IEEE/ACM International Symposium on Quality of Service (IWQoS) 2020
Artificial Intelligence of Things: Intelligence, Battery-free, and Security
Xiangyang Li (USTC, China)
Tommaso Melodia (Northeastern U)
A Deep Reinforcement Learning Approach for Online Computation Offloading in Mobile Edge Computing
Yameng Zhang and Tong Liu (Shanghai University, China); Yanmin Zhu (Shanghai Jiao Tong University, China); Yuanyuan Yang (Stony Brook University, USA)
Decode-and-Compare: An Efficient Verification Scheme for Coded Edge Computing
Mingjia Fu (Soochow University, China); Jin Wang (Soochow Univerisity & City University of Hong Kong, China); Jianping Wang (City University of Hong Kong, Hong Kong); Kejie Lu (University of Puerto Rico at Mayaguez, Puerto Rico); Admela Jukan (Technische Universität Carolo-Wilhelmina zu Braunschweig, Germany); Fei Gu (Soochow University, China)
Finedge: A Dynamic Cost-efficient Edge Resource Management Platform for NFV Network
Miao Li, Qixia Zhang and Fangming Liu (Huazhong University of Science and Technology, China)
Incentive Assignment in PoW and PoS Hybrid Blockchain in Pervasive Edge Environments
Yaodong Huang, Yiming Zeng, Fan Ye and Yuanyuan Yang (Stony Brook University, USA)
Rong Zheng (McMaster)
Best Paper and IWQoS 2021
Best Paper and IWQoS 2021
Kui Ren, Jinsong Han (General co-chairs), Dan Wang, Xue Liu, Tommaso Melodia (Program co-chairs)
Dan Wang (Hong Kong PolyU)
Delay-sensitive Computation Partitioning for Mobile Augmented Reality Applications
Chaokun Zhang (Tianjin University, China); Rong Zheng (McMaster University, Canada); Yong Cui (Tsinghua University, China); Chenhe Li (McMaster University, Canada); Jianping Wu (Tsinghua University, China)
Modeling and Analyzing Live Streaming Performance
Tong Zhang (Nanjing University of Aeronautics and Astronautics, China); Fengyuan Ren and Bo Wang (Tsinghua University, China)
Generative Adversarial Networks-based Privacy-Preserving for 3D Reconstruction
Qinya Li (Shanghai Jiaotong University, China); Zhenzhe Zheng, Fan Wu and Guihai Chen (Shanghai Jiao Tong University, China)
privacy-preserving while guaranteeing to reconstruct a complete 3D model is important and significant. In this paper, we propose PicPrivacy to address this problem, which consists of three parts. (1) Using a pre-trained deep convolution neural network to identify sensitive information and erase it from images. (2) Using a GAN-based image feature completion algorithm to repair blank regions and minimize the absolute information gap between generated images and raw ones. (3) Taking generated images as the input of 3D reconstruction and using a structure-
from-motion algorithm to reconstruct 3D models. Finally, we extensively evaluate the performance of PicPrivacy on real-world datasets. The results demonstrate that PicPrivacy not only achieves individual privacy-preserving but also can guarantee to create complete 3D models.
PQA-CNN: Towards Perceptual Quality Assured Single-Image Super-Resolution in Remote Sensing
Yang Zhang, Xiangyu Dong, Md Tahmid Rashid, Lanyu Shang, Jun Han, Daniel Zhang and Dong Wang (University of Notre Dame, USA)
Panlong Yang (USTC)
Network-based Malware Detection with a Two-tier Architecture for Online Incremental Update
Anli Yan and Zhenxiang Chen (University of Jinan, China); Riccardo Spolaor (University of Oxford, United Kingdom (Great Britain)); Shuaishuai Tan (Huawei Technologies, China); Chuan Zhao, Lizhi Peng and Bo Yang (University of Jinan, China)
Application-Layer DDoS Defense with Reinforcement Learning
Yebo Feng, Jun Li and Thanh Nguyen (University of Oregon, USA)
In this paper, we propose a new, reinforcement-learning-based approach to L7 DDoS attack defense. We introduce a multi-objective reward function to guide a reinforcement learning agent to learn the most suitable action in mitigating L7 DDoS attacks. Consequently, while actively monitoring and analyzing the victim server, the agent can apply different strategies under different conditions to protect the victim: When an L7 DDoS attack is overwhelming, the agent will aggressively mitigate as many malicious requests as possible, thereby keeping the victim server functioning (even at the cost of sacrificing a small number of legitimate requests); otherwise, the agent will conservatively mitigate malicious requests instead, with a focus on minimizing collateral damage to legitimate requests. Our evaluation results show that our approach can mitigate 98.73% of the malicious application messages when the victim is brought to its knees and achieve minimal collateral damage when the L7 DDoS attack is tolerable.
