Abstract
While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on the critical task of configuring the simulator to reflect reality. We present iBox ("Internet in a Box"), which enables data-driven network path simulation, using input/output packet traces gathered at the sender/receiver in the target network to create a model of the end-to-end behaviour of a network path. Our work builds on recent work in this direction and makes three contributions: (1) estimation of a lightweight non reactive cross-traffic model, (2) estimation of a more powerful reactive cross-traffic model based on Bayesian optimization, and (3) evaluation of iBox in the context of congestion control variants in an Internet research testbed and also controlled experiments with known ground truth.
- LEDBAT. https://en.wikipedia.org/wiki/LEDBAT.Google Scholar
- pname project website. https://aka.ms/ibox.Google Scholar
- TCP BBR congestion control comes to GCP -- your Internet just got faster. https://cloud.google.com/blog/products/networking/tcp-bbr-congestion-control-comes-to-gcp-your-internet-just-got-faster.Google Scholar
- TCP CUBIC. https://en.wikipedia.org/wiki/CUBIC_TCP.Google Scholar
- uTorrent Transport Protocol. https://www.bittorrent.org/beps/bep_0029.html.Google Scholar
- I. Alon, M. Qi, and R. J. Sadowski. Forecasting aggregate retail sales:: a comparison of artificial neural networks and traditional methods. Journal of retailing and consumer services, 8(3):147--156, 2001.Google Scholar
- S. Ashok, S. S. Duvvuri, N. Natarajan, V. N. Padmanabhan, S. Sellamanickam, and J. Gehrke. iBox: Internet in a Box. In Proceedings of the 19th ACM Workshop on Hot Topics in Networks, HotNets '20, page 23--29, New York, NY, USA, 2020. Association for Computing Machinery.Google Scholar
Digital Library
- W. Bao, J. Yue, and Y. Rao. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7):e0180944, 2017.Google Scholar
Cross Ref
- J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl. Algorithms for Hyper-Parameter Optimization. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 24. Curran Associates, Inc., 2011.Google Scholar
- J. Bergstra, D. Yamins, and D. D. Cox. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. ICML'13, page I--115--I--123. JMLR.org, 2013.Google Scholar
- L. S. Brakmo, S. W. O'Malley, and L. L. Peterson. TCP Vegas: New Techniques for Congestion Detection and Avoidance. In Proceedings of the Conference on Communications Architectures, Protocols and Applications, SIGCOMM '94, page 24--35, New York, NY, USA, 1994. Association for Computing Machinery.Google Scholar
Digital Library
- Y. Cao, A. Jain, K. Sharma, A. Balasubramanian, and A. Gandhi. When to use and when not to use bbr: An empirical analysis and evaluation study. In Proceedings of the Internet Measurement Conference, IMC '19, page 130--136, New York, NY, USA, 2019. Association for Computing Machinery.Google Scholar
Digital Library
- M. P. Clements, P. H. Franses, and N. R. Swanson. Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting, 20(2):169--183, 2004.Google Scholar
Cross Ref
- C. Dovrolis, P. Ramanathan, and D. Moore. Packet-dispersion techniques and a capacity-estimation methodology. IEEE/ACM Transactions On Networking, 12(6):963--977, 2004.Google Scholar
Digital Library
- I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative Adversarial Nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS'14, page 2672--2680, Cambridge, MA, USA, 2014. MIT Press.Google Scholar
Digital Library
- S. Grossberg. Nonlinear neural networks: Principles, mechanisms, and architectures. Neural networks, 1(1):17--61, 1988.Google Scholar
Cross Ref
- A. Grover, A. Kapoor, and E. Horvitz. A Deep Hybrid Model for Weather Forecasting. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, page 379--386, New York, NY, USA, 2015. Association for Computing Machinery.Google Scholar
Digital Library
- S. Ha, I. Rhee, and L. Xu. CUBIC: A New TCP-Friendly High-Speed TCP Variant. SIGOPS Oper. Syst. Rev., 42(5):64--74, jul 2008.Google Scholar
Digital Library
- Hyperopt. http://hyperopt.github.io/hyperopt.Google Scholar
