Session Keynote-2

Keynote 2

8:30 AM — 9:30 AM HKT
Jun 15 Mon, 8:30 PM — 9:30 PM EDT

Quality of Service aware Security and Privacy for Cloud and Edge Computing Environments

Jaideep Vaidya (Rutgers, USA)

This talk does not have an abstract.

Session Chair

Xue Liu (McGill)

Session Keynote-3

Keynote 3

9:30 AM — 10:30 AM HKT
Jun 15 Mon, 9:30 PM — 10:30 PM EDT

Domain-Specific Network Optimization for Distributed Deep Learning

Kai Chen (Hong Kong UST, Hong Kong)

This talk does not have an abstract.

Session Chair

Jinsong Han (Zhejiang U)

Session 2A


10:45 AM — 12:05 PM HKT
Jun 15 Mon, 10:45 PM — 12:05 AM EDT

Improving the Path Programmability for Software-Defined WANs under Multiple Controller Failures

Zehua Guo and Songshi Dou (Beijing Institute of Technology, China); Wenchao Jiang (University of Minnesota, USA)

Enabling path programmability is an essential feature of Software-Defined Networking (SDN). During controller failures in Software-Defined Wide Area Networks (SD-WANs), a resilient design should maintain path programmability for offline flows, which were controlled by the failed controllers. Existing solutions can only partially recover the path programmability rooted in two problems: (1) the implicit preferable recovering flows with long paths and (2) the sub-optimal remapping strategy in the coarse-grained switch level. In this paper, we propose ProgrammabilityGuardian to improve the path programmability of offline flows while maintaining low communication overhead. These goals are achieved through the fine-grained flow-level mapping enabled by existing SDN techniques. ProgrammabilityGuardian configures the flow-controller remapping to recover offline flows with a similar path programmability, maximize the total programmability of the offline flows, and minimize the total communication overhead for controlling these recovered flows. Simulation results of different controller failure scenarios show that ProgrammabilityGuardian recovers all offline flows with a balanced path programmability, improves the total programmability of the recovered flows up to 68%, and reduces the communication overhead up to 83%, compared with the baseline algorithm.

Joint Switch-Controller Association and Control Devolution for SDN Systems: An Integration of Online Control and Online Learning

Xi Huang, Yinxu Tang, Ziyu Shao and Yang Yang (ShanghaiTech University, China); Hong Xu (City University of Hong Kong, Hong Kong)

In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems the decision making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this paper, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution (LASAC) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear regret bound over a finite time horizon.

Greening Reliability of Virtual Network Functions via Online Optimization

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

The fast development of virtual network functions (VNFs) brings new challenges to providing reliability. The widely adopted approach of deploying backups incurs financial costs and environmental impacts. On the other hand, the recent trend of incorporating renewable energy into computing systems provides great potentials, yet the volatility of renewable energy generation presents significant operational challenges. In this paper, we optimize availability of VNFs under a limited backup budget and renewable energy using a dynamic strategy GVB. GVB applies a novel online algorithm to solve the VNF reliability optimization problem with non-stationary energy generation and VNF failures. Both theoretical bound and extensive simulation results highlight that GVB provides higher reliability compared with existing baselines.

Serpens: A High-Performance Serverless Platform for NFV

Junxian Shen, Heng Yu and Zhilong Zheng (Tsinghua University, China); Chen Sun (Alibaba Group & Tsinghua University, China); Mingwei Xu and Jilong Wang (Tsinghua University, China)

Many enterprises run Network Function Virtualization (NFV) services on public clouds to relieve management burdens and reduce costs. However, NFV operators still face the burden of choosing the right types of virtual machines (VMs) for various network functions (NFs), as well as the cost of renting VMs at a granularity of months or years while many VMs remain idle during valley hours. A recent computing model named serverless computing automatically executes user-defined functions on requests arrival, and charges users based on the number of processed requests. For NFV operators, serverless computing has the potential of completely relieving NF management burden and significantly reducing costs.
Nevertheless, naively exploring existing serverless platforms for NFV introduces significant performance overheads in three aspects, including high remote state access latency, long NF launching time, and high packet delivery latency between NFs.
To address these problems, we propose Serpens, a high-performance serverless platform for NFV. Firstly, Serpens designs a novel state management mechanism to support local state access. Secondly, Serpens proposes an efficient NF execution model to provide fast NF launching and avoid extra packet delivery.
We have implemented a prototype of Serpens. Evaluation results demonstrate that Serpens could significantly improve performance for NFs and service function chains (SFCs) comparing to existing serverless platforms.

