IEEE INFOCOM 2023

Program at a Glance

IEEE INFOCOM 2023

Session Break-1-Day3: Coffee Break Session Lunch-Day3: Conference Lunch Session Break-2-Day3: Coffee Break

Session A-7: ML Security Session A-8: Internet/Web Security Session A-9: IoT Session A-10: Distributed Learning

Session B-7: PHY Networking Session B-8: Scheduling Session B-9: Wireless Systems Session B-10: Learning

Session C-7: Wireless Charging Session C-8: Network Applications Session C-9: Crowdsourcing Session C-10: Security and Trust

Session D-7: Edge Computing 1 Session D-8: Edge Computing 2 Session D-9: Cross-technology Communications Session D-10: Edge Computing 3

Session E-7: ML Applications Session E-8: Video and Web Applications Session E-9: SDN Session E-10: Video Streaming 4

Session F-7: Distributed Learning Session F-8: Network Design and Fault Tolerance Session F-9: Memory/Cache Management 2 Session F-10: Federated Learning 6

Session G-7: TCP and Congestion Control Session G-9: Cloud/Edge Computing 2 Session G-10: Miscellaneous

Session Poster-2: Poster Session 2

Ant Colony based Online Learning Algorithm for Service Function Chain Deployment

Yingling Mao, Xiaojun Shang and Yuanyuan Yang (Stony Brook University, USA)

Network Function Virtualization (NFV) emerges as a promising paradigm with the potential for cost-efficiency, manage-convenience, and flexibility, where the service function chain (SFC) deployment scheme is a crucial technology. In this paper, we propose an Ant Colony Optimization (ACO) meta-heuristic algorithm for the Online SFC Deployment, called ACO-OSD, with the objectives of jointly minimizing the server operation cost and network latency. As a meta-heuristic algorithm, ACO-OSD performs better than the state-of-art heuristic algorithms, specifically 42.88% lower total cost on average. To reduce the time cost of ACO-OSD, we design two acceleration mechanisms: the Next-Fit (NF) strategy and the many-to-one model between SFC deployment schemes and ant tours. Besides, for the scenarios requiring real-time decisions, we propose a novel online learning framework based on the ACO-OSD algorithm, called prior-based learning real-time placement (PLRP). It realizes near real-time SFC deployment with the time complexity of O(n), where n is the total number of VNFs of all newly arrived SFCs. It meanwhile maintains a performance advantage with 36.53% lower average total cost than the state-of-art heuristic algorithms. Finally, we perform extensive simulations to demonstrate the outstanding performance of ACO-OSD and PLRP compared with the benchmarks.

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)

A variety of network management and orchestration (MANO) tasks take advantage of predictions to support anticipatory decisions. In many practical scenarios, such predictions entail two largely overlooked challenges: (i) the exact relationship between the predicted values (e.g., allocated resources) and the performance objective (e.g., quality of experience of end users) in many cases is tangled and cannot be known a priori, and (ii) multiple predictions contribute to the objective in an intertwined way (e.g., resources are limited and must be shared among competing flows). We present AutoManager, a novel meta-learning model that can support complex MANO tasks by addressing these two challenges. Our solution learns how multiple intertwined predictions affect a common performance goal, and steers them so as to attain the correct operation point under a priori unknown loss function. We demonstrate AutoManager in practical use cases and with real-world traffic measurements, showing how it can achieve substantial gains over state-of-the art approaches

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)

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

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)

There has been much research effort on using small buffers in backbone routers, as they would provide lower delays for users and free capacity for vendors. Unfortunately, with small buffers, droptail policy has an excessive loss rate, and existing AQM (active queue management) policies can be unreliable.

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.

