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An Elastic Data Persisting Solution with High Performance for Spark

An Elastic Data Persisting Solution with High Performance fo...

引用

IEEE international Conference on Smart City

作者： Zhipeng Jiang Haopeng Chen Huan Zhou Jenny Wu School of Software Shanghai Jiao Tong University Data Center Group Intel Asia-Pacific R&D Ltd. Research and Development Cornerstone Research

ISBN: (纸本)9781509018949

With the increasing popularity of in-memory computing, Spark [1] has been highly successful in implementing large scale data intensive applications, especially for those that reuse data across multiple parallel operations. However due to the fact that Moore's Law has slowed down and memory resources are still costly, we presented an elastic data persisting solution for Spark, which enables data compression to save more heap space for JVM and reducing disk I/O throughput for faster data access. We mathematically derived the criteria for selecting the optimal data compression and persisting plan. Our evaluation of the preliminary prototype of this elastic data persisting solution shows that it can provide resource management recommendations by accounting for input data type, memory space and CPU resource, and can consistently yield high performance that accelerates Spark up to 6×.

关键词： Spark In-memory computing Data Compression Data Persisting Data Caching

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An Inter-Framework Cache for Diverse Data-Intensive computing Environments

An Inter-Framework Cache for Diverse Data-Intensive Computin...

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IEEE international Conference on Smart City

作者： Chun-Yu Wang Tzu-En Huang Yu-Tang Huang Jyh-Biau Chang Ce-Kuen Shieh Institution of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Department of Digital Applications University of Kang Ning

ISBN: (纸本)9781509018949

Hadoop distributed File System (HDFS) provides the storage to keep analyzing outcomes for the diversity of frameworks. MapReduce, Storm, and Spark each applies on batching, streaming and in-memory computing, all of them need the HDFS to collect and assemble results. For coping with Big-Data analysis in the real world, complicated platforms required working together. However, collaborating analysis on heterogeneous frameworks, the data must be write-through firstly and post-fetch upon HDFS that degrades the performance and lower the effectiveness of the whole system. For best our knowledge, no previous work had focused on inter-framework data caching. To solve above problems on collaborating analysis within heterogeneous frameworks such as Hadoop and Strom, in this paper, we propose a cache system upon YARN called " Inter-Framework Cache" (IF-cache). It uses in-memory cache to reserve temporary outcomes while also reducing the HDFS access frequency and improve analysis performance. Experiments had shown that Hadoop with IF-cache can reduce about 50% times comparing the no-cache one.

关键词： MapReduce Hadoop YARN Big data Cache mechanism

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A paradigm shift in GP-GPU computing: Task based execution of applications with dynamic data dependencies

A paradigm shift in GP-GPU computing: Task based execution o...

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6th international Workshop on Data-Intensive distributed computing, DIDC 2014, Held in Conjunction with the international ACM symposium on High Performance distributed computing, HPDC 2014

作者： Belviranli, Mehmet E. Chou, Chih-Hsun Bhuyan, Laxmi N. Gupta, Rajiv University of California Riverside Riverside CA United States

ISBN: (纸本)9781450329132

General purpose computing on GPUs have became increasingly popular over the last decade. Scientific applications with SIMD computation characteristics show considerable performance improvements when run on these massively parallel architectures. However, data dependencies across thread blocks significantly impact the degree of achievable parallelism by requiring global synchronization across multi-processors (SMs) inside the GPU. In order to efficiently run applications with inter-block data dependencies, we need fine-granular 'task-based execution models' that will treat SMs inside GPU as stand-alone parallel processing units. Such a scheme will enable efficient execution by utilizing all internal computation elements inside GPU and eliminating unnecessary waits during global barriers. In this paper, we propose a new, dynamic and 'all-in-GPU' task execution framework for executing both regular and irregular data-dependent applications on GPUs. Our runtime eliminates the need for global synchronization and minimize inter-SM communication through distributed queues. In our preliminary experiments run on a Tesla c2050 GPU, we have obtained up to 62% more speedup when compared to centralized queue approach. the overhead of system has been measured as low as 5%. Copyright 2014 ACM.

