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检索条件"任意字段=DataCloud 5th International Workshop on Data Intensive Computing in the Clouds"

共 26 条记录，以下是11-20 订阅

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data-intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery 7

Data-Intensive Supercomputing in the Cloud: Global Analytics...

引用

7th international workshop on data-intensive computing in the clouds, datacloud 2016

作者： Warren, Michael S. Skillman, Samuel W. Chartrand, Rick Kelton, Tim Keisler, Ryan Raleigh, David Turk, Matthew Descartes Labs 1925 Trinity Drive Los AlamosNM87544 United States School of Information Sciences University of Illinois Urbana-Champaign UrbanaIL61801 United States

ISBN: (纸本)9781509061587

We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in highperformance computing, we draw parallels between the early days of clustered computing systems and the current state of cloud computing and its potential to disrupt the HPC market. Using our own virtual file system layer on top of cloud remote object storage, we demonstrate aggregate read bandwidth of 230 gigabytes per second using 512 Google Compute Engine (GCE) nodes accessing a USA multi-region standard storage bucket. this figure is comparable to the best HPC storage systems in existence. We also present several of our application results, including the identification of field boundaries in Ukraine, and the generation of a global cloud-free base layer from Landsat imagery. © 2016 IEEE.

关键词： Satellite imagery

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An Ensemble-Based Recommendation Engine for Scientific data Transfers 7

An Ensemble-Based Recommendation Engine for Scientific Data ...

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7th international workshop on data-intensive computing in the clouds, datacloud 2016

作者： Agnew, William Fischer, Michael Foster, Ian Chard, Kyle Georgia Inst. of Tech 30332 North Ave NW AtlantaGA United States University of Wisconsin-Milwaukee MilwaukeeWI United States University of Chicago 5801 S Ellis Ave ChicagoIL United States

ISBN: (纸本)9781509061587

Big data scientists face the challenge of locating valuable datasets across a network of distributed storage locations. We explore methods for recommending storage locations ('endpoints') for users based on a range of prediction models including collaborative filtering and heuristics that consider available information such as user, institution, access history, endpoint ownership, and endpoint usage. We combine the strengths of these models by training a deep recurrent neural network on their predictions. Collectively we show, via analysis of historical usage from the Globus research data management service, that our approach can predict the next storage location accessed by users with 80.3% and 95.3% accuracy for top-1 and top-3 recommendations, respectively. Additionally, our heuristics can predict the endpoints that users will use in the future with over 75% precision and recall. © 2016 IEEE.

关键词： Collaborative filtering

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Pecos: A Scalable Solution for Analyzing and Managing Qualitative data 7

Pecos: A Scalable Solution for Analyzing and Managing Qualit...

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7th international workshop on data-intensive computing in the clouds, datacloud 2016

作者： Arora, Ritu Ba, Trung Nguyen Connors, Tiffany A. Texas Advanced Computing Center AustinTX United States University of Texas at Austin AustinTX United States Texas State University San MarcosTX United States

ISBN: (纸本)9781509061587

Large, heterogeneous, and complex data collections can be difficult to analyze and manage manually. there is a need for scalable and user-friendly approaches that can automate the analysis and management of such collections in a timely and efficient manner. To meet the aforementioned need, we are developing a system named Pecos which combines (1) an android application, (2) cloud computing middleware and resources, and (3) High Performance computing (HPC) platform and software, for performing large-scale data analysis through an easy-to-use interface. Currently, Pecos can be used to analyze and manage data collections residing on Google drive or on remote Linux systems that are accessible via SSH connection. Some of the steps in the data analysis and management process that are already enabled through Pecos are (1) content-based classification and clustering of documents, (2) filtering or searching the documents on the basis of a text pattern, (3) performing checksum analysis and metadata extraction, (4) supporting format conversion of documents, and, (5) developing visualizations for analyzing the collection. Work is under progress to (1) optimize the algorithms in Pecos for document analysis, (2) develop a recommendation system, and (3) integrate the functionality for analyzing data from social media. © 2016 IEEE.