Session Chair

Baochun Li (University of Toronto)

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)

The low-light environment is an integral surrounding in real-world video analytic applications. Conventional wisdom claims that in order to adapt to the extensive computation requirement of the analytics model and achieve high inference accuracy, the overall pipeline should leverage a client-to-cloud framework that designs a cloud-based inference with on-demand video streaming. However, we show that due to the amplified noise, directly streaming the video in low-light scenarios can introduce significant bandwidth inefficiency.
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)

The quality of the video stream is key to neural network-based video analytics. However, low-quality video is inevitably collected by existing surveillance systems because of poor quality cameras or over-compressed/pruned video streaming protocols, e.g., as a result of upstream bandwidth limit. To address this issue, existing studies use quality enhancers (e.g., neural super-resolution) to improve the quality of videos (e.g., resolution) and eventually ensure inference accuracy. Nevertheless, directly applying quality enhancers does not work in practice because it will introduce unacceptable latency. In this paper, we present AccDecoder, a novel accelerated decoder for real-time and neural-enhanced video analytics. AccDecoder can select a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality by neural super-resolution and then up-scale the unselected frames that reference them, which leads to 6-21% accuracy improvement. AccDecoder provides efficient inference capability via filtering important frames using DRL for DNN-based inference and reusing the results for the other frames via extracting the reference relationship among frames and blocks, which results in a latency reduction of 20-80% than baselines.

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))

Although cross-device input/output (I/O) sharing is useful for users who own multiple computing devices, previous solutions had a platform-dependency problem. The meta-platform characteristics of web applications could provide a viable solution. In this paper, we propose the Crow application programming interface (API) that allows web applications to access other devices' I/O through standard web APIs without modifying operating systems or browsers. The provision of cross-device I/O should resolve two key challenges. First, the web environment lacks support for device discovery when making a device-to-device connection. This requires a significant effort for developers to implement and maintain signaling servers. To address this challenge, we propose a serverless Crow connectivity mechanism using devices' I/O-specific communication schemes. Second, JavaScript runtimes have limitations in supporting cross-device inter-process communication (IPC). To solve the problem, we propose a web IPC scheme, called Crow IPC, which introduces a proxy interface that relays the cross-device IPC connection. Crow IPC also provides a mechanism for ensuring functional consistency. We implemented the Crow API as a JavaScript library with which developers can easily develop their applications. An extensive evaluation showed that the Crow API provides cross-device I/O sharing functionality effectively and efficiently on various web applications and platforms.

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)

Quality of Experience (QoE) is one of the most important quality indicators for video streaming applications. But it is still an open question how to assess QoE value objectively and quantitatively over continuous time both for academia and industry. In this paper, we carry out extensive data study on user behaviors in one of the largest short-video service providers. The measurement data reveals that user's exiting behavior in viewing short video stream is an appropriate choice as continuous time QoE metric. Secondly, we build a quantitative QoE model to objectively assess the quality of short-video playback by discretizing playback session into state chain. By collecting 7 billion viewing session logs which cover users from 60 countries and regions, 40 CDN providers and 120 Internet service providers around the world, the proposed state-chain-based model of State-Exiting Ratio (SER) is validated. The experimental results show that the modeling error of SER and session duration are less than 2% and 10s respectively. By using the proposed scheme to optimize adaptive video streaming, the average session duration is improved up to 60% to baseline, and 20% to the existing black-box-like machine learning methods.

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.

Session Chair

Xuyu Wang

Nimble: Fast and Safe Migration of Network Functions

Sheng Liu (Microsoft, USA); Michael Reiter (Duke University, USA); Theophilus A. Benson (Brown University, USA)

Network function (NF) migration alongside (and possibly because of) routing policy updates is a delicate task, making it difficult to ensure that all traffic is processed by its required network functions, in order. Indeed, all previous solutions to this problem adapt routing policy only after NFs have been migrated, in a serial fashion. This paper proposes a design called Nimble for interleaving these tasks to complete both more efficiently while ensuring complete processing of traffic by the required NFs, provided that the route-update protocol enforces a specific property that we define. We demonstrate the benefits of the Nimble design using an implementation in Open vSwitch and the Ryu controller, building on both known routing update protocols and a new protocol of our design that implements specifically the needed property.

