IEEE INFOCOM 2023
Ant Colony based Online Learning Algorithm for Service Function Chain Deployment
Yingling Mao, Xiaojun Shang and Yuanyuan Yang (Stony Brook University, USA)
Speaker Yingling Mao (Stony Brook University)
Yingling Mao received her B.S. degree in Mathematics and Applied Mathematics in Zhiyuan College from Shanghai Jiao Tong University, Shanghai, China, in 2018. She is currently working toward the Ph.D degree in the Department of Electrical and Computer Engineering, Stony Brook University. Her research interests include network function virtualization, edge computing, cloud computing and quantum networks.
AutoManager: a Meta-Learning Model for Network Management from Intertwined Forecasts
Alan Collet and Antonio Bazco Nogueras (IMDEA Networks Institute, Spain); Albert Banchs (Universidad Carlos III de Madrid, Spain); Marco Fiore (IMDEA Networks Institute, Spain)
Speaker Alan Collet
Alan Collet is a Ph.D. Student at IMDEA Networks Institute. He obtained two Master's degrees, one from the Illinois Institute of Technology, Chicago, USA, and one from the ENSEIRB-MATMECA, Bordeaux, France. His primary research interest and thesis subject is self-learning network intelligence.
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
Tung Anh Nguyen, Jiayu He, Long Tan Le, Wei Bao and Nguyen H. Tran (The University of Sydney, Australia)
Speaker Nguyen H. Tran (The University of Sydney)
Nguyen H. Tran received BS and PhD degrees (with best PhD thesis award in 2011), from HCMC University of Technology and Kyung Hee University, in electrical and computer engineering, in 2005 and 2011, respectively. Dr Tran is an Associate Professor at the School of Computer Science, The University of Sydney. He was an Assistant Professor with Department of Computer Science and Engineering, Kyung Hee University, from 2012 to 2017. His research group has special interests in Distributed compUting, optimizAtion, and machine Learning (DUAL group). He received several best paper awards, including IEEE ICC 2016 and ACM MSWiM 2019. He receives the Korea NRF Funding for Basic Science and Research 2016-2023, ARC Discovery Project 2020-2023, and SOAR award 2022-2023. He serves as an Editor for several journals such as IEEE Transactions on Green Communications and Networking (2016-2020), IEEE Journal of Selected Areas in Communications 2020 in the area of distributed machine learning/Federated Learning, and IEEE Transactions on Machine Learning in Communications Networking (2022).
QueuePilot: Reviving Small Buffers With a Learned AQM Policy
Micha Dery, Orr Krupnik and Isaac Keslassy (Technion, Israel)
We introduce QueuePilot, an RL (reinforcement learning)-based AQM that enables small buffers in backbone routers, trading off high utilization with low loss rate and short delay. QueuePilot automatically tunes the ECN (early congestion notification) marking probability. After training once offline with a variety of settings, QueuePilot produces a single lightweight policy that can be applied online without further learning. We evaluate QueuePilot on real networks with hundreds of TCP connections, and show how it provides a performance in small buffers that exceeds that of existing algorithms, and even exceeds their performance with larger buffers.
Speaker Micha Dery (Technion)
Micha Dery received his B.Sc. and M.Sc. from the Department of Electrical and Computer Engineering at the Technion - Israel Institute of Technology. He is interested in ML applications in networking, mobile ad-hoc networks, and distributed systems.
Baochun Li (University of Toronto)
Video and Web Applications
Owl: A Pre-and Post-processing Framework for Video Analytics in Low-light Surroundings
Rui-Xiao Zhang, Chaoyang Li, Chenglei Wu, Tianchi Huang and Lifeng Sun (Tsinghua University, China)
In this paper, we propose Owl, an intelligent framework to optimize the bandwidth utilization and inference accuracy for the low-light video analytic pipeline. The core idea of Owl is two-fold. On the one hand, we will deploy a light-weighted pre-processing module before transmission, through which we will get the denoised video and significantly reduce the transmitted data; on the other hand, we recover the information from the denoised video via a DNN-based enhancement module in the server-side. Specifically, through content-aware feature clustering and task-oriented fine-tuning, Owl can well coordinate the front-end and back-end, and intelligently determine the best denoise level and corresponding enhancement model for different videos. Experiments with a variety of datasets and tasks show that Owl achieves significant bandwidth benefits, while consistently optimizing the inference accuracy.
