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)

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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)

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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)

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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)

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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)

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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)

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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)

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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)

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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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Industrial Talk

Improving QoE in the cloud native era — Issues in Edge Computing and Multi-Access Convergence

Conference
11:30 AM — 12:10 PM JST
Local
Jun 27 Sun, 10:30 PM — 11:10 PM EDT

Improving QoE in the cloud native era — Issues in Edge Computing and Multi-Access Convergence

Miya Kohno, Distinguished Systems Architect, Cisco Systems

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This talk does not have an abstract.

Session Chair

Miya Kohno, Distinguished Systems Architect, Cisco Systems

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Short Paper Session 4

Edge and Cloud

Conference
12:30 PM — 1:55 PM JST
Local
Jun 27 Sun, 11:30 PM — 12:55 AM EDT

Online Cloud Resource Provisioning Under Cost Budget for QoS Maximization

Yu Liu (Stony Brook University, USA); Niangjun Chen (Singapore University of Technology and Design, Singapore); Zhenhua Liu and Yuanyuan Yang (Stony Brook University, USA)

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Cloud computing is becoming one of the ubiquitous computing paradigms for enterprises and organizations in recent years. Due to the volatility of system states such as cloud resource price and workload demand, it is challenging to provision cloud resources efficiently. This paper studies online cloud resource provisioning problems under cost budget where no accurate or distributional future information is available. We develop an algorithmic framework and design online algorithms based on the framework. We prove the competitive ratio of the proposed algorithms. We further show the proposed algorithms have better performance than a prominent existing algorithm named CR-Pursuit. While prior works on the problem require the objective functions to be concave, the proposed algorithms work for non-convex and non-concave objective functions. We conduct real-world trace-driven simulations. Results highlight the proposed algorithms outperform baselines significantly over a wide range of settings.

FuzzySkyline: QoS-Aware Fuzzy Skyline Parking Recommendation Using Edge Traffic Facilities

Yinglong Li (Zhejiang University of Technolgy, China); Jiaye Zhang and Tieming Chen (Zhejiang University of Technology, China); Weiru Liu (University of Bristol, United Kingdom (Great Britain))

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Drivers always confront parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. Some of the existing literatures concentrate on multi-consideration optimization for parking decision by collecting the nearby real-time parking-related data. Others provide online parking navigation services through outsourced storage and cloud computing. Massive (raw) data transmission and complex processing are always involved in the existing methods, which results in undesired QoS such as real-time performance and privacy protection. In this paper, we propose a fuzzy skyline parking recommendation scheme for real-time parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from edge parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low data transmission, and quick response time with privacy protection.

Towards Robust Multi-Tenant Clouds Through Multi-Constrained VM Placement

Yutong Zhai, Gongming Zhao and Hongli Xu (University of Science and Technology of China, China); Yangming Zhao (University at Buffalo, USA); Jiawei Liu and Xingpeng Fan (University of Science and Technology of China, China)

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More and more tenants (enterprises and personal users) migrate their tasks to clouds since it is a simple and low-cost way to obtain enough computing resources. However, due to potential node failures and malicious tenants, the modern cloud encounters one critical challenge, i.e., robustness. Conventionally, the cloud vendors deploy auxiliary systems to protect the cloud, which requires additional resource cost and increases the network complexity. To enhance the system robustness, this paper proposes a complementary scheme to improve the cloud robustness through efficient VM placement. Specifically, to alleviate the impact of malicious tenants and node failures on the cloud, when deploying VMs, we limit the number of pods (or service nodes) that each tenant can access, and the number of tenants hosted by each pod (or service node). Though there are a lot of works on VM placement, it is very challenging when the robustness issue is taken into consideration. To solve this problem, we formulate an integer linear programming and propose a rounding-based algorithm with a logarithmic approximation ratio. Both the experimental results and simulation results show the high efficiency of the proposed algorithm. For example, our algorithm can improve the network throughput by 150% and reduce flow completion time by 25% compared with other alternatives.

Joint Resource Placement and Task Dispatching in Mobile Edge Computing across Timescales

Xinliang Wei and Yu Wang (Temple University, USA)

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The proliferation of Internet of Things (IoT) data and innovative mobile services has promoted an increasing need for low-latency access to resources such as data and computing services. Mobile edge computing has become an effective computing paradigm to meet the requirement for low-latency access by placing resources and dispatching tasks at the network edge near mobile users. The key challenge of such solution is how to efficiently place resources and dispatch tasks to meet the QoS of mobile users or maximize the platform's utility. In this paper, we study the joint optimization problem of resource placement and task dispatching in mobile edge computing across multiple timescales under the dynamic status of edge servers. We first propose a two-stage iterative algorithm to solve the joint optimization problem in different timescales, which can handle the varieties among the dynamic of edge resources and/or tasks. We then propose a reinforcement learning (RL) based algorithm which leverages the learning capability of Deep Deterministic Policy Gradient (DDPG) technique to tackle the network variation and dynamic as well. The results from trace-driven simulations demonstrate that our proposed approaches can effectively place resources and dispatching tasks across two timescales to maximize the total utility of all scheduled tasks.

