IEEE/ACM International Symposium on Quality of Service (IWQoS) 2022
Keynote 3: Network Configuration for High-performance Distributed Machine Learning
Network Configuration for High-performance Distributed Machine Learning
Carla Fabiana Chiasserini, IEEE Fellow, Politecnico di Torino, Italy
Session Chair
Jiangchuan Liu, Simon Fraser University, Canada
Keynote 4: From a need, to an idea, to a complete system: a perspective based on real-world applications
From a need, to an idea, to a complete system: a perspective based on real-world applications
Pål Halvorsen, SimulaMet; and Oslo Metropolitan University, Norway
Session Chair
Dan Wang, The Hong Kong Polytechnic University, China
Privacy
Privacy-Preserving and Robust Federated Deep Metric Learning
Yulong Tian and Xiaopeng Ke (Nanjing University, China); Zeyi Tao (William and Mary, USA); Shaohua Ding and Fengyuan Xu (Nanjing University, China); Qun Li (William & Mary, USA); Hao Han (Nanjing University of Aeronautics and Astronautics, China); Sheng Zhong (Nanjing University, China); Xinyi Fu (Ant Group, China)
PPAR: A Privacy-Preserving Adaptive Ranking Algorithm for Multi-Armed-Bandit Crowdsourcing
Shuzhen Chen and Dongxiao Yu (Shandong University, China); Feng Li (Shandong Universiy, China); Zongrui Zou (Shandong University, China); Weifa Liang (City University of Hong Kong, Hong Kong); Xiuzhen Cheng (Shandong University, China)
A Privacy-aware Distributed Knowledge Graph Approach to QoIS-driven COVID-19 Misinformation Detection
Lanyu Shang and Ziyi Kou (University of Illinois at Urbana-Champaign, USA); Yang Zhang (University of Notre Dame, USA); Jin Chen (Cleveland Clinic, USA); Dong Wang (University of Illinois at Urbana-Champaign, USA)
Nearly Optimal Protocols for Computing Multi-party Private Set Union
Xuhui Gong, Qiang-Sheng Hua and Hai Jin (Huazhong University of Science and Technology, China)
For this special case (two-party), we further optimize and design a more efficient protocol using OT protocol, i.e., OT-PSU. It only requires O(1) rounds and O(k\lambda) communication complexity which almost matches the communication lower bound \Omega(k). More importantly, it avoids using computationally expensive public key operations (exponentiations). In other words, the number of exponentiations in this protocol is independent of the size of the data sets.
Compared with the existing protocols, our two protocols have the lowest communication, computation and round complexities.
Session Chair
Yifei Zhu, Shanghai Jiao Tong University
Mobile and Wireless Networks
An Experimental Study of Triggered Multi-User Uplink Access with Real Application Traffic
Vinicius Da Silva Goncalves and Edward W. Knightly (Rice University, USA)
Bandwidth Prediction for 5G Cellular Networks
Yuxiang Lin, Wei Dong and Yi Gao (Zhejiang University, China)
Investigating the Predictability of QoS Metrics in Cellular Networks
Stefan Herrnleben, Johannes Grohmann, Veronika Lesch, Thomas Prantl, Florian Metzger, Tobias Ho?feld and Samuel Kounev (University of Wuerzburg, Germany)
This work investigates the predictability of QoS metrics in cellular networks based on the experience of previous measurements. For this, we developed an Android app to measure download bitrates with minimal data consumption. We performed over 90000 measurements using a single network operator and analyzed how precise QoS indicators like packet round trip times and download bitrates can be predicted. We developed a methodology to predict the expected download bitrate along a route and present our approach of aggregating measurements into hexagons of dynamic size. The core contributions of this work are (i) a methodology and implementation of systematic measurement data collection, (ii) an open data publication of our measurement data set, and (iii) an approach for predicting QoS metrics in cellular networks based on aggregated measurements. Our results show, that our approach is able to predict the downlink bitrate, the packet round trip time (ping), or DNS query duration along a given route.