关键词： Graphics processing unit

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Big Data Analysis Using Hadoop Cluster 5

Big Data Analysis Using Hadoop Cluster

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5th IEEE international Conference on Computational Intelligence and computing Research (IEEE ICCIC)

作者： Saldhi, Ankita Goel, Abhinav Yadav, Dipesh Saldhi, Ankur Saksena, Dhruv Indu, S. Ctr Dev Telemat Mandi Rd Delhi 110030 India Aardee Solut Delhi 110059 India Designo Interior Delhi 110085 India Jamia Millia Islamia Dept Comp Engn Delhi 110025 India Carnegie Mellon Univ Pittsburgh PA 15213 USA Delhi Technol Univ Elect & Commun Engn Dept Delhi 110042 India

ISBN: (纸本)9781479939756

Industries keep a check on all statistics of their business and process this data using various data mining techniques to measure profit trends, revenue, growing markets and interesting opportunities to invest. these statistical records keep on increasing and increase very fast. Unfortunately, as the data grows it becomes a tedious task to process such a large data set and extract meaningful information. Also if the data generated is in various formats, its processing possesses new challenges. Owing to its size, big data is stored in Hadoop distributed File System (HDFS). In this standard architecture, all the DataNodes function parallel but functioning of a single Data Node is still in sequential fashion. this paper proposes to execute tasks assigned to a single Data Node in parallel instead of executing them sequentially. We propose to use a bunch of streaming multi-processors (SMs) for each single Data Node. An SM can have various processors and memory and all SMs run in parallel and independently. We process big data which may be coming from different sources in different formats to run parallelly on a Hadoop cluster, use the proposed technique and yield desired results efficiently. We have applied proposed methodology to the raw data of an industrial firm, for doing intelligent business, with a final objective of finding profit generated for the firm and its trends throughout a year. We have done analysis over a yearlong data as trends generally repeat after a year.

关键词： Big data Hadoop distributed data processing data mining Mappers Reducers

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Use of High Performance computing for the Rigorous Estimation of Very High Degree Spherical Harmonic Gravity Field Models

Use of High Performance Computing for the Rigorous Estimatio...

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IAG 5th international symposium on Gravity, Geoid and Height Systems (GGHS)

作者： Brockmann, Jan Martin Roese-Koerner, Lutz Schuh, Wolf-Dieter Univ Bonn Inst Geodesy & Geoinformat Dept Theoret Geodesy D-53115 Bonn Germany

ISBN: (纸本)9783319108377;9783319108360

the estimation of the global Earth's gravity field parameterized as a finite spherical harmonic series is computationally demanding. the computational effort depends on the one hand on the maximal resolution of the spherical harmonic expansion and on the other hand on the number of observations which might be several millions. All global high-resolution Earth's gravity field models currently available above degree and order 360 were computed introducing approximations, significantly reducing the numerical complexity. For example, the prerequisites for the orthogonality of the spherical harmonic base functions, leading to a block diagonal system of normal equations, are often introduced artificially by working with equally distributed data along parallels assuming constant accuracy. these methods do not allow for a complex modeling of the observation errors, or the inclusion of redundant observations. Within this contribution, we demonstrate how high-performance computers can be used for very high degree gravity field determination without introducing approximations. In addition, complex modeling of the observation errors is made possible within the algorithm to derive consistent error estimates for the spherical harmonic coefficients. Based on the high performance computing library ScaLAPACK, a gravity field solver was implemented which allows for the estimation of high degree gravity fields (e.g. degree and order 720, resulting in more than 500;000 unknown parameters) from various data sources with the direct solution method using assembly and solution of full normal equations.

关键词： Assembling of normal equations Data combination Global gravity field Massive parallel computations ScaLAPACK Spherical harmonics

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A distributed Speech Algorithm for Large Scale Data Communication Systems 28

A Distributed Speech Algorithm for Large Scale Data Communic...

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28th IEEE international parallel & distributed Processing symposium Workshops (IPDPSW)

作者： Xiong, Naixue Tong, Guoxiang Guo, Wenzhong Tan, Jian Wu, Guanning Hubei Univ Educ Hubei Coinnovat Ctr Basic Educ Technol Serv Sch Comp Sci Wuhan Hubei Peoples R China Univ Shanghai Sci & Technol Sch Optelect & Comp Engn Shanghai Key Lab Modern Opt Syst Shanghai Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China

ISBN: (纸本)9781479941162

Data-driven computing and using data for strategic advantages are exemplified by communication systems, and the speech intelligibility in communication systems is generally interrupted by interfering noise. this interference comes from the environmental noise, so we can reduce them intelligibility by masking the interested signal [1, 2]. An important work in communication systems is to extract speech from noisy speech and inhibiting background noise. In this paper, the subspace algorithm theory is introduced into a speech noise reduction system. We first analyze the principle of LMS adaptive speech noise reduction algorithm with the subspace algorithm, and then, we merge the subspace algorithm into the VS-LMS algorithm and propose a combined algorithm for an adaptive speech noise reduction system. Furthermore, we analyze the combined algorithm, which can decrease musical noise, as well as generate a suitable step-size factor to resolve the contradiction. this issue cannot be resolved by the current LMS algorithm [31], which has less convergence speed and larger residual noise than our system. Our simulation results demonstrate that our algorithm can get 3 to 10 times better than original algorithm in low SNR (-5 similar to 0db) and high SNR (0 similar to + 5db).

