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A MapReduce-based approach for computing reachability preserving graph

A MapReduce-based approach for computing reachability preser...

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international conference on BioMedical Engineering and Informatics (BMEI)

作者： Guohui Ding Xujun Ma Chunlong Fan College of Computer Shenyang Aerospace University Shenyang China

Traditional computations of reachability preservation based on breadth-first search (BFS) results usually focus on small-scale datasets on stand-alone. In order to solve the bottlenecks of memory capacity and computing speed while traditional approaches manipulate large-scale datasets and intermediate data generated, we propose a MapReduce-based approach to compute the reachability preserving graph. Based on the distributed parallel computing platform with the MapReduce programming model, we first obtain the whole strongly connected components (SCCs) based on the BFS results of original graph dataset. For condensing SCCs, we present an updating method based on label propagation. Additionally, for further compression, equivalence classes are processed via analyzing BFS results based on reachability equivalence relation. the experiments show that the BFS-based parallel computation of reachability preservation can achieve a good compression ratio and speed up well on the Hadoop computing cluster.

关键词： Yttrium Programming parallel processing distributed databases Computational modeling Cloud computing Biomedical engineering

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A MapReduce Algorithm for Polygon Retrieval in Geospatial Analysis

A MapReduce Algorithm for Polygon Retrieval in Geospatial An...

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IEEE international conference on Cloud computing

作者： Qiulei Guo Balaji Palanisamy Hassan A. Karimi School of Information Sciences University of Pittsburgh

the proliferation of data acquisition devices like 3D laser scanners had led to the burst of large-scale spatial terrain data which imposes many challenges to spatial data analysis and computation. With the advent of several emerging cloud technologies, a natural and cost-effective approach to managing such large-scale data is to store and process such datasets in a publicly hosted cloud service using modern distributed computing paradigms such as MapReduce. For several key spatial data analysis and computation problems, polygon retrieval is a fundamental operation which is often computed under real-time constraints. However, existing sequential algorithms fail to meet this demand effectively given that terrain data in recent years have witnessed an unprecedented growth in both volume and rate. In this work, we present a MapReduce-based parallel polygon retrieval algorithm which aims at minimizing the IO and CPU loads of the map and reduce tasks during spatial data processing. Our proposed algorithm first hierarchically indexes the spatial terrain data using a quad-tree index, with the help of which, a significant amount of data is filtered out in the pre-processing stage based on the query object. In addition, a prefix tree based on the quad-tree index is built to query the relationship between the terrain data and query area in real time which leads to significant savings in both I/O load and CPU time. the performance of the proposed techniques is evaluated in a Hadoop cluster and the results demonstrate that the proposed techniques are scalable and lead to more than 35% reduction in execution time of the polygon retrieval operation over existing distributed algorithms.

关键词： Tin Indexes Spatial databases distributed databases Real-time systems Computational modeling Partitioning algorithms

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Performance Comparison of parallel Execution using GPU and CPU in SVM Training Session

Performance Comparison of Parallel Execution using GPU and C...

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international conference on Advanced Computer Science applications and technologies

作者： Nur Shakirah Md Salleh Muhammad Fahim Baharim Department of Systems and Networking Universiti Tenaga Nasional

ISBN: (纸本)9781509004256

Support Vector Machine (SVM) is a machine learning approach, which is used in a growing number of applications. SVM is a useful technique for data classification. this machine learning approach has been optimized using two (2) parallel computing approaches. this includes symmetric multiprocessor (SMP) approach and vector processor approach. the outcome performance of the implementation of symmetric multiprocessor approach and vector processor approach on SVM training session is the focus of this paper. We have carried out a performance analysis to benchmark between Central Processing Unit (CPU) and Graphics Processing Units (GPUs) optimization. the result shows the GPU optimization of SVM training duration achieves better performance than the CPU optimized program by 3.11 of speedup.

关键词： Component parallel computing OpenMP CUDA Support Vector Machine UCW dataset

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Large Scale distributed Localization Based on RSS and Mass-Spring Model

Large Scale Distributed Localization Based on RSS and Mass-S...

