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检索条件"任意字段=2010 5th Workshop on Workflows in Support of Large-Scale Science, WORKS 2010"

共 18 条记录，以下是1-10 订阅

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2010 5th workshop on workflows in support of large-scale science, works 2010

2010 5th Workshop on Workflows in Support of Large-Scale Sci...

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2010 5th workshop on workflows in support of large-scale science, works 2010

ISBN: (纸本)9781424489893

the proceedings contain 9 papers. the topics discussed include: using SchedFlow for performance evaluation of workflow applications;on optimization of scientific workflows to support streaming applications in distributed network environments;network resource selection for data transfer processes in scientific workflows;BReW: blackbox resource selection for e-science workflows;streaming satellite data to cloud workflows for on-demand computing of environmental data products;taming complex bioinformatics workflows with weaver, makeflow, and starch;workflow-based comparison of two distributed computing infrastructures;a history-tracing XML-based provenance framework for workflows;linking multiple workflow provenance traces for interoperable collaborative science;and the 5th workshop on workflows in support of large-scale science in conjunction with SC 10.

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A history-tracing XML-based provenance framework for workflows

A history-tracing XML-based provenance framework for workflo...

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： Gerhards, M. Belloum, A. Berretz, F. Sander, V. Skorupa, S. FH Aachen University of Applied Science Jülich 52428 Germany University of Amsterdam Amsterdam 1098 XG Netherlands

ISBN: (纸本)9781424489893

the importance of validating and reproducing the outcome of computational processes is fundamental to many application domains. Assuring the provenance of workflows will likely become even more important with respect to the incorporation of human tasks to standard workflows by emerging standards such as WS-HumanTask. this paper addresses this trend by an actor-based workflow approach that actively support provenance. It proposes a framework to track and store provenance information automatically that applies for various workflow management systems. In particular, the introduced provenance framework supports the documentation of workflows in a legally binding way. the authors therefore use the concept of layered XML documents, i.e. history-tracing XML. Furthermore, the proposed provenance framework enables the executors (actors) of a particular workflow task to attest their operations and the associated results by integrating digital XML signatures.

关键词： XML

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On optimization of scientific workflows to support streaming applications in distributed network environments

On optimization of scientific workflows to support streaming...

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： Wu, Qishi Gu, Yi Lu, Xukang Zhu, Mengxia Brown, Patrick Lin, Wuyin Liu, Yangang University of Memphis Memphis TN 38152 United States Southern Illinois University Carbondale IL 62901 United States Brookhaven National Laboratory Upton NY 11793 United States

ISBN: (纸本)9781424489893

large-scale data-intensive streaming applications in various science fields feature complex DAG-structured workflows comprised of distributed computing modules with intricate inter-module dependencies. supporting such workflows in high-performance network environments and optimizing their throughput are crucial to collaborative scientific exploration and discovery. We formulate workflow mapping as a frame rate optimization problem and propose an efficient heuristic solution, which is integrated into the Condor-based Scientific Workflow Automation and Management Platform (SWAMP) in place of Condor's default mapping scheme. the SWAMP system is also augmented with several new components to improve the workflow management process. the performance superiority of the proposed solution is verified using both simulations and a real-life scientific workflow for climate modeling deployed in a distributed heterogeneous network environment.

关键词： Grid computing

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Taming complex bioinformatics workflows with weaver, makeflow, and starch

Taming complex bioinformatics workflows with weaver, makeflo...

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： thrasher, Andrew Carmichael, Rory Bui, Peter Yu, Li thain, Douglas Emrich, Scott Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States

ISBN: (纸本)9781424489893

In this paper we discuss challenges of common bioinformatics applications when deployed outside their initial development environments. We propose a three-tiered approach to mitigate some of these issues by leveraging an encapsulation tool, a high-level workflow language, and a portable intermediary. As a case study, we apply this approach to refactor a custom EST analysis pipeline. the Starch tool encapsulates program dependencies to simplify task specification and deployment. the Weaver language provides abstractions for distributed computing and naturally encourages code modularity. the Makeflow workflow engine provides a batch system agnostic engine to execute compiled Weaver code. To illustrate the benefits of our framework, we compare implementations, show their performance, and discuss benefits derived from our new workflow approach relative to traditional bioinformatics development.

