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检索条件"任意字段=3rd International Conference on Parallel and Distributed Processing and Applications"

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A Study on Big Data processing Frameworks: Spark and Storm 3rd

A Study on Big Data Processing Frameworks: Spark and Storm

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3rd international conference on Smart Computing and Informatics (SCI)

作者： Deshai, N. Venkataramana, S. Sekhar, B. V. D. S. Srinivas, K. Varma, G. P. Saradhi JNTUK SRKR Engn Coll Dept Informat Technol Bhimavaram Andhra Pradesh India

ISBN: (纸本)9789813296909;9789813296893

Today's internet world impose a trade-off between Peta-byte to Exa-byte being created in digital computer world attributable enormous volume of unstructured datasets being generating from diverse social sites, IOT, Google, Twitter, Yahoo, monitoring surroundings through sensors, etc., is big data (BD). Because second to second doubles the datasets volume size but the shortage of smooth dynamic processing, analysis and scalability techniques. Because the recent high-speed decade we applied only extant methods and common tools about the gigabyte data process and perform computations on whole world huge data. Apache open free source Hadoop is the latest BD weapon can process zetta-byte dimensions of databases by its most developed and popular components as HDFS and map reduce (MR), to get done excellent storage features magnificent and reliable processing on zetta-byte of datasets. MR likes more famous software, popular framework for handling BD existing issues with full parallel, highly distributed, and most scalable manner. Despite, Hadoop, map and reduces tasks have more limitations like poor allocating custom resources, stream way processing, shortage of latency, the deficit of efficient performance, imperfection of optimization, the real-time trend of computations and diverse logical elucidation. We significant most modern progressive features computing procedures. This examination paper shows Apache fastest spark tool, world latest and fastest tool is apache storm has efficient frameworks to conquer those limitations.

关键词： Big data Hadoop Mapreduce Yarn Spark Storm

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Empowering domain experts to preprocess massive distributed datasets 23rd

Empowering domain experts to preprocess massive distributed ...

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23rd international conference on Business Information Systems, BIS 2020

作者： Behringer, Michael Hirmer, Pascal Fritz, Manuel Mitschang, Bernhard Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart70569 Germany

ISBN: (纸本)9783030533366

In recent years, the amount of data is growing extensively. In companies, spreadsheets are one common approach to conduct data processing and statistical analysis. However, especially when working with massive amounts of data, spreadsheet applications have their limitations. To cope with this issue, we introduce a human-in-the-loop approach for scalable data preprocessing using sampling. In contrast to state-of-the-art approaches, we also consider conflict resolution and recommendations based on data not contained in the sample itself. We implemented a fully functional prototype and conducted a user study with 12 participants. We show that our approach delivers a significantly higher error correction than comparable approaches which only consider the sample dataset. © Springer Nature Switzerland AG 2020.

关键词： Spreadsheets

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Data processing for managing remote sensing images 23

Data processing for managing remote sensing images

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23rd international conference on Computer Graphics and Vision, GraphiCon 2013

作者： Shi, Jianting Wang, Jiancai Zhao, Fujun College of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin China

ISBN: (纸本)9785804414024

This paper discusses the establishment, management and application of remote sensing image database. A systematical structure for remote sensing image database is presented and analyzed. This architecture is based on web service technology, rather than traditional Browser/Server or Client/Server model. To process and publish mass remote sensing images on the web, appropriate system framework and software platforms are adopted in order to establish stable website and create web map service. In our system, Oracle is used to store the remote sensing images and GIS data, ArcGIS Server to create web services and accomplish web applications, and ArcSDE to manage the geospatial data. WebGIS applications and enterprise applications are carried out by utilizing and developing the geospatial database. The distributed database system provides a new model to archive and manage geospatial images effectively. © 23rd international conference on Computer Graphics and Vision, GraphiCon 2013 - conference Proceedings. All rights reserved.

关键词： Web services

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parallel Scheduling of Data-Intensive Tasks 26th

Parallel Scheduling of Data-Intensive Tasks

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Meng, Xiao Golab, Lukasz Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9783030576752;9783030576745

Workloads with precedence constraints due to data dependencies are common in various applications. These workloads can be represented as directed acyclic graphs (DAG), and are often data-intensive, meaning that data loading cost is the dominant factor and thus cache misses should be minimized We address the problem of parallel scheduling of a DAG of data-intensive tasks to minimize makespan. To do so, we propose greedy online scheduling algorithms that take load balancing, data dependencies, and data locality into account. Simulations and an experimental evaluation using an Apache Spark cluster demonstrate the advantages of our solutions.

关键词： parallel scheduling Data-intensive tasks Caching

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Co-located and Orchestrated Network Fabric (CONF): An Automated Cloud Virtual Infrastructure for Social Network applications 25th

Co-located and Orchestrated Network Fabric (CONF): An Automa...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Shi, Zeshun Zhou, Huan Hu, Yang Koulouzis, Spiros Rubia, Carlos Zhao, Zhiming Univ Amsterdam Informat Inst Amsterdam Netherlands Agilia Ctr AGI Seville Spain

ISBN: (纸本)9783030483401;9783030483395

Cloud environments can provide virtualized, elastic, control-lable and high-quality on-demand infrastructure services for supporting complex distributed applications. However, existing IaaS (Infrastructure-as-a-Service) solutions mainly focus on the automated integration or deployment of generic applications;they lack flexible infrastructure planning and provisioning solutions and do not have rich support for the high service quality and trustworthiness required by social network applications. This paper introduces an automated cloud virtual infrastructure solution for social network applications, called Co-located and Orchestrated Network Fabric (CONF), which was conducted in a recently funded EU H2020 project ARTICONF. CONF aims to improve the existing infrastructure support in the DevOps lifecycle of social network applications to optimize QoS performance metrics as well as ensure fast recovery in the presence of faults or performance drops.

