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1. Synergy: A SmartNIC Accelerated 5G Dataplane and Monitor for Mobility Prediction

   https://doi.org/10.1109/ICNP55882.2022.9940261  

   Panda, Sourav ; Ramakrishnan, K. K. ; Bhuyan, Laxmi N. ( October 2022 , 2022 IEEE 30th International Conference on Network Protocols (ICNP))

   The 5G user plane function (UPF) is a critical inter-connection point between the data network and cellular network infrastructure. It governs the packet processing performance of the 5G core network. UPFs also need to be flexible to support several key control plane operations. Existing UPFs typically run on general-purpose CPUs, but have limited performance because of the overheads of host-based forwarding. We design Synergy, a novel 5G UPF running on SmartNICs that provides high throughput and low latency. It also supports monitoring functionality to gather critical data on user sessions for the prediction and optimization of handovers during user mobility. The SmartNIC UPF efficiently buffers data packets during handover and paging events by using a two-level flow-state access mechanism. This enables maintaining flow-state for a very large number of flows, thus providing very low latency for control and data planes and high throughput packet forwarding. Mobility prediction can reduce the handover delay by pre-populating state in the UPF and other core NFs. Synergy performs handover predictions based on an existing recurrent neural network model. Synergy's mobility predictor helps us achieve 2.32× lower average handover latency. Buffering in the SmartNIC, rather than the host, during paging and handover events reduces packet loss rate by at least 2.04×. Compared to previous approaches to building programmable switch-based UPFs, Synergy speeds up control plane operations such as handovers because of the low P4-programming latency leveraging tight coupling between SmartNIC and host. 

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2. Evolving to 6G: Improving the Cellular Core to lower control and data plane latency

   https://doi.org/10.1109/6GNet54646.2022.9830519  

   Jain, Vivek ; Panda, Sourav ; Qi, Shixiong ; Ramakrishnan, K. K. ( July 2022 , 1st International Conference on 6G Networking (6GNet), 2022)

   With the commercialization and deployment of 5G, efforts are beginning to explore the design of the next generation of cellular networks, called 6G. New and constantly evolving use cases continue to place performance demands, especially for low latency communications, as these are still challenges for the 3GPP-specified 5G design, and will have to be met by the 6G design. Therefore, it is helpful to re-examine several aspects of the current cellular network’s design and implementation.Based on our understanding of the 5G cellular network specifications, we explore different implementation options for a dis-aggregated 5G core and their performance implications. To improve the data plane performance, we consider advanced packet classification mechanisms to support fast packet processing in the User Plane Function (UPF), to improve the poor performance and scalability of the current design based on linked lists. Importantly, we implement the UPF function on a SmartNIC for forwarding and tunneling. The SmartNIC provides the fastpath for device traffic, while more complex functions of buffering and processing flows that suffer a miss on the SmartNIC P4 tables are processed by the host-based UPF. Compared to an efficient DPDK-based host UPF, the SmartNIC UPF increases the throughput for 64 Byte packets by almost 2×. Furthermore, we lower the packet forwarding latency by 3.75× by using the SmartNIC. In addition, we propose a novel context-level QoS mechanism that dynamically updates the Packet Detection Rule priority and resource allocation of a flow based on the user context. By combining our innovations, we can achieve low latency and high throughput that will help us evolve to the next generation 6G cellular networks. 

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3. Flow-level Dynamic Bandwidth Allocation in SDN-enabled Edge Cloud using Heuristic Reinforcement Learning

   Arslan Qadeer, Myung Lee ( January 2021 , IEEE Conference, on Future Internet of Things and Cloud)

   Edge Cloud (EC) is poised to brace massive machine type communication (mMTC) for 5G and IoT by providing compute and network resources at the edge. Yet, the EC being regionally domestic with a smaller scale, faces the challenges of bandwidth and computational throughput. Resource management techniques are considered necessary to achieve efficient resource allocation objectives. Software Defined Network (SDN) enabled EC architecture is emerging as a potential solution that enables dynamic bandwidth allocation and task scheduling for latency sensitive and diverse mobile applications in the EC environment. This study proposes a novel Heuristic Reinforcement Learning (HRL) based flowlevel dynamic bandwidth allocation framework and validates it through end-to-end implementation using OpenFlow meter feature. OpenFlow meter provides granular control and allows demand-based flow management to meet the diverse QoS requirements germane to IoT traffics. The proposed framework is then evaluated by emulating an EC scenario based on real NSF COSMOS testbed topology at The City College of New York. A specific heuristic reinforcement learning with linear-annealing technique and a pruning principle are proposed and compared with the baseline approach. Our proposed strategy performs consistently in both Mininet and hardware OpenFlow switches based environments. The performance evaluation considers key metrics associated with real-time applications: throughput, end-to-end delay, packet loss rate, and overall system cost for bandwidth allocation. Furthermore, our proposed linear annealing method achieves faster convergence rate and better reward in terms of system cost, and the proposed pruning principle remarkably reduces control traffic in the network. 

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4. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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5. P4NFV: P4 Enabled NFV Systems with SmartNICs

   https://doi.org/10.1109/NFV-SDN47374.2019.9040000  

   Mohammadkhan, Ali ; Panda, Sourav ; Kulkarni, Sameer G. ; Ramakrishnan, K. K. ; Bhuyan, Laxmi N. ( November 2019 , 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))

   Software Defined Networking (SDN) and Network Function Virtualization (NFV) are transforming Data Center (DC), Telecom, and enterprise networking. The programmability offered by P4 enables SDN to be more protocol-independent and flexible. Data Centers are increasingly adopting SmartNICs (sNICs) to accelerate packet processing that can be leveraged to support packet processing pipelines and custom Network Functions (NFs). However, there are several challenges in integrating and deploying P4 based SDN control as well as host and sNIC-based programmable NFs. These include configuration and management of the data plane components (Host and sNIC P4 switches) for the SDN control plane and effective utilization of data plane resources. P4NFV addresses these concerns and provides a unified P4 switch abstraction framework to simplify the SDN control plane, reducing management complexities, and leveraging a host-local SDN Agent to improve the overall resource utilization. The SDN agent considers the network-wide, host, and sNIC specific capabilities and constraints. Based on workload and traffic characteristics, P4NFV determines the partitioning of the P4 tables and optimal placement of NFs (P4 actions) to minimize the overall delay and maximize resource utilization. P4NFV uses Mixed Integer Linear Programming (MILP) based optimization formulation and achieves up to 2. 5X increase in system capacity while minimizing the delay experienced by flows. P4NFV considers the number of packet exchanges, flow size, and state dependency to minimize the delay imposed by data transmission over PCI Express interface. 

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