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Title: Network testbed creation and validation

Abstract

Embodiments of network testbed creation and validation processes are described herein. A "network testbed" is a replicated environment used to validate a target network or an aspect of its design. Embodiments describe a network testbed that comprises virtual testbed nodes executed via a plurality of physical infrastructure nodes. The virtual testbed nodes utilize these hardware resources as a network "fabric," thereby enabling rapid configuration and reconfiguration of the virtual testbed nodes without requiring reconfiguration of the physical infrastructure nodes. Thus, in contrast to prior art solutions which require a tester manually build an emulated environment of physically connected network devices, embodiments receive or derive a target network description and build out a replica of this description using virtual testbed nodes executed via the physical infrastructure nodes. This process allows for the creation of very large (e.g., tens of thousands of network elements) and/or very topologically complex test networks.

Inventors:
; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1347565
Patent Number(s):
9,600,386
Application Number:
14/289,853
Assignee:
Sandia Corporation SNL-A
DOE Contract Number:
AC04-94AL85000
Resource Type:
Patent
Resource Relation:
Patent File Date: 2014 May 29
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Thai, Tan Q., Urias, Vincent, Van Leeuwen, Brian P., Watts, Kristopher K., and Sweeney, Andrew John. Network testbed creation and validation. United States: N. p., 2017. Web.
Thai, Tan Q., Urias, Vincent, Van Leeuwen, Brian P., Watts, Kristopher K., & Sweeney, Andrew John. Network testbed creation and validation. United States.
Thai, Tan Q., Urias, Vincent, Van Leeuwen, Brian P., Watts, Kristopher K., and Sweeney, Andrew John. Tue . "Network testbed creation and validation". United States. doi:. https://www.osti.gov/servlets/purl/1347565.
@article{osti_1347565,
title = {Network testbed creation and validation},
author = {Thai, Tan Q. and Urias, Vincent and Van Leeuwen, Brian P. and Watts, Kristopher K. and Sweeney, Andrew John},
abstractNote = {Embodiments of network testbed creation and validation processes are described herein. A "network testbed" is a replicated environment used to validate a target network or an aspect of its design. Embodiments describe a network testbed that comprises virtual testbed nodes executed via a plurality of physical infrastructure nodes. The virtual testbed nodes utilize these hardware resources as a network "fabric," thereby enabling rapid configuration and reconfiguration of the virtual testbed nodes without requiring reconfiguration of the physical infrastructure nodes. Thus, in contrast to prior art solutions which require a tester manually build an emulated environment of physically connected network devices, embodiments receive or derive a target network description and build out a replica of this description using virtual testbed nodes executed via the physical infrastructure nodes. This process allows for the creation of very large (e.g., tens of thousands of network elements) and/or very topologically complex test networks.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 21 00:00:00 EDT 2017},
month = {Tue Mar 21 00:00:00 EDT 2017}
}