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)

To achieve line rate in the high-speed environment of modern networks, there is a continuing effort to offload network functions from software to programmable hardware (ProgHW). Although the offloading has shown greater performance, it brings up difficulty in the verification of timing-related network functions (T-NFs). T-NFs use numerical timing values to complete various network tasks. For example, a congestion control algorithm BBR uses round-trip time to improve throughput. Errors in T-NFs could cause packet loss and poor throughput. However, verifying T-NFs in ProgHW often involves many clock cycles that can result in an exponentially increasing number of test cases. Current verification methods either do not scale or sacrifice soundness for scalability.

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)

Configuration updates to networks are frequent nowadays to adapt to the rapid evolution of networks. To ensure the safety of the configuration update, network verification can be used to verify that network properties hold for the new configuration. However, configuration updates typically involve multiple routers changing their configurations. Changes on these routers can not be applied simultaneously. This leads to intermediate configurations, which might violate network properties such as reachability. Configuration updates synthesis aims to find an order of applying changes on routers such that network properties hold for all intermediate configurations.

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)

Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into Time-Sensitive Networking (TSN) switches. By combining control and transmission functions in switches, CaaS virtualizes the whole industrial TSN network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.

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.

Session Chair

Houbing H. Song

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)

Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency for immersive video steaming. The former allows the selective download for the contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to reconstruct the received video into higher quality using the advanced neural network models. In this work, we propose CASE, a collaborated adaptive streaming and enhancement framework for mobile immersive videos, which integrates super resolution with tile-based streaming to optimize user experience with dynamic bandwidth and limited computing capability. To coordinate the video transmission and reconstruction in CASE, we identify and address several key design issues including unified video quality assessment, computation complexity model for super resolution, and buffer analysis considering the interplay between transmission and reconstruction. We further formulate the quality-of-experience (QoE) maximization problem for mobile immersive video streaming and propose a rate adaptation algorithm to make the best decisions for download and for reconstruction based on the Lyapunov optimization theory. The extensive evaluation results validate the superiority of our proposed approach, which presents stable performance with considerable QoE improvement and is able to adjust the trade-off between playback smoothness and video quality.

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)

Recent years have witnessed video streaming gradually evolves into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing the Quality of Experience (QoE) for video streaming is a long-standing challenge. The emergence of serverless computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel computing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimization scheme to solve the video bitrate adaptation issue. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better user-perceived QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Extensive results show that our proposed EAVS significantly improves user-perceived QoE and reduces the stall rate, achieving over 9.1% QoE improvement and 60.2% latency decrease compared to state-of-the-art solutions.

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)

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing great challenges to service providing. First, the unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as loss, bitrate, network conditions, etc.

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)

Deploying deep convolutional neural network (CNN) to perform video analytics at edge poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in computational resources. Model partitioning, as a promising approach, splits CNNs and distributes them to multiple edge devices in closer proximity to each other for serial inferences, however, it causes considerable cross-edge delay for transmitting intermediate feature maps. To overcome this challenge, we present ResMap, a new edge video analytics framework that significantly improves the cross-edge transmission and flexibly partitions the CNNs. Briefly, by exploiting the sparsity of the intermediate raw or residual feature map, ResMap effectively removes the redundant transmission, thereby decreasing the cross-edge transmission delay. In addition, ResMap incorporates an Online Data-Aware Scheduler to regularly update the CNN partitioning scheme so as to adapt to the time-varying edge runtime and video content. We have implemented ResMap fully based on COTS hardware, and the experimental results show that ResMap reduces the intermediate feature map volume by 14.93-46.12% and improves the average processing time by 17.43-30.6% compared to other alternative designs.

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.

Session Chair

Jun ZHAO

Program at a Glance