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Map-Reduce Algorithm for Mining Outliers in the Large Data Sets using Twister Programming Model

Map-Reduce Algorithm for Mining Outliers in the Large Data S...

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international conference on Science, Engineering & Technology (ICSET’15);3rd international conference on Education, Literature, Management and Economics (ELME’15);3rd international conference on Food, Agriculture, Ecology and Medical Sciences (FAEMS’15)

作者： Subramanyam.RBV Sonam.Gupta National Institute of Technology Warangal Telangana India Department of Computer Science & Engineering National Institute of Technology Warangal

An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. For example, in credit card transaction data, outliers might indicate potential fraud;in network traffic data, outliers might represent potential intrusion attempts. Outlier mining is the most important data mining task whose goal is to find the observation which is dissimilar from the remaining set of data set. Traditional outlier detection method assumes data is centralized at single location but this assumption is not true in today's scenario because data set size is increasing day to day. Moreover new requirement of these methods must be applicable to data which might be distributed among different locations. Design of efficient parallel algorithms and frameworks are the key to meeting the scalability and performance requirements. Map reduce is a framework for processing large data sets with a parallel, distributed algorithm on a cluster. Iterative Map reduce programming model has simplified the implementations of many distributed data mining applications. In this work we design and realize a parallel Outlier mining algorithm based on iterative Map Reduce framework. This algorithm uses Twister programming model which is a light weight map reduce runtime.

关键词： Outlier mining,Iterative map Reduce,Twister Model,data mining,MRdriver

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A MapReduce Optimization Method on Hadoop Cluster

A MapReduce Optimization Method on Hadoop Cluster

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international conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration

作者： Xiaodong Wu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9781467383134

The MapReduce parallel and distributed computing framework has been widely applied in both academia and industry. MapReduce applications are divided into two steps: Map and Reduce. Then, the input data is divided into splits, which can be concurrently processed, and the amount of the splits determines the number of map tasks. In this paper, we present a regression-based method to compute the number of Map tasks as well as Reduce tasks such that the performance of the MapReduce application can be improved. The regression analysis is used to predict the executing time of MapReduce applications. Experimental results show that the proposed optimization method can effectively reduce the execution time of the applications.

关键词： MapReduce Hadoop Optimization Polynomial Regression

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Practical Near-Data processing for In-Memory Analytics Frameworks

Practical Near-Data Processing for In-Memory Analytics Frame...

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international conference on parallel Architecture and Compilation techniques (PACT)

作者： Mingyu Gao Grant Ayers Christos Kozyrakis Stanford University

The end of Dennard scaling has made all systemsenergy-constrained. For data-intensive applications with limitedtemporal locality, the major energy bottleneck is data movementbetween processor chips and main memory modules. For such workloads, the best way to optimize energy is to place processing near the datain main memory. Advances in 3D integrationprovide an opportunity to implement near-data processing (NDP) withoutthe technology problems that similar efforts had in the past. This paper develops the hardware and software of an NDP architecturefor in-memory analytics frameworks, including MapReduce, graphprocessing, and deep neural networks. We develop simple but scalablehardware support for coherence, communication, and synchronization, anda runtime system that is sufficient to support analytics frameworks withcomplex data patterns while hiding all thedetails of the NDP hardware. Our NDP architecture provides up to 16x performance and energy advantageover conventional approaches, and 2.5x over recently-proposed NDP systems. We also investigate the balance between processing and memory throughput, as well as the scalability and physical and logical organization of the memory system. Finally, we show that it is critical to optimize software frameworksfor spatial locality as it leads to 2.9x efficiency improvements for NDP.

关键词： Hardware Computer architecture Three-dimensional displays Instruction sets Random access memory Runtime

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Intelligence in the Era of Big Data: 4th international conference on Soft Computing, Intelligent Systems, and Information Technology, ICSIIT 2015, Bali, Indonesia, March 11-14, 2015. Proceedings 1

引用

丛书名： Communications in Computer and Information Science 516

2015年

作者： Rolly Intan, Chi-Hung Chi, Henry N. Palit, Leo W. Santoso (eds.)