Timely-throughput Optimal Scheduling for Wireless Flows with Deep Reinforcement Learning
Qi Wang and ChenTao He (Institute of Computing Technology, Chinese Academy of Sciences, China); Katia Jaffrès-Runser (University of Toulouse - Toulouse INP & IRIT Laboratory, France); Jianhui Huang and Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences, China)
Session Chair
Yong Cui, Tsinghua University, China
Edge Computing
When Multi-access Edge Computing Meets Multi-area Intelligent Reflecting Surface: A Multi-agent Reinforcement Learning Approach
Shen Zhuang and Ying He (Shenzhen University, China); F. Richard Yu (Carleton University, Canada); Chengxi Gao (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China); Weike Pan and Zhong Ming (Shenzhen University, China)
JCSP: Joint Caching and Service Placement for Edge Computing Systems
Yicheng Gao and Giuliano Casale (Imperial College London, United Kingdom (Great Britain))
Service Placement and User Assignment in Multi-Access Edge Computing with Base-Station Failure
Haruto Taka, Fujun He and Eiji Oki (Kyoto University, Japan)
worst-case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-hard and introduce two algorithms with allocation upgrade and preemption to solve the problem. The results show that the introduced algorithms obtain a solution with the smaller worst-case penalty than the benchmark in a practical time.
Dynamic Pricing Scheme for Edge Computing Services: A Two-layer Reinforcement Learning Approach
Feng Lyu and Xinyao Cai (Central South University, China); Fan Wu (Tsinghua University, China); Huali Lu and Sijing Duan (Central South University, China); Ju Ren (Tsinghua University, China)
Session Chair
Nakjung Choi, Nokia Bell Labs
Traffic Analysis
AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples
Hao Liu and Jimmy Dani (University of Cincinnati, USA); Hongkai Yu (Cleveland State University, USA); Wenhai Sun (Purdue University, USA); Boyang Wang (University of Cincinnati, USA)
Flow Sequence-Based Anonymity Network Traffic Identification with Residual Graph Convolutional Networks
Ruijie Zhao and Xianwen Deng (Shanghai Jiao Tong University, China); Yanhao Wang (QI-ANXIN Technology Research Institute, China); Libo Chen, Ming Liu, Zhi Xue and Yijun Wang (Shanghai Jiao Tong University, China)
APS: Adaptive Packet Sizing for Efficient End-to-End Network Transmission
Feixue Han (Tsinghua University, China); Qing Li (Peng Cheng Laboratory, China); Jianer Zhou (SUSTech, China); Hong Xu (The Chinese University of Hong Kong, Hong Kong); Yong Jiang (Graduate School at Shenzhen, Tsinghua University, China)
iSwift: Fast and Accurate Impact Identification for Large-scale CDNs
Jiyan Sun (Institute of Information Engineering, Chinese Academy of Sciences, China); Tao Lin (Communication University of China, China); Yinlong Liu (Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, University of Chinese Academy of Sciences, China); Xin Wang (Stony Brook University, USA); Bo Jiang (Shanghai Jiao Tong University, China); Liru Geng (Institute of Information Engineering Chinese Academy of Sciences, China); Pengkun Jing and Liang Dai (Institute of Information Engineering, Chinese Academy of Sciences, China)
Session Chair
Cristina Alcaraz, University of Malaga, Spain
Video Streaming
VPPlus: Exploring the Potentials of Video Processing for Live Video Analytics at the Edge
Junpeng Guo, Shengqing Xia and Chunyi Peng (Purdue University, USA)
Ivory: Learning Network Adaptive Streaming Codes
Salma Emara, Fei Wang, Isidor Kaplan and Baochun Li (University of Toronto, Canada)
To maintain the highest quality, Ivory attempts to correct as many lost packets as possible on-the-fly, yet incurring the smallest footprint in terms of coding overhead over the network. To achieve such an objective, Ivory uses a deep reinforcement learning agent to estimate the best coding parameters in real-time based on observed network states and experience learned. It learns offline the best coding parameters to use based on previously observed loss patterns and takes into account the round-trip time observed to decide on the optimum decoding delay for a low-latency application. Our extensive array of experiments shows that Ivory achieves a better trade-off between recovering packets and using lower redundancy than the state-of-the-art network adaptive algorithms.
Choice-supportive bias affects video viewing experience: Subjective experiment and evaluation
Daichi Kominami (Osaka University, Japan)
Harmonizing Energy Efficiency and QoE for Brightness Scaling-based Mobile Video Streaming
Chao Qian, Daibo Liu and Hongbo Jiang (Hunan University, China)
Session Chair
Bo Wang, Tsinghua University
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