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international conference on P2P, parallel, Grid, Cloud and Internet computing (3PGCIC)

作者： Bart Praats Rafael Berkvens Glenn Ergeerts Maarten Weyn MOSAIC FTI University of Antwerp Antwerp Belgium

ISBN: (纸本)9781467394741

Large scale localization has gained interest in the last few years together with wireless sensor networks. applications such as a swarm of drones or a sensor network to control an environment already exist but do not cover scales up to thousands of nodes. In this paper, a method for large scale distributed localization is developed based on existing technologies including received signal strength and mass-spring model (MSM). First, we created a theoretical model based on specific indoor and outdoor tests. Based on this model, a scaled up simulation was done using an MSM-based algorithm. Using this simulation, an average error of up to 7.66 m could be achieved in a field of 100 m by 100 m.

关键词： Databases Computational modeling Wireless sensor networks Radio frequency Receiving antennas Wrist

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Two Class Fisher's Linear Discriminant Analysis Using MapReduce

Two Class Fisher's Linear Discriminant Analysis Using MapRed...

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international conference on Computer Modeling and Simulation, UKSIM

作者： Prajesh P. Anchalia Kaushik Roy Kunal Roy Department Of CSE R V College Of Engineering Bangalore India Department Of CSE Global Academy of Technology Bangalore India

ISBN: (纸本)9781479987146

Information explosion propelled by the exponential growth in digitised data is an unstoppable reality. To be able to extract relevant and useful knowledge from this voluminous data in order to make well-informed decision is a competitive advantage in the information age. However, the attempts to transform raw data into valuable knowledge face both data and computational intensive challenges. As a result, parallel and distributed computing is often strongly sought after to alleviate these challenges. While there are many distributed computing technologies being proposed over the years, MapReduce has gained an immense amount of interest in recent years due to its simplicity and superb scalability at low cost. In this paper we implement the fisher linear discriminant technique to reduce dimensionality and remove the so-called "noisy" data values from a given data set and project the data values in the direction of largest variance. Few of Fisher linear discriminant analysis' applications are Life Sciences, Face Recognition, Music and Speech classification. We implement the Fisher Linear Discriminant on a distributed computing environment and we conclude that it outperforms the sequential implementation.

关键词： Data mining distributed databases Data models Algorithm design and analysis Support vector machines Computational modeling

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the GENiC Architecture for Integrated Data Centre Energy Management 15

The GENiC Architecture for Integrated Data Centre Energy Man...

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IEEE international conference on Utility and Cloud computing (UCC)

作者： D. Pesch A. McGibney P. Sobonski S. Rea T. Scherer L. Chen T. Engbersen D. Mehta B. O'Sullivan E. Pages J. Townley D. Kasinathan J.I. Torrens V. Zavrel J. Hensen Cork Institute of Technology Ireland IBM Research – Zurich Rüschlikon Switzerland University College Cork Insight Centre Ireland Atos Spain Barcelona Spain United Technologies Research Centre – Cork Ireland Building Physics and Services TU Eindhoven The Netherlands

ISBN: (纸本)9780769556970

We present an architecture for integrated data centre energy management developed in the EC funded GENiC project. the architecture was devised to create a platform that can integrate functions for workload management, cooling, power management and control of heat recovery for future, highly efficient data centres. the architecture is based on a distributed systems approach that allows the integration of components developed by several entities through defined interfaces and data formats. We also present use cases for the architecture, a brief description of the project's prototypical implementation, evaluation metrics and some lessons learned.

关键词： Cooling Monitoring Optimization Heat recovery Renewable energy sources thermal management

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Midimew-Connected Gaussian Network

Midimew-Connected Gaussian Network

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international conference on Advanced Computer Science applications and technologies

作者： Nuha Hussain Antari M. M. Hafizur Rahman Rizal Mohd Nor Tengku Mohd Tengku Sembok Dept. of Computer Science KICT IIUM Cyber Security Center National Defense University Malaysia

ISBN: (纸本)9781509004256

Midimew-Connected Gaussian Network (MGN) is a hierarchical interconnection network which is able to interconnect millions of nodes in the network to achieve high performance and cost-effective design for massively parallel computing system (MPC). MGN consists of multiple basic modules with Wrap-around links (MIDIMEW) to form the higher level network, where the basic module of MGN is dense Gaussian network with additional links in its edge nodes. this paper focuses on the architecture design of MGN, and computes the performance parameters such as degree, arc connectivity, bisection width and wiring complexity. the results show that the MGN design has better performance than other network topologies such as hierarchical Gaussian network (HGN).

