Technical Sessions

Session 15

Mobile and Wireless Networks

Conference
9:00 AM — 10:10 AM JST
Local
Jun 27 Sun, 8:00 PM — 9:10 PM EDT

CUP: Cellular Ultra-light Probe-based Available Bandwidth Estimation

Lixing Song (Rose-Hulman Institute of Technology, USA); Emir Halepovic (AT&T Labs - Research, USA); Alamin Mohammed and Aaron D Striegel (University of Notre Dame, USA)

0
Cellular networks provide an essential connectivity foundation for a sizable number of mobile devices and applications, making it compelling to measure their performance in regard to user experience. Although cellular infrastructure provides low-level mechanisms for network-specific performance measurements, there is still a distinct gap in discerning the actual application-level or user-perceivable performance from such methods. Put simply, there is little substitute for direct sampling and testing to measure end-to-end performance. Unfortunately, most existing technologies often fall quite short. Achievable Throughput tests use bulk TCP downloads to provide an accurate but costly (time, bandwidth, energy) view of network performance. Conversely, Available Bandwidth techniques offer improved speed and low cost but are woefully inaccurate when faced with the typical dynamics of cellular networks. In this paper, we propose CUP, a novel approach for Cellular Ultra-light Probe-based available bandwidth estimation that seeks to operate at the cost point of available Bandwidth techniques while correcting accuracy issues by leveraging the intrinsic aggregation properties of cellular scheduling, coupled with intelligent packet timing trains and the application of Bayesian probabilistic analysis. By keeping the costs low with reasonable accuracy, our approach enables scaling both with respect to time (longitude) and space (UE density). We construct a CUP prototype to evaluate our approach under various demanding real-world cellular environments (longitudinal, driving, multiple vendors) to demonstrate the efficacy of our approach.

Charging on the Move: Scheduling Static Chargers with Tunable Power for Mobile Devices

Tao Wu (National University of Defense Technology, China); Panlong Yang (University of Science and Technology of China, China); Haipeng Dai (Nanjing University & State Key Laboratory for Novel Software Technology, China)

0
The breakthrough of Wireless Power Transfer technique provides a promising paradigm to tackle the energy limitation problem for end-devices. It helps replenish energy of sensors wirelessly and prolong the network lifetime without the need of replacing battery. In this paper, we are concerned with the issue of \underline{C}harging on the \underline{M}ove (CM) and propose a reliable scheme, that is, given a fixed number of chargers with flexible power level and a set of mobile sensors on the plane, scheduling the transmitting power of charges to maximize the overall charging utility, subject to the power budget. To address CM, we approximate the variational charging power as piecewise constant power, and divide the movement trajectories with approximated charging utility. Then, we first consider our problem with fixed power level, where each charger can be scheduled off or on at a fixed power level. We prove the submodularity of the objective function and design a .. approximation algorithm. On this basis, we further bound the performance loss during the problem reformulation, and finally propose a .. approximation algorithm for flexible scheduling strategy. Extensive simulations and trace-driven evaluations are conducted to evaluate the performance of our proposed algorithm.

Stateful-BBR - An Enhanced TCP for Emerging High-Bandwidth Mobile Networks

Lingfeng Guo (The Chinese University of Hong Kong, Hong Kong); Yan Liu and Wenzheng Yang (Tencent, China); Yuming Zhang and Jack Y. B. Lee (The Chinese University of Hong Kong, Hong Kong)

0
With the progressive deployment of 5G networks around the world, mobile networks are entering a new era where bandwidth will be breaking through the Gbps barrier. In this work, we investigate the performance of current TCP designs in such high-bandwidth networks, demonstrating the potential bottleneck due to TCP's Slow-Start mechanism which is an integral component in most TCP designs. For example, transferring a file of 1 MB size in a first-generation 5G network using Linux's default TCP-Cubic and Google's TCP-BBR resulted in average throughputs of 36.9 Mbps and 37.8 Mbps, respectively. Compared to the mean available bandwidth of 180 Mbps, the gap is significant. To tackle this problem, we developed an enhanced Stateful-TCP technique to transform BBR into a new S-BBR to accelerate its startup performance to narrow the gap. Results from trace-driven emulated 5G network experiments show that S-BBR could improve BBR's throughput performance by 50% to 100% while maintaining similar delay performance. This is further validated by an independent competitive benchmark using over 500 clients where S-BBR raised BBR's throughput by 69%. S-BBR is sender-based and thus can be readily deployed, it retains BBR's desirable features and so offers a promising solution to enhance mobile applications' performance in the emerging high-bandwidth mobile and wireless networks.

IMP: Impedance Matching Enhanced Power-Delivered-to-Load Optimization for Magnetic MIMO Wireless Power Transfer System

Wangqiu Zhou, Hao Zhou and Wenxiong Hua (University of Science and Technology of China, China); Fengyu Zhou (University of Science and Technology of China, China); Xiang Cui (University of Science and Technology of China, China); Suhua Tang and Zhi Liu (The University of Electro-Communications, Japan); Xiang-Yang Li (University of Science and Technology of China, China)

0
Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching enhanced PDL optimization in MIMO MRC-WPT systems (called IMP), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping, and solve them through alternating direction method of multipliers (ADMM) based, tabu search (TS) based, and graph clique cover based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency (PTE) maximization solution, the proposed algorithm IMP achieves a 74.7X performance improvement of PDL on average.

