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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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EXPLORATION OF MULTIFIDELITY UQ SAMPLING STRATEGIES FOR COMPUTER NETWORK APPLICATIONS

Volume 11, Edição 1, 2021, pp. 93-118
DOI: 10.1615/Int.J.UncertaintyQuantification.2021033774
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RESUMO

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

Referências
  1. Smith, R., Uncertainty Quantification: Theory, Implementation, and Applications, Computational Science and Engineering, Philadelphia: SIAM, 2013.

  2. Morgan, M.G. and Henrion, M., Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, Cambridge: Cambridge University Press, 1990.

  3. Dienstfrey, A. and Boisvert, R.E., Uncertainty Quantification in Scientific Computing, in 10th IFIP WG 2.5 Working Conference, WoCoUQ2011, Berlin: Springer, 2012.

  4. Helton, J., Hansen, C., and Swift, P.E., Performance Assessment for the Proposed High-Level Radioactive Waste Repository at Yucca Mountain, Nevada, Reliab. Eng. System Saf., 122:1-6, 2014.

  5. Stocker, T., Qin, D., Plattner, G.K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., and Bex, V., Eds., Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Tech. Rep., Cambridge: Cambridge University Press, 2013.

  6. Saltelli, A., Chan, K., and Scott, E., Sensitivity Analysis, New York: John Wiley & Sons, 2000.

  7. Oberkampf, W.L. and Roy, C.J., Verification and Validation in Scientific Computing, Cambridge: Cambridge University Press, 2010.

  8. Constantine, P.G., Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies, Vol. 2, Philadelphia: SIAM, 2015.

  9. Giles, M.B., Multilevel MonteCarlo Path Simulation, Oper. Res, 56(3):607-617, 2008.

  10. Giles, M.B., Multilevel MonteCarlo Methods, Acta Numer., 24:259-328, 2015.

  11. Haji-Ali, A.L., Nobile, F., and Tempone, R., Multi-Index Monte Carlo: When Sparsity Meets Sampling, Numer. Math, 132(4):767-806, 2016.

  12. Jakeman, J.D., Eldred, M.S., Geraci, G., and Gorodetsky, A., Adaptive Multi-Index Collocation for Uncertainty Quantification and Sensitivity Analysis, Int. J. Numer. Methods Eng., 121(6):1314-1343,2020.

  13. Pasupathy, R., Schmeiser, B.W., Taaffe, M.R., and Wang, J., Control-Variate Estimation Using Estimated Control Means, IIE Trans, 44(5):381-385,2012.

  14. Peherstorfer, B., Willcox, K., and Gunzburger, M., Optimal Model Management for Multifidelity Monte Carlo Estimation, SIAM J. Sci. Comput., 38(5):A3163-A3194, 2016.

  15. Geraci, G., Iaccarino, G., and Eldred, M.S., A Multi Fidelity Control Variate Approach for the Multilevel Monte Carlo Technique, CTR Center Ann. Res. Briefs, pp. 169-181, 2015.

  16. Fairbanks, H., Doostan, A., Ketelsen, C., and Iaccarino, G., A Low-Rank Control Variate for Multilevel Monte Carlo Simulation of High-Dimensional Uncertain Systems, J. Comput. Phys, 341:121-139,2017.

  17. Geraci, G., Eldred, M.S., and Iaccarino, G., A Multifidelity Multilevel Monte Carlo Method for Uncertainty Propagation in Aerospace Applications, in Proc. of 19th AIAA Non-Deterministic Approaches Conf., p. 1951, 2017.

  18. Gorodetsky, A.A., Geraci, G., Eldred, M.S., and Jakeman, J.D., A Generalized Approximate Control Variate Framework for Multifidelity Uncertainty Quantification, J. Comput. Phys, 408:109257, 2020.

  19. Gorodetsky, A., Jakeman, J., Geraci, G., and Eldred, M., MFNets: Multi-Fidelity Data-Driven Networks for Bayesian Learning and Prediction, Int. J. Uncertainty Quantif., 10(6):595-622, 2020.

  20. Jofre, L., Geraci, G., Fairbanks, H., Doostan, A., and Iaccarino, G., Multi-Fidelity Uncertainty Quantification of Irradiated Particle-Laden Turbulence, in Center for Turbulence Research Annual Research Briefs, Center for Turbulence Research, Stanford University, Stanford, CA, pp. 21-34, 2017.