Session Chair

Lei Jiao (Oregon)

Session 2B

Wireless Network

1:30 PM — 2:50 PM HKT
Jun 16 Tue, 1:30 AM — 2:50 AM EDT

Decimeter-Level WiFi Tracking in Real-Time

Zheng Yang and Wei Gong (University of Science and Technology of China, China)

This paper presents DeTrack, a tracking system that can continuously trace WiFi objects at decimeter-level in real-time. To enable this, we make three main proposals. The first one is a super-resolution localization scheme that combines compressive sensing and expectation-maximization algorithms to iteratively resolve multipath, which realizes better resolution compared against traditional MUSIC. The second part is a customized particle filter that takes advantage of WiFi signals and the geometric nature of AoA estimates to properly update location states and particle weights. Finally, an SVD-based multi-packet fusion is employed to reinforce the signal space and improve tracking efficiency at the same time. A prototype is built using only commercial WiFi NICs. Extensive experiments demonstrate that DeTrack achieves an 80th percentile localization accuracy of 0.9 meters and a median latency of around 90 milliseconds. As a result, DeTrack is looking to benefit a wide range of applications, e.g., indoor navigation, intelligent logistics, and smart cities.

Exploiting Rateless Codes and Cross-Layer Optimization for Low-Power Wide-Area Networks

Gonglong Chen, Jiamei Lv and Wei Dong (Zhejiang University, China)

Long communication range and low energy consumption are two most important design goals of Low-Power Wide-Area Networks (LPWAN), however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. Although there are PHY-layer and link layer C9approaches proposed to improve the performance of LPWAN, they either rely heavily on the hardware modifications or suffer from low data recovery capability especially with bursty packet loss pattern. In this paper, we propose a practical system, eLoRa, for COTS devices. eLoRa utilizes rateless codes and jointly decoding with multiple gateways to extend the communication range and lifetime of LoRaWAN. To further improve the performance of LoRaWAN, eLoRa optimizes parameters of the PHY-layer (e.g., spreading factor) and the link layer (e.g, block length). We implement eLoRa on COTS LoRa devices, and conduct extensive experiments on outdoor testbed to evaluate the effectiveness of eLoRa. Results show that eLoRa can effectively improve the communication range of DaRe and LoRaWAN by 43.2% and 55.7% with packet reception ratio higher than 60%, and increase the expected lifetime of DaRe and LoRaWAN by 18.3% and 46.6%.

i5GAccess: Nash Q-learning Based Multi-Service Edge Users Access in 5G Heterogeneous Networks

Anqi Zhu (Southwest University, China); Songtao Guo (Chongqing University, China); Mingfang Ma (Southwest University, China)

In the heterogeneous wireless networks, it remains a significant challenge to achieve an efficient network selection strategy to satisfy the demands of a massive number of edge users and novel 5G services. In this paper, we formulate the network selection problem for edge users as a discrete-time Markov model, and propose a Nash Q-learning based intelligent network access algorithm for multi-agent system, named MAQNS. We consider the joint optimization of network selection strategies among different types of networks, aims at maximizing the long-term performance of multi-agent system. Meanwhile, we use Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to characterize the user preferences for networks. Experimental results show that comparing to the existing network selection algorithms, the proposed MAQNS has better performance in terms of system throughput, user blocking probability, average energy efficiency, average delay and average user satisfaction.

Online Distributed Edge Caching for Mobile Data Offloading in 5G Networks

Yiming Zeng, Yaodong Huang, Zhenhua Liu and Yuanyuan Yang (Stony Brook University, USA)

Edge caching is an effective approach to improve the quality of service for mobile users and therefore a critical component for5G networks. Despite the importance, it is not clear how to determine which contents to cache and how to serve the requests in 5G networks to minimize the total operational cost in a distributed and online manner, especially when some mobile users can be served by multiple small base stations. In this paper, we formulate an optimization problem to jointly decide the caching policy and the routing decision. There are two challenges: the need for distributed control and the lack of future information. We therefore develop an online distributed algorithm with provable performance guarantees in terms of convergence and competitive ratio compared to the offline optimal solution. Numerical simulations based on real-world traces highlight the significant performance improvement compared to existing baselines.