关键词： Adaptive Filter Subspace noise reduction algorithm Speech noise reduction system digital signal processing (DSP) LMS (least mean square)

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Proceedings - international symposium on Computers and Communications

Proceedings - International Symposium on Computers and Commu...

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ISCC 2014 Workshop - 5th IEEE international Workshop on Performance Evaluation of Communications in distributed Systems and Web based Service Architectures, PEDISWESA 2014

ISBN: (纸本)9781479942770

the proceedings contain 29 papers. the topics discussed include: distributed cooperative Q-learning for mobility-sensitive handover optimization in LTE SON;topology selection criteria for a virtual topology controller based on neural memories;GP-m: mobile middleware infrastructure for ambient assisted living;formal modeling and checking of an enhanced variant of the IEEE 802.11 CSMAICA protocol;measuring the Internet's threat level: a global-local approach;decomposition of memory consumption footprints to identify problematic threads;monitoring applications and services to improve the cloud foundry PaaS;an efficient MAC-signature scheme for authentication in XOR network coding;near-clouds: bringing public clouds to users' doorsteps;programmable mobile core network;a slot assignment for wireless body area networks;modeling, optimization and performance prediction of parallel algorithms;and automating the Hadoop configuration for easy setup in resilient cloud systems.

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Near-clouds: Bringing public clouds to users' doorsteps 19

Near-clouds: Bringing public clouds to users' doorsteps

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ISCC 2014 Workshop - 5th IEEE international Workshop on Performance Evaluation of Communications in distributed Systems and Web based Service Architectures, PEDISWESA 2014

作者： Mian, Rizwan Elgazzar, Khalid Khalifa, Shady Martin, Patrick Silberman, Gabriel Goldschmidt, Daniel School of Computing Queen's University Canada Dell Research United States CA Technologies Canada

ISBN: (纸本)9781479942770

the rate of data growth in many domains is straining our ability to manage and analyze it. Cloud computing appears as a promising platform for data-intensive computing because it offers 'infinite' resources on demand, and on a pay-as-you-go basis. Surprisingly, we observe that public clouds are not being used for 'serious' data-processing on a continuous basis except by the cloud vendors. We primarily attribute this observation to the long data transfer times over wide-area networks between the clients and the centralized public clouds. We introduce the idea of a near-cloud that brings a public cloud in the close proximity of a user to overcome the data transfer bottlenecks. the near-clouds also provide a unique opportunity to localize the privacy and security policies to boost confidence of a user in using shared resources for data processing. To the cloud providers, near-cloud ensures a minimum constant stream of revenue from dedicated clients. © 2014 IEEE.

关键词： Data transfer

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Wireless sensor network distributed data collection strategy based on the regional correlated variability of perceptive area

Wireless sensor network distributed data collection strategy...

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5th international symposium on IT in Medicine and Education, ITME 2013

作者： Zhang, Yongjun Chen, Enxiu Shandong Institute of Commerce and Technology China National Engineering Research Center for Agricultural Products Logistics Jinan China National Engineering Research Center for Agricultural Products Logistics Jinan China

ISBN: (纸本)9789400776173

On account of the energy saving problem in wireless sensor network (WSN), this paper proposes the method that classifies sensing area according to the similarity of variability of sensing in terms of fuzzy clustering. through the head of a cluster in a similar area to sample data to represent approximately collection of the regional sensing data. We through a test manifests data collection of partitioned similar sensing area will implement well in data monitoring and it will reduce the total energy consumption, reduce the computing task for data fusion and lengthen the WSN life circle. © Springer Science+Business Media Dordrecht 2014.

关键词： Wireless sensor networks

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Design of trust model based on behavior in cloud computing environment

Design of trust model based on behavior in cloud computing e...

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5th international symposium on IT in Medicine and Education, ITME 2013

作者： Zhang, Yong Sheng Tian, Ming Lv, Shen Juan Zhang, Yan Dong School of Information Science and Engineering Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University Jinan 250014 China

ISBN: (纸本)9789400776173

this paper studies the characteristics of cloud computing environment, based on the traditional access control management technology, proposes a reference model for access control management in the cloud computing, designs a model based on trust grade of behaviors and role control, its basic thought is through entity behaviors, or the results which entity's behaviors produced, and integrated calculation processing generates the entity of credible value, with this as basis determines or changes entity of trust grade, as to change the entity of role and authority, to reach the purpose of supervision and protection about single entity or even the whole cloud system. © Springer Science+Business Media Dordrecht 2014.

关键词： Cloud computing

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