关键词： data mining

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workshop Committee

Workshop Committee

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international workshop on data-intensive computing in the clouds (datacloud)

Provides a listing of current committee members and society officers.

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An Efficient Parallel Implementation of a Light-weight data Privacy Method for Mobile Cloud Users 7

An Efficient Parallel Implementation of a Light-weight Data ...

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7th international workshop on data-intensive computing in the clouds, datacloud 2016

作者： Bahrami, Mehdi Li, Dong Singhal, Mukesh Kundu, Ashish Clous Lab University of California Merced United States IBM Thomas J. Waston Research Center Yorktown HeightsNY United States

ISBN: (纸本)9781509061587

Cloud computing provides an opportunity to users to outsource their data and applications. However, data privacy is one of the key challenges for the users who are outsourcing data on some transparent cloud servers. data encryption is the best option to protect users' data privacy on the cloud. However, computation overheads of encryption methods could be expensive to some small computing machines, such as mobile or IoT devices with limited resources, such as battery. In our previous study, we developed a light-weight data Privacy Method (DPM) based on a chaos system that uses a Pseudo Random Permutation (PRP) to scramble the content of original data. Although the nature of PRP is against parallelization, we provide an efficient parallel algorithm to scramble a file while the file splits into multiple chunks. the parallel DPM avoids an adversary to access the original data (e.g., by using a brute-force attack), when the size of each scrambled data is large enough. In this paper, we accelerate DPM on a Graphic Processing Unit (GPU) by using NVIDIA CUDA platform for implementation. We assess the generated shuffle addresses from pseudo-random and the distribution of randomness when the computation on data is parallelized on a multiple GPU-cores. A set of rigorous evaluation results shows that the parallel DPM provides a superior performance over tradition DPM when the most time consuming of native CUDA parallel functions have monitored. We also perform a security analysis of parallel DPM to ensure it is secure and it is a cost effective model to protect users' data privacy in a cloud environment. © 2016 IEEE.

关键词： Graphics processing unit

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Evaluating the suitability of map reduce for surface temperature analysis codes

Evaluating the suitability of map reduce for surface tempera...

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2nd international workshop on data intensive computing in the clouds, datacloud'11, Held in Conjunction with SC'11

作者： Sudhakaran, Vinay Hong, Neil Chue EPCC University of Edinburgh JCMB Mayfield Road Edinburgh EH16 5AH United Kingdom

ISBN: (纸本)9781450311441

Processing large volumes of scientific data requires an efficient and scalable parallel computing framework to obtain meaningful information quickly. In this paper, we evaluate a scientific application from the environmental sciences for its suitability to use the MapReduce framework. We consider cccgistemp - a Python reimplementation of the original NASA GISS model for estimating global temperature change - which takes land and ocean temperature records from different sites, removes duplicate records, and adjusts for urbanisation effects before calculating the 12 month running mean global temperature. the application consists of several stages, each displaying differing characteristics, and three stages have been ported to use Hadoop with the mrjob library. We note performance bottlenecks encountered while porting and suggest possible solutions, including modification of data access patterns to overcome uneven distribution of input data. © 2011 ACM.

关键词： MapReduce

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

Near-clouds: Bringing public clouds to users' doorsteps

引用

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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Towards Scalable Distributed Graph database Engine for Hybrid clouds

Towards Scalable Distributed Graph Database Engine for Hybri...