- About the calibrated emulators. https://groups.google.com/g/pantheon-stanford/c/sbiP6OAN1NY/m/MmPL9l6mAQAJ.Google Scholar
- R. J. Kuligowski and A. P. Barros. Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks. Weather and forecasting, 13(4):1194--1204, 1998.Google Scholar
Cross Ref
- A. Lazaris and V. K. Prasanna. Deep Learning Models For Aggregated Network Traffic Prediction. In 15th International Conference on Network and Service Management (CNSM), 2019.Google Scholar
- Z. Lin, A. Jain, C. Wang, G. Fanti, and V. Sekar. Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions. In Proceedings of the ACM Internet Measurement Conference, IMC '20, page 464--483, New York, NY, USA, 2020. Association for Computing Machinery.Google Scholar
Digital Library
- Mahimahi. http://mahimahi.mit.edu/.Google Scholar
- Network Emulator. https://man7.org/linux/man-pages/man8/tc-netem.8.html.Google Scholar
- R. Netravali, A. Sivaraman, S. Das, A. Goyal, K. Winstein, J. Mickens, and H. Balakrishnan. Mahimahi: Accurate Record-and-Replay for HTTP . In 2015 USENIX Annual Technical Conference (USENIX ATC 15), pages 417--429, Santa Clara, CA, 2015.Google Scholar
- B. D. Noble, M. Satyanarayanan, G. T. Nguyen, and R. H. Katz. Trace-Based Mobile Network Emulation. In Proceedings of the ACM SIGCOMM '97 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM '97, page 51--61, New York, NY, USA, 1997. Association for Computing Machinery.Google Scholar
Digital Library
- ns-2 Network Simulator. https://www.isi.edu/nsnam/ns.Google Scholar
- ns-3 Network Simulator. https://www.nsnam.org/.Google Scholar
- OPNET Technologies. https://www.riverbed.com/in/products/steelcentral/opnet.html.Google Scholar
- Pantheon: The Training Ground for Internet Congestion Control Research. https://pantheon.stanford.edu/.Google Scholar
- R. Prado and M. West. Time series: modeling, computation, and inference. CRC Press, 2010.Google Scholar
- QualNet: Network Simulation. https://www.scalable-networks.com/qualnet-network-simulation.Google Scholar
- Y. Rubner, C. Tomasi, and L. J. Guibas. The Earth Mover's Distance as a Metric for Image Retrieval. International Journal of Computer Vision, 40(2):99--121, Nov 2000.Google Scholar
Digital Library
- S. Shalunov, G. Hazel, J. Iyengar, and M. Kuehlewind. Low Extra Delay Background Transport (LEDBAT). RFC 6817, IETF, Dec. 2012.Google Scholar
- J. Sommers and P. Barford. Self-Configuring Network Traffic Generation. In Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, IMC '04, page 68--81, New York, NY, USA, 2004. Association for Computing Machinery.Google Scholar
Digital Library
- J. Sommers, R. Bowden, B. Eriksson, P. Barford, M. Roughan, and N. Duffield. Efficient network-wide flow record generation. In 2011 Proceedings IEEE INFOCOM, pages 2363--2371, 2011.Google Scholar
Cross Ref
- C. Voyant, M. Muselli, C. Paoli, and M.-L. Nivet. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy, 39(1):341--355, 2012.Google Scholar
Cross Ref
- K. Winstein, A. Sivaraman, and H. Balakrishnan. Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), pages 459--471, Lombard, IL, Apr. 2013. USENIX Association.Google Scholar
- F. Y. Yan, J. Ma, G. D. Hill, D. Raghavan, R. S. Wahby, P. Levis, and K. Winstein. Pantheon: the training ground for internet congestion-control research. In 2018 USENIX Annual Technical Conference (USENIX ATC 18), pages 731--743, 2018.Google Scholar
Index Terms
Data-Driven Network Path Simulation with iBox
Recommendations
iBox: Internet in a Box
HotNets '20: Proceedings of the 19th ACM Workshop on Hot Topics in NetworksWe present a vision of data-informed network simulation to address significant shortcomings in the state of the art. We substantiate our position with proof points based on iBox, which leverages networking domain knowledge and machine learning (ML) ...
Data-Driven Network Path Simulation with iBox
SIGMETRICS/PERFORMANCE '22: Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer SystemsWhile network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on ...
Data-Driven Network Path Simulation with iBox
SIGMETRICS '22While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on ...






Comments