Session Chair

Fan Wu (Shanghai Jiaotong U)

Session 2C


3:10 PM — 4:30 PM HKT
Jun 16 Tue, 3:10 AM — 4:30 AM EDT

High-quality Activity-Level Video Advertising

Mu Yuan, Lan Zhang, Zhengtao Wu and Daren Zheng (University of Science and Technology of China, China)

Online video advertising is a billion dollar business, but the current low CTR reveals the huge potential for improvement in the ad serving quality.
In this work, we present a novel activity-level video advertising system named ActVA. Different from existing systems that assume a fixed scope of ad keywords, ActVA enables advertising targeted to non-predefined activities in a highly efficient way requiring no training data for diverse activities.
To achieve this goal, a general and extensible graphical representation of both video content and advertising demand is proposed to embed multimodal content at the activity level. Our ad-content relevancy measurement can achieve 10,000 FPS retrieval speed. We model the ads assigning task as an optimization problem taking content relevance, ads revenue as well as viewer experience into consideration. A non-maximal suppression based algorithm is designed to significantly reduce the algorithm complexity for online ad serving. Our extensive objective and subjective experimental results show the effectiveness and efficiency of ActVA. ActVA can effectively uncovers numerous high-quality (content-relevant) advertising opportunities and delivers ads to viewers in a profitable and user-friendly way.

DeepQoE: Real-time Measurement of Video QoE from Encrypted Traffic with Deep Learning

Meng Shen (Beijing Institute of Technology, China); Jinpeng Zhang (Beijing Institute of Tchnology, China); Ke Xu (Tsinghua University, China); Liehuang Zhu (Beijing Institute of Technology, China); Jiangchuan Liu (Simon Fraser University, Canada); Xiaojiang Du (Temple University, USA)

This talk does not have an abstract.

Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation

YiXin Chen (National University of Defense Technology, China); Xinye Lin (National Key Laboratory of Science and Technology on Blind Signal Processing, China); Keshi Ge (National University of Defense Technology, China); Wenbo He (McMaster University, Canada); Dongsheng Li (School of Computer, National University of Defense Technology, China)

In the era of big data, web videos are known for their astronomical volume, and high difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms such as YouTube.
Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of adjacent videos. From this intuition, we develop a semi-supervised approach to accurately estimate the relevance between Internet videos and user-provided labels, and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.

MPTCP+: Enhancing Adaptive HTTP Video Streaming over Multipath

Jia Zhao and Jiangchuan Liu (Simon Fraser University, Canada); Cong Zhang (University of Science and Technology of China, China); Yong Cui (Tsinghua University, China); Yong Jiang (Graduate School at Shenzhen, Tsinghua University, China); Wei Gong (University of Science and Technology of China, China)

HTTP-based adaptive video streaming has become a key contributor to the Internet traffic. The streaming quality however is heavily affected by the throughput and stability of the underlying TCP. Being compatible and fair, multipath TCP (MPTCP) has recently been suggested as a promising enhancement to the conventional TCP, particularly considering that multi-home is becoming ubiquitous for modern networked devices. The opportunities and challenges of adaptive streaming over MPTCP however remain largely unclear to the research community.

This paper presents a systematic study on adaptive streaming over MPTCP. We start from realworld experiments with Dynamic Adaptive Streaming over HTTP (DASH) and analysis on its performance over MPTCP. We show that DASH can greatly benefit from the improved aggregated throughput by MPTCP; yet the inter-path throughput difference and the intra-path throughput fluctuation have noticeable (negative) impact, too. Without a proper design of path selection and adaptation in MPTCP, they can easily confuse the adaptation logic of DASH, resulting in low bitrates or frequent rebuffering even if high-bandwidth paths are available. We present MPTCP+, an extended multipath TCP solution to offer high quality and smooth playback for adaptive HTTP streaming. MPTCP+ incorporates a path use decision algorithm that smartly disables/enables a path to minimize the inter-path difference, and a novel congestion control algorithm that smooths congestion window evolution with multiple paths. We have implemented MPTCP+ in the MPTCP Linux kernel, with minimum change on the server-side MPTCP module only. It is fully compatible with the existing MPTCP clients and requires no change on the upper-layer protocols, too. Our experiments suggest that MPTCP+ increases the quality of experience (QoE) of DASH by up to 50\%.

Session Chair

Dan Li (Tsinghua U)

Session 2D

Sensing and Wireless

4:50 PM — 6:10 PM HKT
Jun 16 Tue, 4:50 AM — 6:10 AM EDT

RLLL: Accurate Relative Localization of RFID Tags with Low Latency

Xuan Liu and Quan Yang (Hunan University, China); Shigeng Zhang (Central South University, China); Bin Xiao (The Hong Kong Polytechnic University, Hong Kong)

Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in smart libraries, we need to know the relative ordering of the tags attached to the books. Although several relative RFID localization algorithms have been proposed, they usually suffer from large localization latency and cannot support applications that require real-time detection of tag (product) positions like automatic manufacturing on an assembly line. Moreover, existing approaches face significant
degradation in ordering accuracy when the tags are close to each other. In this paper, we propose RLLL, an accurate Relative Localization algorithm for RFID tags with Low Latency. RLLL reduces localization latency by proposing a novel geometry-based approach to identifying the V-zone in the phase reading sequence of each tag. Moreover, RLLL uses only the data in the V-zone to calculate relative positions of tags, and thus avoids the negative effects of low-quality data collected when the tag is far from the antenna. Experimental results with COTS RFID devices show that RLLL achieves ordering accuracy of higher than 0.986 with latency less than 0.8 seconds even when the tags are spaced only 7 mm, in which case the state-of-the-art solution only achieves accuracy lower than 0.8 with latency larger than 3 seconds.