  21. Fairbanks, H.R., Jofre, L., Geraci, G., Iaccarino, G., and Doostan, A., Bi-Fidelity Approximation for Uncertainty Quantification and Sensitivity Analysis of Irradiated Particle-Laden Turbulence, J. Comput. Phys, 402:108996, 2020.

  22. Hsieh, A., Maniaci, D.C., Herges, T.G., Geraci, G., Seidl, D.T., Eldred, M.S., Blaylock, M.L., and Houchens, B.C., Multilevel Uncertainty Quantification Using CFD and OpenFAST Simulations of the SWiFT Facility, in AIAA SciTech 2020 Forum, AIAA Paper No. 2020-1949, 2020.

  23. Seidl, D.T., Geraci, G., King, R., Menhorn, F., Glaws, A., and Eldred, M.S., Multifidelity Strategies for Forward and Inverse Uncertainty Quantification of Wind Energy Applications, in AIAA SciTech 2020 Forum, AIAA Paper No. 2020-1950, 2020.

  24. Fleeter, C.M., Geraci, G., Schiavazzi, D.E., Kahn, A.M., Eldred, M.S., and Marsden, A.L., Multilevel Multifidelity Approaches for Cardiovascular Flow under Uncertainty, in Sandia Center for Computing Research Summer Proc., A.D. Baczewski and M.L. Parks, Eds., Tech. Rep. No. SAND2018-2780O, Sandia National Laboratories, Albuquerque, NM, pp. 27-50,2018.

  25. Fleeter, C.M., Geraci, G., Schiavazzi, D.E., Kahn, A.M., and Marsden, A.L., Multilevel and Multifidelity Uncertainty Quantification for Cardiovascular Hemodynamics, Comput. Methods Appl. Mech. Eng., 365:113030, 2020.

  26. Peherstorfer, B., Willocox, K., and Gunzburger, M., Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization, SIAM Rev, 60(3):550-591,2018.

  27. Mets, K., Ojea, J.A., and Develder, C., Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis, IEEE Commun. Surv. Tutorials, 16(3):1771-1796, 2014.

  28. Nunes, B.A.A., Mendonca, M., Nguyen, X., Obraczka, K., and Turletti, T., A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks, IEEE Commun. Surv. Tutorials, 16(3):1617-1634, 2014.

  29. Martinez, F.J., Toh, C.K., Cano, J.C., Calafate, C.T., and Manzoni, P., A Survey and Comparative Study of Simulators for Vehicular ad hoc Networks (VANETs), Wireless Commun. Mobile Comput., 11(7):813-828, 2011.

  30. Crussell, J., Kroeger, T.M., Brown, A., and Phillips, C., Virtually the Same: Comparing Physical and Virtual Testbeds, in 2019 Int. Conf. on Computing, Networking and Communications (ICNC), New York: IEEE, 2019.

  31. Henderson, T.R., Lacage, M., Riley, G.F., Dowell, C., and Kopena, J., Network Simulations with the ns-3 Simulator, SIG-COMMDemonstr, 14(14):527, 2008.

  32. Farooq, J. and Turletti, T., An IEEE 802.16 WiMAX Module for the NS-3 Simulator, in Proceedings of the 2nd International Conference on Simulation Tools and Techniques, Brussels, Belgium: Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, p. 8, 2009.

  33. Nguyen, B., Banerjee, A., Gopalakrishnan, V., Kasera, S., Lee, S., Shaikh, A., and Van der Merwe, J., Towards Understanding TCP Performance on LTE/EPC Mobile Networks, Proc. of the 4th Workshop on All Things Cellular: Operations, Applications, and Challenges, ACM, pp. 41-46, 2014.

  34. Rabieh, K., Mahmoud, M.M., Akkaya, K., and Tonyali, S., Scalable Certificate Revocation Schemes for Smart Grid Ami Networks Using Bloom Filters, IEEE Trans. Dependable Secure Comput., 14(4):420-432, 2017.

  35. Van den Abeele, F., Haxhibeqiri, J., Moerman, I., and Hoebeke, J., Scalability Analysis of Large-Scale LoRaWAN Networks in ns-3, IEEE Internet Things J, 4(6):2186-2198, 2017.

  36. Minimega Developers, Minimega: A Distributed VM Management Tool, 2019.

  37. Bellard, F., QEMU, a Fast and Portable Dynamic Translator, in USENIX Annual Technical Conference, FREENIX Track, Vol. 41, p. 46,2005.

  38. Kivity, A., Kamay, Y., Laor, D., Lublin, U., and Liguori, A., KVM: The Linux Virtual Machine Monitor, in Proc. of the Linux Symp., Vol. 1, Ottawa, Canada, pp. 225-230,2007.

