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   Lantz, B ; Yu, J ; Bhardwaj, A ; Diaz-Montiel, A ; Quraishy, A ; Santaniello, S ; Chen, T ; Fujieda, R ; Mukhopadhyay, A ; Zussman, G ; et al ( June 2021 , OFC radio)

   We demonstrate SDN-controlled dynamic front-haul optical network pro visioning and modulation format adaptation, running on an emulation of the COSMOS testbed benchmarked against the COSMOS hardware testbed. 

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2. Co-optimization of charging scheduling and platooning for long-haul electric freight vehicles

   https://doi.org/10.1016/j.trc.2022.104009  

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3. Programmable Optical x-Haul Network in the COSMOS Testbed

   https://doi.org/10.1109/ICNP.2019.8888108  

   Gutterman, Craig ; Zussman, Gil ; Minakhmetov, Artur ; Yu, Jiakai ; Sherman, Michael ; Chen, Tingjun ; Zhu, Shengxiang ; Seskar, Ivan ; Raychaudhuri, Dipankar ; Kilper, Daniel ( October 2019 , 2019 IEEE 27th International Conference on Network Protocols (ICNP))

   The Cloud-Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multi-user advanced wireless testbed that is being deployed in West Harlem of New York City [1]. To keep pace with the significantly increased wireless link bandwidth and to effectively integrate the emerging C-RANs, COSMOS is designed to incorporate a fast programmable core network for providing connections across different computing layers. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies and experimentation directly in the optical physical layer. The optical architecture of COSMOS was presented in [2]. In this abstract, we present the tools and services designed to configure and monitor the performance of optical paths and topologies of the COSMOS testbed. In particular, we present the SDN framework that allows testbed users to implement experiments with application-driven control of optical and data networking functionalities. 

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4. Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

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   Kadota, Igor ; Jacoby, Dror ; Messer, Hagit ; Zussman, Gil ; Ostrometzky, Jonatan ( December 2022 , Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances. 

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   Eby, Ron ; Scoles, Robert ; Hughes, Brent Bancroft ; Wasson, Kerstin ( November 2017 , Ecology)