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international workshop on data-intensive computing in the clouds (datacloud)

作者： Miyuru Dayarathna Toyotaro Suzumura Singapore/JST CREST Nanyang Technological University IBM Research University College Dublin / JST CREST

Large graph data management and mining in clouds has become an important issue in recent times. We propose Acacia which is a distributed graph database engine for scalable handling of such large graph data. Acacia operates between the boundaries of private and public clouds. Acacia partitions and stores the graph data in the private cloud during its initial deployment. Acacia bursts into the public cloud when the resources of the private cloud are insufficient to maintain its service-level agreements. We implement Acacia using X10 programming language. We describe how Top-K PageRank has been implemented in Acacia. We report preliminary experiment results conducted with Acacia on a small compute cluster. Acacia is able to upload 69 million edges LiveJournal social network data set in about 10 minutes. Furthermore, Acacia calculates the average out degree of vertices of LiveJournal graph in 2 minutes. these results indicate Acacias potential for handling large graphs.

关键词： Cloud computing Distributed databases Partitioning algorithms Engines Computer architecture Protocols

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Job and data clustering for aggregate use of multiple production cyberinfrastructures

Job and data clustering for aggregate use of multiple produc...

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5th international workshop on data-intensive Distributed computing, DIDC'12

作者： Maheshwari, Ketan Espinosa, Allan Katz, Daniel S. Wilde, Michael Zhang, Zhao Foster, Ian Callaghan, Scott Maechling, Phillip Mathematics and Computer Science Division Argonne National Laboratory Argonne IL United States Department of Computer Science University of Chicago Chicago IL United States Computation Institute University of Chicago Argonne National Laboratory Chicago IL United States Computation Institute University of Chicago Chicago IL United States Mathematics and Computer Science Division Argonne National Laboratory Argonne IL United States Southern California Earthquake Center University of Southern California Los Angeles CA United States

ISBN: (纸本)9781450313414

In this paper, we address the challenges of reducing the time-to-solution of the data intensive earthquake simulation workflow "CyberShake" by supplementing the high-performance parallel computing (HPC) resources on which it typically runs with distributed, heterogeneous resources that can be obtained opportunistically from grids and clouds. We seek to minimize time to solution by maximizing the amount of work that can be efficiently done on the distributed resources. We identify data movement as the main bottleneck in effectively utilizing the combined local and distributed resources. We address this by analyzing the I/O characteristics of the application, processor acquisition rate (from a pilot-job service), and the data movement throughput of the infrastructure. With these factors in mind, we explore a combination of strategies including partitioning of computation (over HPC and distributed resources) and job clustering. We validate our approach with a theoretical study and with preliminary measurements on the Ranger HPC system and distributed Open Science Grid resources. More complete performance results will be presented in the final submission of this paper. Copyright 2012 ACM.

关键词： Clustering algorithms

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To Overlap or Not to Overlap: Optimizing Incremental MapReduce Computations for On-Demand data Upload

To Overlap or Not to Overlap: Optimizing Incremental MapRedu...

引用

international workshop on data-intensive computing in the clouds (datacloud)

作者： Stefan Ene Bogdan Nicolae Alexandru Costan Gabriel Antoniu University Politehnica Bucharest Bucharest Romania IBM Research Damastown Mulhuddart Dublin Ireland IRISA / INSA de Rennes Campus de Beaulieu Rennes France Inria Rennes Campus de Beaulieu Rennes France

Research on cloud-based Big data analytics has focused so far on optimizing the performance and cost-effectiveness of the computations, while largely neglecting an important aspect: users need to upload massive datasets on clouds for their computations. this paper studies the problem of running MapReduce applications when considering the simultaneous optimization of performance and cost of both the data upload and its corresponding computation taken together. We analyze the feasibility of incremental MapReduce approaches to advance the computation as much as possible during the data upload by using already transferred data to calculate intermediate results. Our key finding shows that overlapping the transfer time with as many incremental computations as possible is not always efficient: a better solution is to wait for enough to fill the computational capacity of the MapReduce cluster. Results show significant performance and cost reduction compared with state-of-the-art solutions that leverage incremental computations in a naive fashion.

关键词： data transfer Cloud computing throughput Computational modeling Algorithm design and analysis Context data models

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