关键词： Starch

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BReW: Blackbox resource selection for e-science workflows

BReW: Blackbox resource selection for e-Science workflows

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： Simmhan, Yogesh Soroush, Emad Van Ingen, Catharine Agarwal, Deb Ramakrishnan, Lavanya Computer Engineering Division University of Southern California Los Angeles CA United States Computer Science Department University of Washington Seattle WA United States eScience Group Microsoft Research San Francisco CA United States Advanced Computing for Science Department Lawrence Berkeley National Lab. Berkeley CA United States

ISBN: (纸本)9781424489893

workflows are commonly used to model data intensive scientific analysis. As computational resource needs increase for escience, emerging platforms like clouds present additional resource choices for scientists and policy makers. We introduce BReW, a tool enables users to make rapid, high-level platform selection for their workflows using limited workflow knowledge. this helps make informed decisions on whether to port a workflow to a new platform. Our analysis of synthetic and real escience workflows shows that using just total runtime length, maximum task fanout, and total data used and produced by the workflow, BReW can provide platform predictions comparable to whitebox models with detailed workflow knowledge.

关键词： BReW HPC blackbox resource selection cloud cloud computing clouds data analysis data intensive scientific analysis e-science workflows natural sciences computing planning resource platforms resource selection workflow workflow management software workflow migration

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Network resource selection for data transfer processes in scientific workflows

Network resource selection for data transfer processes in sc...

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： Zhao, Zhiming Grosso, Paola Koning, Ralph Van Der Ham, Jeroen De Laat, Cees System and Network Engineering Research Group Informatics Institute University of Amsterdam 1098XH Amsterdam Netherlands

ISBN: (纸本)9781424489893

Quality of the service (QoS) plays an important role in the life-cycle of scientific workflows for composing and executing applications. However, the quality of network services has so far rarely been considered in composing and executing scientific workflows. Currently, scientific applications tune the execution quality neglecting network resources, and by selecting only optimal software services and computing resources. One reason is that IP-based networks provide few possibilities for workflow systems to manage the service quality, and limit or prevent bandwidth reservation or network paths selection. We see nonetheless a strong need from scientific applications, and network operators, to include the network quality management in the workflow systems. In this paper, we discuss our ongoing research on this issue and present a semantic based solution to searching network resources with awareness of QoS requirements. the solution aims at complementing existing workflow systems on selecting network resources in the context of workflow composition, scheduling and execution when advanced network services are available. Our research is conducted in the context of the CineGrid project.

关键词： Data transfer

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Streaming satellite data to cloud workflows for on-demand computing of environmental data products

Streaming satellite data to cloud workflows for on-demand co...

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： Zinn, Daniel Hart, Quinn Ludäscher, Bertram Simmhan, Yogesh UC Davis Genome Center University of California Davis CA United States Department of Land Air and Water Resources University of California Davis CA United States Department of Computer Science University of California Davis CA United States Computer Science Department Univ of Southern California Los Angeles CA United States

ISBN: (纸本)9781424489893

Environmental data arriving constantly from satellites and weather stations are used to compute weather coefficients that are essential for agriculture and viticulture. For example, the reference evapotranspiration (ET0) coefficient, overlaid on regional maps, is provided each day by the California Department of Water Resources to local farmers and turf managers to plan daily water use. Scaling out single-processor compute/data intensive applications operating on realtime data to support more users and higher-resolution data poses data engineering challenges. Cloud computing helps data providers expand resource capacity to meet growing needs besides supporting scientific needs like reprocessing historic data using new models. In this article, we examine migration of a legacy script used for daily ET0 computation by CIMIS to a workflow model that eases deployment to and scaling on the Windows Azure Cloud. Our architecture incorporates a direct streaming model into Cloud virtual machines (VMs) that improves the performance by 130% to 160% for our workflow over using Cloud storage for data staging, used commonly. the streaming workflows achieve runtimes comparable to desktop execution for single VMs and a linear speed-up when using multiple VMs, thus allowing computation of environmental coefficients at a much larger resolution than done presently.