  39. Linux Foundation, Open v-Switch, 2019.

  40. Le Maitre, O. and Knio, O.M., Spectral Methods for Uncertainty Quantification with Applications to Computational Fluid Dynamics, Dordrecht, the Netherlands: Springer, 2010.

  41. Swiler, L., Hough, P., Qian, P., Xu, X., Storlie, C., and Lee, H., Surrogate Models for Mixed Discrete-Continuous Variables, in Constraint Programming and Decision Making. Studies in Computational Intelligence, M. Ceberio and V. Kreinovich, Eds., Springer, Vol. 539,2014.

  42. Rosenblatt, M., Remarks on a Multivariate Transformation, Ann. Math. Stat., 23(3):470-472, 1952.

  43. Iman, R.L. and Conover, W. J., A Distribution-Free Approach to Inducing Rank Correlation Among Input Variables, Commun. Stat. Simul. Comput, B11(3):311-334, 1982.

  44. Lavenberg, S., Moeller, T., and Welch, P., Statistical Results on Multiple Control Variables with Application to Variance Reduction in Queueing Network Simulation, IBM Thomas J. Watson Research Division, 1978.

  45. Lavenberg, S.S. and Welch, P.D., A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations, Manag. Sci, 27(3):322-335,1981.

  46. Lavenberg, S.S., Moeller, T.L., and Welch, P.D., Statistical Results on Control Variables with Application to Queueing Network Simulation, Oper. Res, 30(1):182-202, 1982.

  47. Ng, L. and Willcox, K., Multifidelity Approaches for Optimization under Uncertainty, Int. J. Numer. Methods Eng., 100(10):746-772, 2014.

  48. Castillo, A., Arguello, B., Cruz, G., and Swiler, L., Cyber-Physical Emulation and Optimization of Worst-Case Cyber Attacks on the Power Grid, 2019 Resilience Week (RWS), Vol. 1,pp. 14-18,2019.

  49. Malkin, G.S., RIP Version 2, RFC 2453, 1998.

  50. Moy, J., OSPF Version 2, RFC 2328, 1998.

  51. Adams, B.M., Bohnhoff, W.J., Dalbey, K.R., Eddy, J.P., Ebeida, M.S., Eldred, M.S., Frye, J.R., Geraci, G., Hooper, R.W., Hough, P.D., Hu, K.T., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi, A., Stephens, J.A., Swiler, L.P., Winokur, J.G., Vigil, D.M., and Wildey, T.M., Dakota, a Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.7 Theory Manual, Tech. Rep. SAND2014-4253, Sandia National Laboratories, Albuquerque, NM, 2018.

  52. Adams, B.M., Bohnhoff, W.J., Dalbey, K.R., Eddy, J.P., Ebeida, M.S., Eldred, M.S., Frye, J.R., Geraci, G., Hooper, R.W., Hough, P.D., Hu, K.T., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi, A., Stephens, J.A., Swiler, L.P., Winokur, J.G., Vigil, D.M., and Wildey, T.M., Dakota, a Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.7 Users Manual, Tech. Rep. SAND2014-4633, Sandia National Laboratories, Albuquerque, NM, 2018.

  53. Blonigan, P.J., Geraci, G., Rizzi, F., Eldred, M.S., and Carlberg, K., Online Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification, Tech. Rep. SAND2019-11427R, Sandia National Laboratories, Albuquerque, NM, 2019.

  54. Blonigan, P. J., Geraci, G., Rizzi, F., and Eldred, M.S., Towards an Integrated and Efficient Framework for Leveraging Reduced Order Models for Multifidelity Uncertainty Quantification, in AIAA SciTech 2020 Forum, AIAA Paper No. 2020-0420, 2020.

  55. Geraci, G., Eldred, M.S., Gorodetsky, A.A., and Jakeman, J.D., Leveraging Active Directions for Efficient Multifidelity UQ, in Proc. of the 7th European Conference on Computational Fluid Dynamics (ECFD 7), pp. 2735-2746, 2018.

  56. Geraci, G. and Eldred, M.S., Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification, Tech. Rep. SAND2018-10817, Sandia National Laboratories, Albuquerque, NM, 2018.

CITADO POR
  1. Bomarito Geoffrey, Geraci Gianluca, Warner James, Leser Patrick, Leser William, Eldred Michael S., Jakeman John, Gorodetsky Alex, Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning, AIAA SCITECH 2022 Forum, 2022. Crossref

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