关键词： Massively parallel Computer Hierarchical Interconnection Network Midimew-Connected Gaussian Network (MGN) Network Performance

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Information Management and Platform Refinement for Big Data

Information Management and Platform Refinement for Big Data

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international conference on Cloud computing and Big Data (CCBD)

作者： Joey J. Syu Kevin Z. Ji Hui-I Hsiao Po-Hung Lin Data Analytics Technology & Applications Research Institute Institute for Information Industry Taipei Taiwan Department of Computer Science Trinity University San Antonio TX USA

Information management and related applications are being vigorously researched and developed. According to IDC research reports, the global market for information management is forecasted to grow by 30 percent annually (from a value of 8 billion US dollars in 2012, to a forecasted 24 billion US dollar value in 2016). Both reports explicitly identify the significant growth in the importance of information management and related services in the future. this paper will discuss and analyze our development in topic-specific applications, namely the collection, access, and enrichment of massive data. through the development of adaptive file access technology and the combination of distributed storage (i.e. Hive, HBase, etc.) with In-Memory algorithmic technologies, we will be able to have the system adjust data storage policies and increase efficiency through the identification of data types and some algorithmic properties. Based on the virtual machine migration technology, we utilize the mass data processing workload to dynamically migrate the virtual computing node. this can automatically adjust virtual resources to achieve outstanding calculation and localization of data through parallel execution of multiple mass data management processes, and maximize massive data processing efficiency. In the future we plan to progressively seek potential clients and partners, from which we will tailor a variety of field tests according to our clients' needs. And through this process, is where we will gradually achieve technical and application verifications.

关键词： Data mining Crawlers thesauri HTML Terminology distributed databases Big data

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Scalable Protocol for Remote Integrity Attestation of Cloud Based distributed Services 8

Scalable Protocol for Remote Integrity Attestation of Cloud ...

引用

8th IEEE international conference on Application of Information and Communication technologies (AICT)

作者： Azadiabad, Siamak Pedram, Hossein Abbasy, Mohammad Reza Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Univ Technol Malaysia Adv Informat Sch KL Skudai Johor Malaysia

ISBN: (纸本)9781479941209

although, using cloud-based services is increasing nowadays, but for many sensitive applications, users need to ensure that the processes are performing correctly in the cloud. In this paper, we propose a scalable method for remote attestation of distributed services integrity in the cloud, in comparison with other similar works. this method allows performing attestation at any time, and it is easy to implement. Users also can identify malicious cloud systems in order to report to the cloud service provider in most of the cases.

关键词： Cloud computing distributed Cloud Service Integrity Attestation Cryptography Digital Signature Endorsement

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SCALER: Scalable parallel File Write in HDFS 16

SCALER: Scalable Parallel File Write in HDFS

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16th IEEE international conference on Cluster computing (CLUSTER)

作者： Yang, Xi Yin, Yanlong Jin, Hui Sun, Xian-He IIT Dept Comp Sci Chicago IL 60616 USA Oracle Corp Parallel Execut Grp Redwood City CA USA

ISBN: (纸本)9781479955480

Two camps of file systems exist: parallel file systems designed for conventional high performance computing (HPC) and distributed file systems designed for newly emerged data-intensive applications. Addressing the big data challenge requires an approach that utilizes both high performance computing and data-intensive computing power. thus, HPC applications may need to interact with distributed file systems, such as HDFS. the N-1 (N-to-1) parallel file write is a critical technical challenge, because it is very common for HPC applications but HDFS does not allow it. this study introduces a system solution, named SCALER, which allows MPI based applications to directly access HDFS without extra data movement. SCALER supports N-1 file write at both the inter-block level and intra-block level. Experimental results confirm that SCALER achieves the design goal efficiently.

关键词： parallel I/O distributed file systems Optimization HDFS

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