Speaker Rui-Xiao Zhang (Tsinghua University)
Rui-Xiao Zhang received his B.E and Ph.D degrees from Tsinghua University in 2013 and 2017, repectively. Currently, he is a Post-doctoral fellow in the University of Hong Kong. His research interests lie in the area of content delivery networks, the optimization of multimedia streaming, and the machine learning for systems. He has published more than 20 papers in top conference including ACM Multimedia, IEEE INFOCOM. He also serves as the reviewer for JSAC, TCSVT, TMM, TMC. He has received the Best Student Paper Awards presented by ACM Multimedia System Workshop in 2019.
AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics
Tingting Yuan (Georg-August-University of Göttingen, Germany); Liang Mi (Nanjing University, China); Weijun Wang (Nanjing University & University of Goettingen, China); Haipeng Dai (Nanjing University & State Key Laboratory for Novel Software Technology, China); Xiaoming Fu (University of Goettingen, Germany)
Speaker Tingting Yuan (University of Göttingen)
Dr. Tingting Yuan ([email protected]) is a junior professor with the Institute of Computer Science at University of Göttingen, Germany. She received her Ph.D. degree from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2018. During the year 2018-2020, she was a postdoctor at INRIA, Sophia Antipolis, France. Since 2020, she joined the University of Göttingen as a senior postdoctor with a Humboldt scholarship. Her current interests are in next-generation networks, including software-defined networking, reinforcement learning, vehicular ad-hoc networks, and so on. She has published more than 20 peer-reviewed papers including IEEE INFOCOM, AAAI, IEEE Network, IEEE TNSM, etc. She served as a TPC member of GLOBECOM, NoF, etc.
Crow API: Cross-device I/O Sharing in Web Applications
Seonghoon Park and Jeho Lee (Yonsei University, Korea (South)); Hojung Cha (Yonsei University, S. Korea, Korea (South))
Speaker Seonghoon Park (Yonsei University)
Seonghoon Park is currently working toward the Ph.D. degree in computer science at Yonsei University, Seoul, South Korea. His research interests include mobile web experiences, on-device machine learning, and energy-aware mobile systems.
Rebuffering but not Suffering: Exploring Continuous-Time Quantitative QoE by User's Exiting Behaviors
Sheng Cheng, Han Hu, Xinggong Zhang and Zongming Guo (Peking University, China)
Speaker Sheng Cheng (Peking University), Xinggong Zhang (Peking University)
Sheng Cheng received the bachelor's degree from Peking University, Beijing, China, in 2020. He is currently pursuing the M.S. degree from Wangxuan Institute of Computer Technology, Peking University.
His research interests lie in real-time video streaming, adaptive forward error correction for communication and video quality assessment. He is also interested in the application of Artificial Intelligence in network systems.
Xinggong Zhang (Senior Member, IEEE) received the Ph.D. degree from the Department of Computer Science, Peking University, Beijing, China, in 2011.
He is currently an Associate Professor at Wangxuan Institute of Computer Technology, Peking University. Before that, he was Senior Researcher at Founder Research and Development Center, Peking University from 1998 to 2007. He was a Visiting Scholar with the Polytechnic Institute of New York University from 2010 to 2011. His research interests lie in the modeling and optimization of multimedia networks, VR/AR/video streaming and satellite networks.
Nimble: Fast and Safe Migration of Network Functions
Sheng Liu (Microsoft, USA); Michael Reiter (Duke University, USA); Theophilus A. Benson (Brown University, USA)
Speaker Michael Reiter (Duke University)
Michael Reiter is a James B. Duke Distinguished Professor in the Departments of Computer Science and Electrical & Computer Engineering at Duke University, which he joined in January 2021 following previous positions in industry (culminating as Director of Secure Systems Research at Bell Labs, Lucent) and academia (Professor of CS and ECE at Carnegie Mellon, and Distinguished Professor of CS at UNC-Chapel Hill). His technical contributions lie primarily in computer security and distributed computing.
Efficient Verification of Timing-Related Network Functions in High-Speed Hardware
Tianqi Fang (University of Nebraska Lincoln, USA); Lisong Xu (University of Nebraska-Lincoln, USA); Witawas Srisa-an (University of Nebraska, USA)
In the paper, we propose an invariant-based method to improve the verification without losing soundness. Our method is motivated by an observation that most T-NFs follow a few fixed patterns to use timing information. Based on these patterns, we develop a set of efficient and easy-to-validate invariants to constrain the examination space. According to experiments on real T-NFs, our method can speed up verification by orders of magnitude without tampering the verification soundness.