ISBN: (数字)9783662467428

ISBN: (纸本)9783662467411;9783662467428

This book constitutes the refereed proceedings of the 4th international conference on Soft Computing, Intelligent Systems, and Information Technology, ICSIIT 2015, held in Bali, Indonesia, in March 2015. The 34 revised full papers presented together with 19 short papers, one keynote and 2 invited talks were carefully reviewed and selected from 92 submissions. The papers cover a wide range of topics related to intelligence in the era of Big Data, such as fuzzy logic and control system; genetic algorithm and heuristic approaches; artificial intelligence and machine learning; similarity-based models; classification and clustering techniques; intelligent data processing; feature extraction; image recognition; visualization techniques; intelligent network; cloud and parallel computing; strategic planning; intelligent applications; and intelligent systems for enterprise, government and society.

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Pallas: An Application-Driven Task and Network Simulation Framework

Pallas: An Application-Driven Task and Network Simulation Fr...

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IEEE international conference on Cluster Computing

作者： Yuming Ye Ziyang Li Dongsheng Li Yiming Zhang Feng Liu Yuxing Peng National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781467365994

With the help of simulation tools, users can evaluate new proposals in cluster environment efficiently. However, current cloud simulators cannot meet the needs of application-driven simulation scenarios. In this paper, we propose Pallas, a task and network simulation framework that supports various cloud applications. Task-aware network scheduling and network-perceived task placement algorithms can be easily implemented in Pallas. We present the architecture and main components of Pallas and evaluate its effectiveness by comparing algorithm improvements to the actual results.

关键词： Computational modeling Semantics Scheduling algorithms Clustering algorithms Yttrium Computer architecture

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Advanced commands and distributed data layout to enhance the SSD internal parallelism

Advanced commands and distributed data layout to enhance the...

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international conference on High Performance Computing & Simulation (HPCS)

作者： Soraya Zertal PRiSM Université de Versailles Versailles France

SSDs have been widely deployed in different areas and become competitive storage devices even for data-intensive applications. They have important performance and endurance requirements and their internal features provide a real potential to fulfil them. The multiple and independent SSD internal components allow parallel access to data at each of the four levels (package-chip-die-plane) but it relies completely on the data layout scheme. We proposed a data layout algorithm based only on the SSD basic operations. It distributes data up to the lowest level to exploit the fine grain internal parallelism and improves the SSD performance. In this paper, we also use advanced commands available on newer SSDs and request scheduling in combination with data layout scheme to provide up to the planes parallelism, taking into account both performance and endurance. The result is a new data layout algorithn to exploit the fine grain SSD internal parallelism. It respects the rules imposed by the wise use of advanced commands and the recommandations of maintaining a wide data distribution. The results show an improvement of performance and a Write Amplification (WA) factor very close to the one using basic operations which indicates a preserved endurance.

关键词： Layout parallel processing Performance evaluation Hardware Context distributed databases Reliability

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Spark-Gram: Mining Frequent N-grams Using parallel processing in Spark

Spark-Gram: Mining Frequent N-grams Using Parallel Processin...

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international conference on Advanced Computer Science and Information Systems

作者： Prasetya Ajie Utama Bayu Distiawan Faculty of Computer Science Universitas Indonesia Jakarta Indonesia

ISBN: (纸本)9781509003648

Mining sequence patterns in form of n-grams (sequences of words that appear consecutively) from a large text data is one of the fundamental parts in several information retrieval and natural language processing applications. In this work, we present Spark-gram, a method for large scale frequent sequence mining based on Spark that was adapted from its equivalent method in MapReduce called Suffix-σ. Spark-gram design allows the discovery of all n-grams with maximum length a and minimum occurrence frequency τ, using iterative algorithm with only a single shuffle phase. We show that Spark-gram can outperform Suffix-σ mainly when t is high but potentially worse when the value of a grows higher.