Session Chair

Dan Wang, The Hong Kong Polytechnic University, Hongkong

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Session 16

Data & Processing

Conference
10:20 AM — 11:30 AM JST
Local
Jun 27 Sun, 9:20 PM — 10:30 PM EDT

FairCrowd: Fair Human Face Dataset Sampling via Batch-Level Crowdsourcing Bias Inference

Ziyi Kou, Yang Zhang, Lanyu Shang and Dong Wang (University of Notre Dame, USA)

0
Human face image is a large category of visual information utilized by various human facial data services (e.g., face recognition, face generation, face attribute prediction). However, the quality of data services (QoDS) on human face datasets is usually biased towards the majority demographic group due to the data imbalance issue. In this paper, we focus on a fair human face dataset sampling problem where the goal is to sample a sub-dataset from the original dataset to reduce its bias by leveraging crowd intelligence to infer the demographic labels of face images (e.g., male or female, old or young). Our problem is motivated by the limitations of current fair data sampling solutions that require pre-annotated demographic labels to sample a fair dataset. Two important challenges exist in solving our problem: 1) it is extremely time-consuming and expensive to assign crowd workers to annotate demographic labels of all images in a large-scale facial dataset; 2) it is not a trivial task to improve the fairness of the sampled sub-dataset (with fewer data samples) without sacrificing the accuracy performance of data services on such dataset. To address the above challenges, we develop FairCrowd, a fair crowdsourcing-based data sampling framework that leverages an efficient batch-level demographic label inference model and a joint fair-accuracy-aware data shuffling method. We evaluate the performance of FairCrowd through a large-scale real-world face image dataset that consists of celebrity faces from a diversified set of demographic groups. The results show that FairCrowd not only reduces demographic bias but also improves the accuracy ofdata services trained on the sub-dataset generated by FairCrowd, leading to a more desirable QoDS of the application.

When Virtual Network Operator Meets E-Commerce Platform: Advertising via Data Reward

Qi Cheng and Hangguan Shan (Zhejiang University, China); Weihua Zhuang (University of Waterloo, Canada); Tony Q. S. Quek (Singapore University of Technology and Design, Singapore); Zhaoyang Zhang (Zhejiang University, China)

0
In China, some e-commerce platform (EP) companies such as Alibaba and JD have been now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players after EP companies being VNOs through appropriate integration of the VNO business and the companies' own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage Stackelberg game. In Stage I, the NO decides the price of mobile data for the VNO; in Stage II, the VNO decides its data plan fee for MUs and the ad price for e-commerce merchants (EMs); in Stage III, the MUs make their own decisions on the data plan subscription and the number of ads to be watched, while the EMs decide the number of ad slots they buy from the EP. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Simulation results show the impact of the system parameters on the utilities of game players and social welfare, and reveal that the solution can indeed lead to a quadri-win outcome in some cases. At the same time, we summarize some insights that have some economic guidance.

FedACS: Federated Skewness Analytics in Heterogeneous Decentralized Data Environments

Zibo Wang and Yifei Zhu (Shanghai Jiao Tong University, China); Dan Wang (The Hong Kong Polytechnic University, Hong Kong); Zhu Han (University of Houston, USA)

0
The emerging federated optimization paradigm performs data mining or artificial intelligence techniques locally on the edge devices, enabling scientists and engineers to utilize the blooming edge data with privacy protection. In such a paradigm, since data cannot be shared or gathered, data heterogeneity naturally emerges, which significantly degrades the performance of federated optimization, ultimately leading to poor quality of federated services. In this paper, we present the first work on characterizing the data heterogeneity in the framework of federated analytics, i.e., to collectively carry out analytics tasks without raw data sharing, and use the information to create a desirable data environment via intelligent client selection. Our proposed Analytics-driven Client Selection framework, named FedACS, tackles the data heterogeneity problem in three steps. First, clients are in charge of generating insights about local data without disclosure of sensitive information. Then, the server uses the insights to infer the situation of clients' data heterogeneity based on the Hoeffding's inequality. Finally, a dueling bandit is formulated to intelligently select clients with slighter data heterogeneity to participate in federated optimization tasks. FedACS can be universally applied to all kinds of federated optimization tasks, and gains benefits including privacy protection, infrastructure reuse, and client load reduction. To test its efficiency, we further customize it to assist federated learning, a popular scenario of federated optimization. According to experiment results, FedACS reduces the accuracy degrading by up to 65.6%, and speeds up the convergence for up to 2.4 times.

GraphCP: An I/O-Efficient Concurrent Graph Processing Framework

Xianghao Xu (Huazhong University of Science and Technology, China); Fang Wang (Wuhan National Laboratory for Optoelectronics, China); Hong Jiang (University of Texas at Arlington, USA); Yongli Cheng (FuZhou University, China); Dan Feng (Huazhong University of Science and Technology, China); Yongxuan Zhang (Huazhong University of Science & Technology, China); Peng Fang (Huazhong University of Science and Technology, China)

0
Big data applications increasingly rely on the analysis of large graphs. In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing outof- core graph processing systems usually incur redundant data accesses and storage and severe competition of I/O bandwidth when handling the CGP jobs, thus leading to very long waiting time experienced by users for the computing results. In this paper, we propose an I/O-efficient out-of-core graph processing system, GraphCP, to support the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model that shares the I/O access and processing of graph data among the CGP jobs and adaptively schedules the loading of graph data, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 10.3x and 4.6x faster than two state-of-the-art out-of-core graph processing systems GridGraph and GraphZ respectively, 2.1x faster than a CGPoriented graph processing system Seraph.

Session Chair

Hanhua Chen, Huazhong University of Science and Technology, China

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