GuardRider: Reliable WiFi Backscatter Using Reed-Solomon Codes With QoS Guarantee

Xin He (University of Science and Technology of China & Anhui Normal University, China); Weiwei Jiang (The University of Melbourne, Australia); Meng Cheng (Japan Advanced Institute of Science and Technology, Japan); Xiaobo Zhou (Tianjin University, China); Panlong Yang (University of Science and Technology of China, China); Brian Michael Kurkoski (Japan Advanced Institute of Science and Technology (JAIST), Japan)

The WiFi backscatter communications offer ultra-low power and ubiquitous connections for IoT systems. Caused by the intermittent-nature of the WiFi traffics, state-of-the-art WiFi backscatter communications are not reliable for backscatter link or simple for the tag to do the adaptive transmission. In order to build reliable WiFi backscatter communications, we present GuardRider, a WiFi backscatter system that enables backscatter communications to improve the quality of service (QoS). The key contribution of GuardRider is an optimization algorithm of designing RS codes to follow the statistical knowledge of WiFi traffics and adjust backscatter transmission. With GuardRider, the reliable baskscatter link is guaranteed and a backscatter tag is able to adaptively transmit information without heavily listening to the excitation channel, by taking QoS into account. We built a hardware prototype of GuardRider using a customized tag with FPGA implementation. Both the simulations and field experiments verify that GuardRider could achieve notably gains in bit error rate and frame error rate, which are a hundredfold reduction in simulations and around 99% in filed experiments. Our system is able to achieve around 700 kbps throughput.

A general approach to robust QR codes decoding

Jiamei Lv, Yuxuan Zhang, Wei Dong, Yi Gao and Chun Chen (Zhejiang University, China)

With the continued proliferation of smart mobile devices, Quick Response (QR) code has played an important role in daily life. They may be distorted and partially invisible due to bright spots, folding and stains When they are printed on soft materials such as plastic bags.
Existing scanners first locate QR codes by detecting special module patterns called finder patterns and acquire data in order by the color of each module. When QR codes are distorted or partial, they usually perform poorly. They may fail in detecting QR codes due to change of finder patterns. Besides, the data acquired may not be decoded because number of errors exceeds the correction capability caused by dislocation and invisibility of modules.
In this paper, we propose a simple but effective approach to decoding distorted and partial QR codes. First, we improve an existing QR code detection algorithm to extract QR codes.Then based on the structural features of QR codes that white and black modules are staggered, we propose a novel distortion correction mechanism that uses an adaptive window to match each module. In order to tackle the problem of invisibility, we print multiple QR codes and capture them in an image. Considering confidence of each module in separate, we reconstruct a relatively complete QR code.

BioDraw: Reliable Multi-Factor User Authentication with One Single Finger Swipe

Jianwei Liu (Zhejiang University & Xi'an Jiaotong University, China); Xiang Zou (Zhejiang University, China); Jinsong Han (Zhejiang University & Institute of Cyber Security Research, China); Feng Lin and Kui Ren (Zhejiang University, China)

Multi-factor user authentication (MFUA) becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing MFUAs require multiple interactions between users and different authentication components when sensing the multiple factors, leading to extra overhead and bad use experiences. In this paper, we propose a secure and user-friendly MFUA system, namely BioDraw, which utilizes four categories of biometrics (impedance, geometry, composition, and behavior) of human hand plus the pattern-based password to identify and authenticate users. A user only needs to draw a pattern on a RFID tag array, while four biometrics can be simultaneously collected. Particularly, we design a gradient-based pattern
recognition algorithm for pattern recognition and then a CNN-LSTM-based classifier for user recognition. Furthermore, to guarantee the systemic security, we propose a novel anti-spoofing scheme, called \textit{Binary ALOHA}, which utilizes the inhabit randomness of RFID systems. We perform extensive experiments over 21 volunteers. The experiment result demonstrates that BioDraw can achieve a high authentication accuracy (with a false reject rate less than 2%) and is effective in defending against various attacks.

Session Chair

Yanyong Zhang (USTC)

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