关键词： distributed computing hadoop mapreduce spark text mining

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MR-Graph: A Customizable GPU MapReduce

MR-Graph: A Customizable GPU MapReduce

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IEEE international conference on Cyber Security and Cloud Computing (CSCloud)

作者： Zhi Qiao Shuwen Liang Hai Jiang Song Fu Department of Computer Science and Engineering University of North Texas Department of Computer Science Arkansas State University

ISBN: (纸本)9781467393010

The MapReduce programming model has been widely used in Big Data and Cloud applications. Criticism on its inflexibility when being applied to complicated scientific applications recently emerges. Several techniques have been proposed to enhance its flexibility. However, some of them exert special requirements on applications, while others fail to support the increasingly popular coprocessors, such as Graphics processing Unit (GPU). In this paper, we propose MR-Graph, a customizable and unified framework for GPU-based MapReduce, which aims to improve the flexibility, scalability and performance of MapReduce. MR-Graph addresses the limitations and restrictions of the traditional MapReduce execution paradigm. The three execution modes integrated in MR-Graph facilitates users to write their applications in a more flexible fashion by defining a Map and Reduce function call graph. MR-Graph efficiently explores the memory hierarchy in GPUs to reduce the data transfer overhead between execution stages and accommodate big data applications. We have implemented a prototype of MR-Graph and experimental results show the effectiveness of using MR-Graph for flexible and scalable GPU-based MapReduce computing.

关键词： Graphics processing units Programming parallel processing Computational modeling Big data Cloud computing Data models

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A Novel parallel Computation Model with Efficient Local Memory Management for Data-Intensive applications

A Novel Parallel Computation Model with Efficient Local Memo...

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IEEE international conference on Cloud Computing, CLOUD

作者： Ahmed Abdulhakim Al-Absi Dae-Ki Kang Division of Computer & Information Engineering Dongseo University Busan Republic of Korea

The provisioning of high-performance computing infrastructure through cloud environments enables data intensive processing to be a viable solution. In this paper, we introduce a novel parallel computation model similar to MapReduce framework. The proposed parallelized model incorporates a parallel execution strategy in worker nodes to decrease execution response times in cloud environments. The parallelized model adopts efficient local memory management techniques in the worker nodes to reduce memory transfer overheads. For evaluation, we compared the proposed framework with the state of art Hadoop MapReduce framework. From experiments on benchmark datasets, it turns out that the parallelized model reduces the execution times by about 45.86%. Those experimental results indicate the efficiency and the scalability of proposed framework on cloud environments.

关键词： Computational modeling Cloud computing Data models Memory management Yarn Clouds Optimization

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Spatial data processing with MapReduce

Spatial data processing with MapReduce

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international conference on Industrial and Information Systems, ICIIS

作者： Tilani Gunawardena Annamaria Vicari Giansalvatore Mecca Department of Mathematics Computer Science and Economics University Of Basilicata Potenza Italy National Institute of Geophysics and Volcanology Italy

The current development of high performance parallel supercomputing infrastructures are pushing the boundaries of applications of science and are bringing new paradigms into engineering practices and simulations. Earthquake engineering is also one of the major fields, which benefits from above by looking for solutions in grid computing and cloud computing techniques. Generally, earthquake simulations involve analysis of petabytes of data. Analyzing these large amounts of data in parallel in thousands of nodes in computer clusters results in gaining high performances. Open source cloud solutions such as Hadoop MapReduce, which is highly scalable and capable of processing large amount of data rapidly in parallel on large clusters provide better solution compared to RDBDM. Both GPUs and MapReduce are designed to support vast data parallelism. For performance considerations, GPU computing could be adopted over low performing CPU systems. This paper discusses MapReduce system using Hadoop and Mars. Mars is a MapReduce framework on graphics processor. Hence, the proposition is to use GPU based systems for earthquake simulations in which Digital elevation model 3D data sets are fully materialized where scientist can make use of these data for various analysis and simulations.

关键词： Graphics processing units Feeds Mars

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