Robustness-Aware Real-Time SFC Routing Update in Multi-Tenant Clouds

Huaqing Tu, Gongming Zhao and Hongli Xu (University of Science and Technology of China, China); Yangming Zhao (University at Buffalo, USA); Yutong Zhai (University of Science and Technology of China, China)

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In multi-tenant clouds, requests need to traverse a set of network functions (NFs) in a specific order, referred to as a service function chain (SFC), for security and business logic issues. Due to workload dynamics, the central controller of a multi-tenant cloud needs to frequently update the SFC routing, so as to optimize various network performance, such as load balancing. To achieve effective SFC routing update, we should consider two critical requirements: system robustness and real-time update. Without considering these two requirements, prior works either result in fragile clouds or suffer from large update delay. In this paper, we propose a robustness-aware real-time SFC routing update (R3-UA) scheme which takes both requirements into consideration. R3-UA pursues robustness-aware real-time routing update through two phases: robust NF instance assignment and real-time SFC routing update. Two algorithms with bounded approximation ratios are proposed for these two phases, respectively. We implement R3-UA on a real testbed. Both small-scale experimental results and large-scale simulation results show the superior performance of R3-UA compared with other alternatives.

Joint Service Placement for Maximizing the Social Welfare in Edge Federation

Sheng Chen and Baochao Chen (Tianjin University, China); Junjie Xie (National University of Defense Technology, China); Xiulong Liu, Deke Guo and Keqiu Li (Tianjin University, China)

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Mobile Edge Computing (MEC) is a promising cloud-network convergence paradigm which provides computational resources close to end devices at the network edge. There exist multiple Edge Infrastructure Providers (EIPs) in MEC which independently manage edges and provide services to customers. Due to the exponentially increasing data generated by end devices, it is almost impossible for a single EIP to accommodate offloaded data. Moreover, when considering that multiple EIPs provide services through federation, an urgent challenge is how to ensure the sustainability of federation. Most of the existing work improves the service provision capabilities of MEC by optimizing service placement without considering the existence of multiple EIPs. In this paper, we design the horizontal collaboration of edge federation, which integrates all edges of all EIPs. First, we model the service placement problem as a programming problem, towards the goal of maximizing social welfare. Then, we propose two dynamic pricing methods for EIPs to determine typical price for customers and insourcing price for other EIPs. The evaluation results based on two real-world data sets demonstrate that our proposed service placement model can increase the total gain of EIPs by up to 24.5% with a decrease of 35.5% in total delay.

Session Chair

Huaming Wu, Tianjin University, China

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Short Paper Session 5

Performance Modeling and Measurements

Conference
2:05 PM — 3:30 PM JST
Local
Jun 28 Mon, 1:05 AM — 2:30 AM EDT

A Reality-Conforming Approach for QoS Performance Analysis of AFDX in Cyber-Physical Avionics Systems

Boyang Zhou and Liang Cheng (Lehigh University, USA)

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AFDX (Avionics Full Duplex Switched Ethernet) is developed to support mission-critical communications while providing deterministic Quality of Service (QoS) across cyber-physical avionics systems in modern aircrafts such as Airbus A380 and Boeing 787. Currently, AFDX utilizes FP/FIFO QoS mechanism to guarantee its real-time performance and to better use the network resources. To analyze the real-time performance of avionic systems in their design processes, existing work analyzes the deterministic delay bound of AFDX using network calculus. However, existing analytical work is based on an unrealistic assumption leading to assumed worst cases that may not be achievable in reality. In this paper, we present a family of algorithms that can search for realistic worst-case delay scenarios in both preemptive and non-preemptive situations. Then we integrate the proposed algorithms with network calculus and apply our approach to analyzing tandem AFDX networks. Our reality-conforming approach yields tighter delay bound estimations than the state of the art. When there are 100 virtual links in AFDX networks, our reality-conforming method can provide delay bounds more than 25\% lower than those calculated by the state of the art in our evaluation. Moreover, when using our reality-conforming method in the design process, it leads to 27.2\% increase in the number of virtual links accommodated by the network. This enables better designs of AFDX networks for cyber-physical avionics systems to meet QoS requirements and offering a possible way to reduce network hardware footprint and thus saving aviation fuel cost.