关键词： Cloud Virtual infrastructure Social network applications

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FLY: A Domain-Specific Language for Scientific Computing on FaaS 25th

FLY: A Domain-Specific Language for Scientific Computing on ...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Cordasco, Gennaro D'Auria, Matteo Negro, Alberto Scarano, Vittorio Spagnuolo, Carmine Univ Campania Dipartimento Psicol Caserta Italy Univ Salerno Dipartimento Informat Fisciano Italy

ISBN: (纸本)9783030483401;9783030483395

Cloud Computing is widely recognized as distributed computing paradigm for the next generation of dynamically scalable applications. Recently a novel service model, called Function-as-a-Service (FaaS), has been proposed, that enables users to exploit the computational power of cloud infrastructures, without the need to configure and manage complex computations systems. FaaS paradigm represents an opportunity to easily develop and execute extreme-scale applications as it allows fine-grain decomposition of the application with a much more efficient scheduling on cloud provider infrastructure. We introduce FLY, a domain-specific language for designing, deploying and executing scientific computing applications by exploiting the FaaS service model on different cloud infrastructures. In this paper, we present the design and the language definition of FLY on several computing (local and FaaS) back-ends: Symmetric multiprocessing (SMP), Amazon AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, and IBM Bluemix/Apache OpenWhisk. We also present the first FLY source-to-source compiler, publicly available on GitHub, which supports SMP and AWS back-ends.

关键词： Domain-Specific Languages Scientific computing parallel computing distributed computing Serverless computing Functions as a Service (FaaS)

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Parameter settings optimization in MapReduce Big Data processing using the MOPSO algorithm 3

Parameter settings optimization in MapReduce Big Data proces...

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3rd African conference on Software Engineering, ACSE 2020

作者： Etyang, Lennah Mwangi, Waweru Nderu, Lawrence School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Big data is a commodity which is highly valued in the entire globe. It is not just regarded as data but in the world of experts, we can derive intelligence from it. Because of it's characteristics which are Variety, Value, Volume, Velocity and the growing need of how it can be handled, organizations are facing difficulties in ensuring optimal as well as affordable processing and storage of large datasets. One of the already existing technology used for rapid processing together with storage in big data is known as Hadoop MapReduce. MapReduce is used in parallel and distributed computing environment for large scale data processing whereas Hadoop on the other hand is for running applications and storage of data in clusters of commodity hardware. Furthermore, Hadoop Mapreduce framework needs to tune more than 190 configuration parameters which are mostly done manually. Due to complex interactions and large spaces between parameters, manual tuning is not effective. Even worse, these parameters have to be tuned each and every time Hadoop MapReduce applications are run. The main objective of this research is to develop an algorithm which shall enhance performance by automatically optimizing parameter settings while MapReduce jobs are running. The algorithm employs Multi Objective Particle Swarm Optimization (MOPSO) concept which makes use of two objective functions in order to perform optimization in the parameters by searching for a pareto optimal solution. The results of the experiments have shown that the algorithm has remarkably improved MapReduce job performance in comparison to the use of default settings. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： MapReduce

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distributed Cube and Conquer with Paracooba 1

引用

23rd international conference on Theory and applications of Satisfiability Testing (SAT)

作者： Heisinger, Maximilian Fleury, Mathias Biere, Armin Johannes Kepler Univ Linz Linz Austria

ISBN: (数字)9783030518257

ISBN: (纸本)9783030518257;9783030518240

Cube and conquer is currently the most effective approach to solve hard combinatorial problems in parallel. It organizes the search in two phases. First, a look-ahead solver splits the problem into many sub-problems, called cubes, which are then solved in parallel by incremental CDCL solvers. In this tool paper we present the first fully integrated and automatic distributed cube-and-conquer solver Paracooba targeting cluster and cloud computing. Previous work was limited to multi-core parallelism or relied on manual orchestration of the solving process. Our approach uses one master per problem to initialize the solving process and automatically discovers and releases compute nodes through elastic resource usage. Multiple problems can be solved in parallel on shared compute nodes, controlled by a custom peer-to-peer based load-balancing protocol. Experiments show the scalability of our approach.

关键词： Cluster computing

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PTangle: A parallel Detector for Unverified Blockchain Transactions 20th

PTangle: A Parallel Detector for Unverified Blockchain Trans...

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20th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Victor, Ashish Christopher Jayanthi, Akhilarka Gopalakrishnan, Atul Anand Nagpal, Rahul PES Univ Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9783030602482;9783030602475

Tangle is a novel directed acyclic graph (DAG)-based distributed ledger preferred over traditional linear ledgers in blockchain applications because of better transaction throughput. Earlier techniques have mostly focused on comparing the performance of graph chains over linear chains and incorporating the Markov Chain Monte Carlo process in probabilistic traversals to detect unverified transactions in DAG chains. In this paper, we present a parallel detection method for unverified transactions. Experimental evaluation of the proposed parallel technique demonstrates a significant, scalable average speed-up of close to 70%, and a peak speed-up of approximately 73% for a large number of transactions.

关键词： Blockchain DAG Tangle Markov Chain Monte Carlo parallelism

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Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection

Word Embedding-based Context-sensitive Network Flow Payload ...

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international conference on Applied Machine Learning (ICAML)

作者： Yizhou Li Yijie Wang Li Cheng Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421263

Payload anomaly detection can discover malicious behaviors hidden in network packets. It is hard to handle payload due to its various possible characters and complex semantic context, and thus identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which applies the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method.

关键词： Art Semantics Clustering algorithms Intrusion detection Machine learning Feature extraction Task analysis

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