Speaker Tianqi Fang (University of Nebraska-Lincoln)
I graduated in 2023 with a Ph.D. degree in computer science. I concentrate on formal verification and its application on FPGA-based Network Functions.
CURSOR: Configuration Update Synthesis Using Order Rules
Zibin Chen (University of Massachusetts, Amherst, USA); Lixin Gao (University of Massachusetts at Amherst, USA)
Existing approaches synthesize a safe update order by traversing the update order space, which is time-consuming and does not scale to a large number of configuration updates. This paper proposes CURSOR, a configuration update synthesis that extracts rules update order should follow. We implement CURSOR and evaluate its performance on real-world configuration update scenarios. The experimental results show that we can accelerate the synthesis by an order of magnitude on large-scale configuration updates.
Speaker Zibin Chen (University of Massachusetts, Amherst)
Zibin Chen is a Ph.D. student currently pursuing his degree with the Department of Electrical and Computer Engineering at the University of Massachusetts, Amherst. He received his Master of Science degree from the same institution in 2021 after completing his Bachelor of Engineering degree from Shandong Normal University in China. His research area includes network management, software-defined network, inter-domain routing and network verification.
CaaS: Enabling Control-as-a-Service for Time-Sensitive Networking
Zheng Yang, Yi Zhao, Fan Dang, Xiaowu He, Jiahang Wu, Hao Cao and Zeyu Wang (Tsinghua University, China); Yunhao Liu (Tsinghua University & The Hong Kong University of Science and Technology, China)
Speaker Zeyu Wang (Tsinghua University)
Zeyu Wang is a PhD candidate in School of Software, Tsinghua University, under the supervision of Prof. Zheng Yang. He received his B.E. degree in School of Software from Tsinghua University in 2020. His research interests include Time-Sensitive Networking, edge computing, and Internet of Things.
Houbing H. Song
Video Streaming 4
Collaborative Streaming and Super Resolution Adaptation for Mobile Immersive Videos
Lei Zhang (Shenzhen University, China); Haotian Guo (ShenZhen University, China); Yanjie Dong (Shenzhen University, China); Fangxin Wang (The Chinese University of Hong Kong, Shenzhen, China); Laizhong Cui (Shenzhen University, China); Victor C.M. Leung (Shenzhen University, China & The University of British Columbia, Canada)
Speaker Haotian Guo
EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines
Biao Hou and Song Yang (Beijing Institute of Technology, China); Fernando A. Kuipers (Delft University of Technology, The Netherlands); Lei Jiao (University of Oregon, USA); Xiaoming Fu (University of Goettingen, Germany)
Speaker Biao Hou (Beijing Institute of Technology)
Biao Hou received the B.S. degree in computer science and the M.S. degree in computer science from the Inner Mongolia University, China, in 2018 and 2021, respectively. He is currently the Ph.D. student with the School of Computer Science and Technology, Beijing Institute of Technology. His research interests include edge computing and video streaming delivery.
SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming
Kai Shen, Dayou Zhang and Zi Zhu (The Chinese University of Hong Kong Shenzhen, China); Lei Zhang (Shenzhen University, China); Fangxin Wang (The Chinese University of Hong Kong, Shenzhen, China); Dan Wang (The Hong Kong Polytechnic University, Hong Kong)
In this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based algorithm and the SJA algorithm is further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ~ 46.5%.
Speaker Dayou Zhang
ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics
Ning Chen, Shuai Zhang, Sheng Zhang, Yuting Yan, Yu Chen and Sanglu Lu (Nanjing University, China)
Speaker Ning Chen (Nanjing University)
I am a Ph.D. student in Department of Computer Science and Technology at Nanjing University advised by Associate Professor Sheng Zhang. My research interests are broadly in edge intelligence. More specifically, I focus on two different directions.
AI for Edge: Using ML algorithms (e.g., reinforcement learning) to solve the potential edge‑oriented problems, e.g., resource allocation and request scheduling (TPDS 2020, CN 2021, ICPADS 2019);
Edge for AI: Applying edge computing paradigm to advance the AI applications (e.g., video analytics, video streaming enhancement and federal learning) (INFOCOM 23, CN 21).
In recent two years, I’ve worked on AI/ML‑oriented video system optimization.
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