Controlling the Maximum Link Estimation Error in Network Performance Tomography

Cuiying Feng (University of Victoria, Canada); Luning Wang (City University of Hong Kong, Hong Kong); Kui Wu (University of Victoria, Canada); Jianping Wang (City University of Hong Kong, Hong Kong)

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Network performance tomography uses a small number of strategically deployed monitors to infer the link performance in a large network. With the limited number of monitors, however, people usually can only estimate the bound rather than the exact values of network link performance. We aim at developing an effective solution to minimize the maximum error bound (MEB) over all the links in the network. To achieve this, we develop a method that theoretically guarantees (1) the minimum number of monitors required to bring down the MEB over all unidentifiable links, and (2) the best places where these new monitors should be deployed. Using this method repeatedly, we can push down the MEB gradually until the desired level is reached. In addition, we develop a new sequential measurement technique that reduces the number of measurement paths and in the meantime guarantees the tightest link error bound. With extensive simulation over real-world network topology, we demonstrate the effectiveness and robustness of our solution in reducing the maximum link error bound with network performance tomography.

Continuous Flow Measurement with SuperFlow

Zongyi Zhao and Xingang Shi (Tsinghua University, China); Arpit Gupta (UC Santa Barbara, USA); Qing Li (Southern University of Science and Technology, China); Zhiliang Wang, Bin Xiong and Xia Yin (Tsinghua University, China)

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Flow-based network measurement enables operators to perform a wide range of network management tasks in a scalable manner. Recently, various algorithms have been proposed for flow record collection at very high speed. However, they all focus on processing traffic in a short time window, but overlook the fact that flow measurements are typically needed continuously for unlimited time. To this end, we propose a new algorithm named SuperFlow to support continuous and accurate flow record collection at very high speed by monitoring the flow activeness and exporting the inactive records from the data plane automatically. Our data structures and the corresponding algorithms are carefully designed and analyzed, so the above goal is achieved with limited memory and bandwidth consumption. We implement SuperFlow on both x86 CPU and state-of-the-art PISA target. Comprehensive experiments show that SuperFlow consistently outperforms its competitors significantly. Especially, compared with the best competitor, it records around 136.7% more flows, reduces the error in flow size estimation by 51.5%, and reduces the memory or bandwidth consumption by up to 71.0%, while bringing only negligible throughput degradation.

Clean: Minimize Switch Queue Length via Transparent ECN-proxy in Campus Networks

Xiaojie Huang, Jiaqing Dong, Wenzheng Yang and Chen Tian (Nanjing University, China); Jun Zhou, Yi Kai and Mingjie Cai (Huawei Technologies, China); Nai Xia, Wanchun Dou and Guihai Chen (Nanjing University, China)

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Campus networks are widely deployed for organizations like universities and large companies. Applications and network-based services require campus networks to guarantee short queue and provide low latency and large bandwidth. However, the widely adopted packet-loss-based congestion control mechanism in client hosts builds up long queues in the switch buffer, which is prone to packet loss in burst scenarios, resulting in great network delay. Therefore, a scheme for efficiently controlling queue length of shallow buffer switches in campus networks is urgently needed. Explicit Congestion Notification(ECN) as an explicit feedback mechanism is widely adopted in data center networks to build lossless networks. In this paper, we propose CLEAN, an efficient queue length control scheme based on transparent ECN-proxy for campus networks. CLEAN is able to exert fine-grained control over arbitrary client TCP stacks by enforcing per-flow congestion control in the access point(AP). It allows the campus network switches to maintain a low queue length, resulting in high throughput, low latency and zero packet loss. Evaluation results demonstrate that CLEAN reduces the maximum queue length of the switch by 86% and reduces the 99th percentile latency by 85%. CLEAN also achieves zero packet loss in burst scenarios.

Scalable Hardware Content Router: Architecture, Modeling and Performance

Bin Liu, Huichen Dai, Wenquan Xu, Tong Yun and Ji Miao (Tsinghua University, China)

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Current Internet is evolving with the gradual shift from the traditional host-to-host communication model to the new host-to-content paradigm, which will eventually lead to a network of caches. The novel Named Data Networking (NDN) has been proposed as a future Internet architecture to embrace this paradigmatic shift, where caching becomes an ubiquitous functionality available at each router.

