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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1-10 订阅

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Proceedings of datacloud 2014: 5th international workshop on data intensive computing in the clouds - Held in Conjunction with SC 2014: the international Conference for High Performance computing, Networking, Storage and Analysis

Proceedings of DataCloud 2014: 5th International Workshop on...

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5th international workshop on data intensive computing in the clouds, datacloud 2014 - Held in Conjunction with the international Conference for High Performance computing, Networking, Storage and Analysis, SC 2014

ISBN: (纸本)9781479970346

the proceedings contain 5 papers. the topics discussed include: towards scalable distributed graph database engine for hybrid clouds;to overlap or not to overlap: optimizing incremental MapReduce computations for on-demand data upload;locality and network-aware reduce task scheduling for data-intensive applications;skyport - container-based execution environment management for multi-cloud scientific workflows;and integrating pig with harp to support iterative applications with fast cache and customized communication.

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Proceedings of datacloud 2016: 7th international workshop on data-intensive computing in the clouds - Held in conjunction with SC 2016: the international Conference for High Performance computing, Networking, Storage and Analysis

Proceedings of DataCloud 2016: 7th International Workshop on...

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

ISBN: (纸本)9781509061587

the proceedings contain 8 papers. the topics discussed include: asterism: Pegasus and Dispel4py hybrid workflows for data-intensive science;an ensemble-based recommendation engine for scientific data transfers;Pecos: a scalable solution for analyzing and managing qualitative data;data-intensive supercomputing in the cloud: global analytics for satellite imagery;model driven advanced hybrid cloud services for big data: paradigm and practice;improved data-aware task dispatching for batch queuing systems;a multi-tenant fair share approach to full-text search engine;and an efficient parallel implementation of a light-weight data privacy method for mobile cloud users.

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Towards Scalable Distributed Graph database Engine for Hybrid clouds 5

Towards Scalable Distributed Graph Database Engine for Hybri...

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datacloud 5th international workshop on data intensive computing in the clouds

作者： Dayarathna, Miyuru Suzumura, Toyotaro Nanyang Technol Univ Sch Comp Engn Singapore Singapore JST CREST Tokyo Japan IBM Res Dublin Ireland Univ Coll Dublin Dublin Ireland

ISBN: (纸本)9781479970346

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： Graph databases database management Distributed databases Graph theory Cloud computing

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To Overlap or Not to Overlap: Optimizing Incremental MapReduce Computations for On-Demand data Upload 5

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

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datacloud 5th international workshop on data intensive computing in the clouds

作者： Ene, Stefan Nicolae, Bogdan Costan, Alexandru Antoniu, Gabriel Univ Politehn Bucuresti Bucharest Romania IBM Res Dublin Ireland INSA Rennes IRISA Rennes France Inria Rennes Rennes France

ISBN: (纸本)9781479970346

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： MapReduce data management incremental processing

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Integrating Pig with Harp to Support Iterative Applications with Fast Cache and Customized Communication 5

Integrating Pig with Harp to Support Iterative Applications ...

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datacloud 5th international workshop on data intensive computing in the clouds

作者： Wu, Tak-Lon Koppula, Abhilash Qiu, Judy Indiana Univ Sch Informat & Comp Bloomington IN USA

ISBN: (纸本)9781479970346

Use of high-level scripting languages to solve big data problems has become a mainstream approach for sophisticated machine learning data analysis. Often data must be used in several steps of a computation to complete a full task. Composing default data transformation operators with the standard Hadoop MapReduce runtime is very convenient. However, the current strategy of using high-level languages to support iterative applications with Hadoop MapReduce relies on an external wrapper script in other languages such as Python and Groovy, which causes significant performance loss when restarting mappers and reducers between jobs. In this paper, we reduce the extra job startup overheads by integrating Apache Pig with the high-performance Hadoop plug-in Harp developed at Indiana University. this provides fast data caching and customized communication patterns among iterations for data analysis. the results show performance improvements of factors from 2 to 5.

关键词： Pig Iterative Algorithms Big data Language MapReduce

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Skyport - Container-Based Execution Environment Management for Multi-Cloud Scientific Workflows 5

Skyport - Container-Based Execution Environment Management f...

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datacloud 5th international workshop on data intensive computing in the clouds

作者： Gerlach, Wolfgang Tang, Wei Keegan, Kevin Harrison, Travis Wilke, Andreas Bischof, Jared D'Souza, Mark Devoid, Scott Murphy-Olson, Daniel Desai, Narayan Meyer, Folker Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Argonne IL USA Ericsson San Jose CA USA

ISBN: (纸本)9781479970346

Recently, Linux container technology has been gaining attention as it promises to transform the way software is developed and deployed. the portability and ease of deployment makes Linux containers an ideal technology to be used in scientific workflow platforms. Skyport utilizes Docker Linux containers to solve software deployment problems and resource utilization inefficiencies inherent to all existing scientific workflow platforms. As an extension to AWE/Shock, our data analysis platform that provides scalable workflow execution environments for scientific data in the cloud, Sky port greatly reduces the complexity associated with providing the environment necessary to execute complex workflows.

关键词： Containers

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Locality and Network-Aware Reduce Task Scheduling for data-intensive Applications 5

Locality and Network-Aware Reduce Task Scheduling for Data-I...