关键词： Agriculture

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Linking multiple workflow provenance traces for interoperable collaborative science

Linking multiple workflow provenance traces for interoperabl...

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2010 5th workshop on workflows in support of large-scale science, works 2010

作者： Missier, Paolo Ludäscher, Bertram Bowers, Shawn Dey, Saumen Sarkar, Anandarup Shrestha, Biva Altintas, Ilkay Anand, Manish Kumar Goble, Carole School of Computer Science University of Manchester United Kingdom Dept. of Computer Science University of California Davis CA United States Dept. of Computer Science Gonzaga University United States Dept. of Computer Science Appalachian State University United States San Diego Supercomputer Center University of California San Diego CA United States

ISBN: (纸本)9781424489893

Scientific collaboration increasingly involves data sharing between separate groups. We consider a scenario where data products of scientific workflows are published and then used by other researchers as inputs to their workflows. For proper interpretation, shared data must be complemented by descriptive metadata. We focus on provenance traces, a prime example of such metadata which describes the genesis and processing history of data products in terms of the computational workflow steps. through the reuse of published data, virtual, implicitly collaborative experiments emerge, making it desirable to compose the independently generated traces into global ones that describe the combined executions as single, seamless experiments. We present a model for provenance sharing that realizes this holistic view by overcoming the various interoperability problems that emerge from the heterogeneity of workflow systems, data formats, and provenance models. At the heart lie (i) an abstract workflow and provenance model in which (ii) data sharing becomes itself part of the combined workflow. We then describe an implementation of our model that we developed in the context of the Data Observation Network for Earth (DataONE) project and that can "stitch together" traces from different Kepler and Taverna workflow runs. It provides a prototypical framework for seamless cross-system, collaborative provenance management and can be easily extended to include other systems. Our approach also opens the door to new ways of workflow interoperability not only through often elusive workflow standards but through shared provenance information from public repositories.

关键词： Interoperability

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the 5th workshop on workflows in support of large-scale science in conjunction with SC 10

2010 5th Workshop on Workflows in Support of Large-Scale Sci...

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2010 5th workshop on workflows in support of large-scale science, works 2010 2010年

作者： Deelman, Ewa Taylor, Ian

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Making data analysis expertise broadly accessible through workflows

Making data analysis expertise broadly accessible through wo...

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6th workshop on workflows in support of large-scale science, works'11, Co-located with SC'11

作者： Hauder, Matheus Gil, Yolanda Sethi, Ricky Liu, Yan Jo, Hyunjoon Information Sciences Institute University of Southern California 4676 Admiralty Way Marina Del Rey CA 90292 United States Computer Science Department University of Southern California 941 Bloom Walk Los Angeles CA 90089-0781 United States

ISBN: (纸本)9781450311007

the demand for advanced skills in data analysis spans many areas of science, computing, and business analytics. this paper discusses how non-expert users reuse workflows created by experts and representing complex data mining processes for text analytics. they include workflows for document classification, document clustering, and topic detection, all assembled from components available in well-known text analytics software libraries. the workflows expose to non-experts expert-level knowledge on how these individual components need to be combined with data preparation and feature selection steps to make the underlying statistical learning algorithms most effective. the framework allows non-experts to easily experiment with different combinations of data analysis processes, represented as workflows of computations that they can easily reconfigure. We report on our experiences to date on having users with limited data analytic knowledge and even basic programming skills to apply workflows to their data. © 2010 ACM.

关键词： Data mining

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