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  • Embodiments of network testbed creation and validation processes are described herein. A "network testbed" is a replicated environment used to validate a target network or an aspect of its design. Embodiments describe a network testbed that comprises virtual testbed nodes executed via a plurality of physical infrastructure nodes. The virtual testbed nodes utilize these hardware resources as a network "fabric," thereby enabling rapid configuration and reconfiguration of the virtual testbed nodes without requiring reconfiguration of the physical infrastructure nodes. Thus, in contrast to prior art solutions which require a tester manually build an emulated environment of physically connected network devices,more » embodiments receive or derive a target network description and build out a replica of this description using virtual testbed nodes executed via the physical infrastructure nodes. This process allows for the creation of very large (e.g., tens of thousands of network elements) and/or very topologically complex test networks.« less
  • Well-controlled experiments that directly compare seasonal algal productivities across geographically distinct locations have not been reported before. To fill this gap, six cultivation testbed facilities were chosen across the United States to evaluate different climatic zones with respect to algal biomass productivity potential. The geographical locations and climates were as follows: Southwest, desert; Western, coastal; Southeast, inland; Southeast, coastal; Pacific, tropical; and Midwest, greenhouse. The testbed facilities were equipped with identical systems for inoculum production and open pond operation and methods were standardized across all testbeds to ensure accurate measurement of physical and biological variables. The ability of the testbedmore » sites to culture and analyze the same algal species, Nannochloropsis oceanica KA32, using identical pond operational and data collection procedures was evaluated during the same seasonal timeframe. This manuscript describes the results of a first-of-its-kind coordinated testbed validation field study while providing critical details on how geographical variations in temperature, light, and weather variables influenced algal productivity, nitrate consumption, and biomass composition. We found distinct differences in growth characteristics due to the geographic location and the resulting climatic and seasonal conditions across the sites, with the highest productivities observed at the desert Southwest and tropical Pacific regions, followed by the Western coastal region. The lowest productivities were observed at the Southeast inland and Midwest greenhouse locations. These differences in productivities among the sites correlated with the differences in pond water temperature and available solar radiation. In addition two sites, the tropical Pacific and Southeast inland experienced unusual events, spontaneous flocculation, and unusually cold and wet (rainfall) conditions respectively, that negatively affected outdoor algal growth. In addition, minor variability in productivity was observed between the different experimental treatments at each site, much smaller compared to differences due to geographic location. Finally, the successful demonstration of the coordinated and standardized operation of the testbed sites established a rigorous basis for future validation of algal strains and operational conditions and protocols across a geographically diverse testbed network.« less
    Cited by 1
  • Well-controlled experiments that directly compare seasonal algal productivities across geographically distinct locations have not been reported before. To fill this gap, six cultivation testbed facilities were chosen across the United States to evaluate different climatic zones with respect to algal biomass productivity potential. The geographical locations and climates were as follows: Southwest, desert; Western, coastal; Southeast, inland; Southeast, coastal; Pacific, tropical; and Midwest, greenhouse. The testbed facilities were equipped with identical systems for inoculum production and open pond operation and methods were standardized across all testbeds to ensure accurate measurement of physical and biological variables. The ability of the testbedmore » sites to culture and analyze the same algal species, Nannochloropsis oceanica KA32, using identical pond operational and data collection procedures was evaluated during the same seasonal timeframe. This manuscript describes the results of a first-of-its-kind coordinated testbed validation field study while providing critical details on how geographical variations in temperature, light, and weather variables influenced algal productivity, nitrate consumption, and biomass composition. We found distinct differences in growth characteristics due to the geographic location and the resulting climatic and seasonal conditions across the sites, with the highest productivities observed at the desert Southwest and tropical Pacific regions, followed by the Western coastal region. The lowest productivities were observed at the Southeast inland and Midwest greenhouse locations. These differences in productivities among the sites correlated with the differences in pond water temperature and available solar radiation. In addition two sites, the tropical Pacific and Southeast inland experienced unusual events, spontaneous flocculation, and unusually cold and wet (rainfall) conditions respectively, that negatively affected outdoor algal growth. In addition, minor variability in productivity was observed between the different experimental treatments at each site, much smaller compared to differences due to geographic location. Finally, the successful demonstration of the coordinated and standardized operation of the testbed sites established a rigorous basis for future validation of algal strains and operational conditions and protocols across a geographically diverse testbed network.« less
  • The Rice Parallel Processing Testbed (RPPT) is a collection of software tools for simulating the interaction of parallel programs and parallel architectures. The testbed uses a novel technique called execution driven simulation, whereby the pseudo-concurrent execution of a parallel algorithm, augmented by profiling code, is used to drive the discrete event simulation of a parallel architecture. This technique is intermediate between the high accuracy and low computational efficiency of instruction-level simulations and the less accurate but high efficiency statistical distribution-driven simulations, effectively combining attractive features of both of these techniques. The technique provides estimates of overall execution time, as wellmore » as more detailed performance indices such as communication vs. computation time, message passing traffic, and processor utilization. The methodology and implementation of the testbed are discussed at length and are compared with recently published related projects. The implementation has been a collective effort involving several people, and the author's contribution to the effort is outlined. Testbed predictions are given for a set of parallel numerical algorithms-LU decomposition, eigenvalue-eigenvector determination, FFT-simulated for a hypercube, and the predictions are validated against measurement of actual program execution on an Intel iPSC 16-node hypercube.« less
  • Purpose: To develop an advanced testbed that combines a 3D motion stage and ultrasound phantom to optimize and validate 2D and 3D tracking algorithms for real-time motion management during radiation therapy. Methods: A Siemens S2000 Ultrasound scanner utilizing a 9L4 transducer was coupled with the Washington University 4D Phantom to simulate patient motion. The transducer was securely fastened to the 3D stage and positioned to image three cylinders of varying contrast in a Gammex 404GS LE phantom. The transducer was placed within a water bath above the phantom in order to maintain sufficient coupling for the entire range of simulatedmore » motion. A programmed motion sequence was used to move the transducer during image acquisition and a cine video was acquired for one minute to allow for long sequence tracking. Images were analyzed using a normalized cross-correlation block matching tracking algorithm and compared to the known motion of the transducer relative to the phantom. Results: The setup produced stable ultrasound motion traces consistent with those programmed into the 3D motion stage. The acquired ultrasound images showed minimal artifacts and an image quality that was more than suitable for tracking algorithm verification. Comparisons of a block matching tracking algorithm with the known motion trace for the three features resulted in an average tracking error of 0.59 mm. Conclusion: The high accuracy and programmability of the 4D phantom allows for the acquisition of ultrasound motion sequences that are highly customizable; allowing for focused analysis of some common pitfalls of tracking algorithms such as partial feature occlusion or feature disappearance, among others. The design can easily be modified to adapt to any probe such that the process can be extended to 3D acquisition. Further development of an anatomy specific phantom better resembling true anatomical landmarks could lead to an even more robust validation. This work is partially funded by NIH grant R01CA190298.« less