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datacloud 5th international workshop on data intensive computing in the clouds

作者： Arslan, Engin Shekhar, Mrigank Kosar, Tevfik SUNY Buffalo Buffalo NY 14260 USA Intel Corp Santa Clara CA USA

ISBN: (纸本)9781479970346

MapReduce is one of the leading programming frameworks to implement data-intensive applications by splitting the map and reduce tasks to distributed servers. Although there has been substantial amount of work on map task scheduling and optimization in the literature, the work on reduce task scheduling is very limited. Effective scheduling of the reduce tasks to the resources becomes especially important for the performance of data-intensive applications where large amounts of data are moved between the map and reduce tasks. In this paper, we propose a new algorithm (LoNARS) for reduce task scheduling, which takes both data locality and network traffic into consideration. data locality awareness aims to schedule the reduce tasks closer to the map tasks to decrease the delay in data access as well as the amount of traffic pushed to the network. Network traffic awareness intends to distribute the traffic over the whole network and minimize the hotspots to reduce the effect of network congestion in data transfers. We have integrated LoNARS into Hadoop-1.2.1. Using our LoNARS algorithm, we achieved up to 15% gain in data shuffling time and up to 3-4% improvement in total job completion time compared to the other reduce task scheduling algorithms. Moreover, we reduced the amount of traffic on network switches by 15% which helps to save energy consumption considerably.

关键词： data handling distributed processing optimisation data locality awareness data access data intensive applications distributed servers locality aware reduce task scheduling map task scheduling network aware reduce task scheduling optimization Benchmark testing Cost function Heart beat Scheduling Scheduling algorithms Servers TASK SCHEDULING Access to Information information retrieval Benchmark testing Heart beat Servers Scheduling algorithms Distributed processing data processing Cost functions Religious Missions distributed server Network Congestion Reduce Reduce

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Model Driven Advanced Hybrid Cloud Services for Big data: Paradigm and Practice 7

Model Driven Advanced Hybrid Cloud Services for Big Data: Pa...

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

作者： Yang, Xi Lehman, Tom University of Maryland Mid-Atlantic Crossroads College ParkMD20740 United States

ISBN: (纸本)9781509061587

Advanced hybrid cloud services aim to serve big data applications by bridging multi-provider high performance cloud resources including direct connects, hypervisor bypassing VM interfaces, on premise clusters, parallel storage and high speed inter-cloud networks. We present a new 'full-stack model driven orchestration' paradigm to integrate these diverse resources through semantic modeling and provide complex highend services through dynamic orchestrated workflows. We also present architectural design of a real-world orchestration system, VersaStack, that implements the paradigm as well as a case study for providing full-scale advanced hybrid cloud services in practice. © 2016 IEEE.

关键词： Big data

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Improved data-Aware Task Dispatching for Batch Queuing Systems 7

Improved Data-Aware Task Dispatching for Batch Queuing Syste...

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

作者： Li, Xieming Tatebe, Osamu Graduate School of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki Japan Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki Japan

ISBN: (纸本)9781509061587

this paper describes a data-aware task dispatching strategy called Improved data-Aware Task Dispatching (IDAD). this approach exploits the high-performance of local file access in non-uniform storage-access (NUSA) file systems and is based on our previous work, data-Aware Dispatch (DAD). In IDAD, the method of calculating data placement is revised, and the CPU factor is removed, as it has no major impact on performance but significantly reduces the difficulty for tweaking *** evaluated our approach in comparison with DAD and the stock FIFO Torque scheduler using BLAST benchmarks. We observed makespan reductions of 10.40% and 35.05% compared with DAD and stock FIFO schedulers, respectively. © 2016 IEEE.

关键词： Digital storage

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Asterism: Pegasus and Dispel4py Hybrid Workflows for data-intensive Science 7

Asterism: Pegasus and Dispel4py Hybrid Workflows for Data-In...

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

作者： Filgueira, Rosa Silva, Rafael Ferreira Da Krause, Amrey Deelman, Ewa Atkinson, Malcolm British Geological Survey Lyell Centre EdinburghEH144AP United Kingdom University of Southern California Information Sciences Institute Marina Del ReyCA United States EPCC University of Edinburgh EdinburghEH89LE United Kingdom School of Informatics University of Edinburgh EdinburghEH89LE United Kingdom

ISBN: (纸本)9781509061587

We present Asterism, an open source data-intensive framework, which combines the strengths of traditional workflow management systems with new parallel stream-based dataflow systems to run data-intensive applications across multiple heterogeneous resources, without users having to: re-formulate their methods according to different enactment engines;manage the data distribution across systems;parallelize their methods;co-place and schedule their methods with computing resources;and store and transfer large/small volumes of data. We also present the data-intensive workflows as a Service (DIaaS) model, which enables easy dataintensive workow composition and deployment on clouds using containers. the feasibility of Asterism and DIaaS model have been evaluated using a real domain application on the NSF-Chameleon cloud. Experimental results shows how Asterism successfully and efficiently exploits combinations of diverse computational platforms, whereas DIaaS delivers specialized software to execute data-intensive applications in a scalable, efficient, and robust way reducing the engineering time and computational cost. © 2016 IEEE.

关键词： Reusability

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