the decision making process of various business organizations. With the several offerings Big Data has come up with several issues and challenges which are related to the Big Data Management, Big Data processing and Big Data analysis. Big Data is having challenges related to volume, velocity and variety. Big Data has 3Vs Volume means large amount of data, Velocity means data arrives at high speed, Variety means data comes from heterogeneous resources. In Big Data definition, Big means a dataset which makes data concept to grow so much that it becomes difficult to manage it by using existing data management concepts and tools. Map Reduce is playing a very significant role in processing of Big Data. This paper includes a brief about Big Data and its related issues, emphasizes on role of MapReduce in Big Data processing. MapReduce is elastic scalable, efficient and fault tolerant for analysing a large set of data, highlights the features of MapReduce in comparison of other design model which makes it popular tool for processing large scale data. Analysis of performance factors of MapReduce shows that elimination of their inverse effect by optimization improves the performance of Map Reduce.

关键词： Big Data MapReduce hadoop distributed file system Google file system hadoop

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A Metrics Based Analysis of Cloud Resource Management Techniques

A Metrics Based Analysis of Cloud Resource Management Techni...

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International Conference on Advanced Communication Control and Computing Technologies*

作者： Chowdhury, Abishi Tripathi, Priyanka Natl Inst Tech Teachers Training & Res Dept Comp Engg & Applicat Bhopal India

ISBN: (纸本)9781479939145

Cloud computing is getting popularity day by day. It provides different techniques for utilizing the power of resources. Most importantly, it provides a programmable infrastructure which can be modified accordingly. Due to its programmable infrastructure it is easy to scale. Cloud computing provides on demand services to the cloud user. Virtualization is the root of cloud computing. Cloud computing provides services to the users in the form of virtual resources. These resources can be the infrastructure, software, application and can be the data also. The management of these resources is very important. There are different techniques for managing these resources and allocating and taking back these resources from the users. In this paper, we have studied these techniques and provided important points of these techniques.

关键词： Virtual machine Map Reduce hadoop distributed file system Datacenters Response Time Load Balancing Private Cloud Hybrid Cloud Public Cloud

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An Efficient Dynamic Indexing and Metadata Based Storage in Cloud Environment 4

An Efficient Dynamic Indexing and Metadata Based Storage in ...

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International Conference on Recent Trends in Information Technology

作者： Anjanadevi, S. Vijayakumar, D. Srinivasagan, K. G. Natl Engn Coll Autonomous Dept Comp Sci & Engn PG Kovilpatti India

ISBN: (纸本)9781479949892

Cloud computing is an emerging, computing model wherein the tasks are allocated to software, combination of connections, and services accessed over a network. This connections and network of servers is collectively known as the cloud. In place of operating their own data centers, users might rent computing power and storage capacity from a service provider and pays only for what they use. Cloud storage is delivering the data storage as service. If the data is stored in cloud, it must provide the data access and heterogeneity. With the advances in cloud computing it allows storing of large number of images and data throughout the world. This paper proposes the indexing and metadata management which helps to access the distributed data with reduced latency. The metadata management can be enhanced for large scale file system applications. When designing the metadata, the storage location of the metadata and attributes is important for the efficient retrieval of the data. Indexes are used to quickly locate data without having to search over every location in storage. Based on these two models, the data can be easily fetched and the search time was reduced to retrieve the appropriate data.

关键词： Metadata Cloud Storage Index Google file system hadoop distributed file system

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A Time Based Analysis of Data Processing on hadoop Cluster 6

A Time Based Analysis of Data Processing on Hadoop Cluster

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6th International Conference on Computational Intelligence and Communication Networks

作者： Pal, Amrit Agrawal, Sanjay Natl Inst Tech Teachers Training & Res Dept Comp Engn & Applicat Bhopal India

ISBN: (纸本)9781479969296

Data when it becomes in that much amount that it cannot be managed by the traditional database management system then it is Bigdata. It is difficult to manage this much amount of the data. hadoop is a technological answer to the Big Data. Data storage and retrieval of information from the data is done by the hadoop distributed file system and the Map Reduce Programming model. MapReduce provides effective bench marks for retrieving the information from the Big Data. In this paper we present our experimental work done on the hadoop Cluster. We have analyzed the time required by the cluster for processing the data with increasing number of nodes into the cluster. We started with a single node and then increase the node by one each time. We have analyzed three types of time. The real time, user time, system time is analyzed.

关键词： MapReduce hadoop distributed file system Name Node Data Node Task Tracker Job Tracker

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