Localizing Failure Root Causes in a Microservice through Causality Inference
Yuan Meng (Tsinghua University, China); Shenglin Zhang and Yongqian Sun (Nankai University, China); Ruru Zhang (NanKai University, China); Zhilong Hu (Nankai University, China); Yiyin Zhang, Chenyang Jia and Zhaogang Wang (Alibaba Group, China); Dan Pei (Tsinghua University, China)
The stability of microservice is thus vitally important for these applications' quality of service.
Accurate failure root cause localization can help operators quickly recover microservice failures and mitigate loss.
Although cross-microservice failure root cause localization has been well studied, how to localize failure root causes in a microservice so as to quickly mitigate this microservice has not yet been studied.
In this work, we propose a framework, MicroCause, to accurately localize the root cause monitoring indicators in a microservice.
MicroCause combines a new pass condition time series (PCTS) algorithm which accurately captures the sequential relationship of time series data, and a novel temporal cause oriented random walk (TCORW) method integrating the causal relationship, temporal order and priority information of monitoring data.
We evaluate MicroCause based on 86 real-world failure cases collected from a top tier global online shopping service.
Our experiments show that the top 5 accuracy AC@5 of MicroCause for intra-microservice failure root cause localization is 98.7%, which is greatly higher (by 33.4%) than the best baseline method.
LogSayer: Log Pattern-driven Cloud Component Anomaly Diagnosis with Machine Learning
Pengpeng Zhou, Yang Wang and Zhenyu Li (Institute of Computing Technology, Chinese Academy of Sciences, China); Xin Wang (Stony Brook University, USA); Gareth Tyson (Queen Mary, University of London, United Kingdom (Great Britain)); Gaogang Xie (Institute of Computing Technology, Chinese Academy of Sciences, China)
Yong Cui (Tsinghua U)
Network Optimization and Network Intelligence
Taming the Wildcards: Towards Dependency-free Rule Caching with FreeCache
Rui Li, Bohan Zhao, Ruixin Chen and Jin Zhao (Fudan University, China)
In this paper, we show how to give applications the flexibility of completely dependency-free wildcard rule caching by decoupling the cached rules and their dependent rules. Our FreeCache scheme has wide applicability to packet classification devices with wildcard rule caching. We validate the effectiveness of FreeCache through two respects: (1) Implementing various cache algorithms (e.g., LSTM) and cache replacement algorithms (e.g., ARC, LIRS) that are difficult to use in dependency-bound situations in the cache system with FreeCache. (2) Developing a prototype in a Software-Defined Network (SDN), where hybrid OpenFlow switches use TCAM as cache and RAM as auxiliary memory. Our experimental results reveal that FreeCache improves the cache performance by up to 60.88% in the offline scenario. FreeCache also offers the promise of applying any existing caching algorithms to wildcard rule caching while guaranteeing the properties of semantic correctness and equivalence.
Additive and Subtractive Cuckoo Filters
Kun Huang (Southern Universtiy of Science and Technology & Peng Cheng Laboratory, China); Tong Yang (Peking University, China)
To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.
Adaptive and Robust Network Routing Based on Deep Reinforcement Learning with Lyapunov Optimization
Zirui Zhuang, Jingyu Wang, Qi Qi and Jianxin Liao (Beijing University of Posts and Telecommunications, China); Zhu Han (University of Houston, USA)
Multi-layer Coordination for High-Performance Energy-Efficient Federated Learning
Li Li (Shenzhen Institutes Of Advanced Technology Chinese Academy Of Sciences, China); Jun Wang (FutureWei Technology, USA); Xu Chen (Sun Yat-sen University, China); Cheng-Zhong Xu (University of Macau, China)
In this paper, we propose MCFL, a multi-layer online coordination framework for high-performance energy efficient federated learning. MCFL consists of two layers: a macro-layer on the central server and a micro-layer on each participating device. In each training round, the macro coordinator performs two tasks, namely, selecting the right devices to participate, and estimating
a time limit, such that the overall training time is significantly reduced while still guaranteeing the model accuracy. Unlike existing systems, MCFL removes the restriction that participating devices must be connected to power sources, thus allowing more timely and ubiquitous training. This clearly requires on-device training to be highly energy-efficient. To this end, the micro coordinator determines optimal schedules for hardware resources
in order to meet the time limit set by the macro coordinator with the least amount of energy consumption. Tested on real devices as well as simulation testbed, MCFL has shown to be able to effectively balance the convergence rate, model accuracy and energy efficiency. Compared with existing systems, MCFL can
achieve a speedup up to 8.66 and reduce energy consumption by up to 76.5% during the training process.
Private Deep Neural Network Models Publishing for Machine Learning as a Service
Yunlong Mao, Boyu Zhu, Wenbo Hong, Zhifei Zhu, Yuan Zhang and Sheng Zhong (Nanjing University, China)
Fangming Liu (Huazhong UST)