Writing programs that run EveryWare on the Computational Grid

Wolski, Rich; Brevik, John; Obertelli, Graziano; Spring, Neil; Su, Alan

doi:10.1109/71.963418

Cited by 16 publications

(15 citation statements)

References 24 publications

(31 reference statements)

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“…Comparing Figure 11 with Figure 13, it can be observed that the information specified in the visual model is used to create an RSL string (lines 4-8, Figure 13) that is used to submit the job to 'Everest' (lines [13][14][15][16]. Finally, Figure 14(a) shows the code that is generated for a job sub-task by the GridAnt model interpreter and Figure 14 File transfer specification.…”

Section: Illustrative Examplementioning

confidence: 98%

GAUGE: Grid Automation and Generative Environment

Hernández

Bangalore

Gray

et al. 2005

Concurrency and Computation

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SUMMARYThe Grid has proven to be a successful paradigm for distributed computing. However, constructing applications that exploit all the benefits that the Grid offers is still not optimal for both inexperienced and experienced users. Recent approaches to solving this problem employ a high-level abstract layer to ease the construction of applications for different Grid environments. These approaches help facilitate construction of Grid applications, but they are still tied to specific programming languages or platforms. A new approach is presented in this paper that uses concepts of domain-specific modeling (DSM) to build a high-level abstract layer. With this DSM-based abstract layer, the users are able to create Grid applications without knowledge of specific programming languages or being bound to specific Grid platforms. An additional benefit of DSM provides the capability to generate software artifacts for various Grid environments. This paper presents the Grid Automation and Generative Environment (GAUGE). The goal of GAUGE is to automate the generation of Grid applications to allow inexperienced users to exploit the Grid fully. At the same time, GAUGE provides an open framework in which experienced users can build upon and extend to tailor their applications to particular Grid environments or specific platforms. GAUGE employs domain-specific modeling techniques to accomplish this challenging task.

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Section: Illustrative Examplementioning

confidence: 98%

GAUGE: Grid Automation and Generative Environment

Hernández

Bangalore

Gray

et al. 2005

Concurrency and Computation

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“…One of our goals is to develop models of resource availability as they are fundamental to an understanding of overall platform behavior and scheduling policies. Our previous modeling and scheduling efforts [70,12,67,68,57,9] capture and make effective use of predicted performance levels, but do not include predictions of resource loss or failure. For initial Grid efforts, resource availability predictions did not prove critical.…”

Section: Develop Novel Models Of Complex Emerging Global Andmentioning

confidence: 99%

Models and Modeling Infrastructures for Global Computational Platforms

Wolski

Nurmi

Brevik

et al.

19th IEEE International Parallel and Distributed Processing Symposium

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“…Wolski et al, were the first to describe the characteristics for programs that run on grids and present a framework for running such programs [19]. More recently, [20] demonstrates a framework that transparently runs applications across resources in both cloud and grid platforms.…”

Section: Related Workmentioning

confidence: 99%

Case Studies in Designing Elastic Applications

Rajan

Thrasher

Abdul-Wahid

et al. 2013

2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing

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Abstract-Clusters, clouds, and grids offer access to large scale computational resources at low cost. This is especially appealing to scientific applications that require a very large scale to compete in the research space. However, the resources available across these platforms differ significantly in their availability, hardware, environment, performance, cost of use, and more. This requires the use of elastic applications that can adapt to the resources available at run-time, transparently handling heterogeneity and failures. In this paper, we present case studies of several elastic applications built using the Work Queue programming framework. From this experience, we offer six general guidelines for the design and implementation of elastic applications that run on thousands of processors.

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Writing programs that run EveryWare on the Computational Grid

Cited by 16 publications

References 24 publications

GAUGE: Grid Automation and Generative Environment

GAUGE: Grid Automation and Generative Environment

Models and Modeling Infrastructures for Global Computational Platforms

Case Studies in Designing Elastic Applications

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