A router with the functionality of content caching, running on NDN mechanisms, is termed as an NDN-based content router. Previous researchers focused on software content routers (SCR), which leverage a commercial off-the-shelf computer to execute content caching/accessing and named-based packet forwarding. SCR can only achieve limited throughput, which is far below the speed requirements of modern routers. Facing this situation, in this paper, we propose a hardware-based content router (HCR), aiming at purchasing wire-speed processing. We design a physically concise architecture for decoupling the packet buffers in line cards from the content caches attached to storage cards, enabling separate management and optimization while facilitating a modular structure for smooth capacity upgrade in response to increasing storage utilization. For lowering the operating complexity and reducing the storage management cost, we choose to employ distributed caches working in a cooperated manner by using consistent hashing. We model several candidate storage organizing schemes and carry out theoretical analyses for comparison. Analytical and synthetic workload-driven results show that the consistent hashing scheme achieves high cache performance and low cost simultaneously. Moreover, caching policy on a single content cache is also improved based on a "low-pass filter" observation, which is verified with real HTTP traces and it effectively promotes the cache hit probability compared with the widely Least Recently Used (LRU) mechanism, while keeping the implementation complexity at the low level.

FlexNF: Flexible Network Function Orchestration on the Programmable Data Plane

Hanyu Zhao (Tsinghua University, China); Qing Li and Jingpu Duan (Southern University of Science and Technology, China); Yong Jiang (Graduate School at Shenzhen, Tsinghua University, China); Kai Liu (Pengcheng Laboratory, Shenzhen, China)

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Recently, Programmable Data Plane (PDP) has been leveraged to offload Network Functions (NFs). Due to its high processing capabilities, programmable data plane can improve the performance of NFs to more than one order of magnitude. However, the coarse-grained NF orchestration granularity on the PDP makes it hard to fulfill the dynamic service chain demands.
In this paper, we propose the Flexible Network Function (FlexNF) Deployment on the programmable data plane. We first design an NF Selection Framework which leverages labels and the pipeline re-enter operation to support Selective Serving Mechanism for flexible NF orchestration. We then design a two-stage service path construction algorithm to provide on-path service based on SSM with load balancing taken into account. We implement 7 types of real network functions in the commodity P4 switch, based which we construct the comprehensive experiments. The results show that FlexNF can reduce the traffic routing delay by about 42.6% while increasing service chain acceptance rate by 5 times compared with current solutions.

Session Chair

Yuchao Zhang, Beijing University of Posts and Telecommunications, China

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Short Paper Session 6

Wireless, Scheduling, and Security

Conference
3:40 PM — 5:20 PM JST
Local
Jun 28 Mon, 2:40 AM — 4:20 AM EDT

Designing Approximate and Deployable SRPT Scheduler: A Unified Framework

Zhiyuan Wang (The Chinese University of Hong Kong, Hong Kong); Jiancheng Ye, Dong Lin and Yipei Chen (Huawei, Hong Kong); John C.S. Lui (The Chinese University of Hong Kong, Hong Kong)

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The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT and slowdown in the M/G/1 setting. We formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. To prevent the starvation of long messages, we also take into account the fairness condition based on the steady-state slowdown. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. We carry out extensive flow-level simulations and packet-level experiments to evaluate the proposed optimal ADS design. Results show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT in terms of MCT and slowdown.

Secure and Efficient Task Matching with Multi-keyword in Multi-requester and Multi-worker Crowdsourcing

Kan Yang and Senjuti Dutta (University of Memphis, USA)

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Crowdsourcing enables users (task requesters) to outsource complex tasks to an unspecified crowd of workers. To guarantee the quality of crowdsourcing service, it is necessary to select the most appropriate task workers to complete the tasks. To this end, the crowdsourcing platform (broker) must conduct the mutual matching between tasks and workers based on the task requirements and worker preferences. However, both task requirements and worker preferences may contain sensitive information (e.g., time, location of the task, etc.), which should not be revealed to the broker and other adversaries. In this paper, we propose a secure and efficient task matching scheme to enable the broker to conduct the mutual matching between tasks and workers, according to task requirements and worker preferences with multiple keywords, while preserving the privacy of keywords contained in task requirements and worker preferences. Specifically, we design a new multi-reader and multi-writer searchable encryption primitive that can support the batch matching of multiple keywords with constant size. In addition, we also design a random masking technique to hide the information that whether a keyword is required by a task. The security proof shows that our proposed task matching scheme is provably secure in the random oracle model under the Bilinear Diffie-Hellman (BDH) assumption. The performance evaluation shows that our multi-keyword batch matching can significantly reduce the communication overhead and computation cost compared to existing methods.

pSAV: A Practical and Decentralized Inter-AS Source Address Validation Service Framework

Jiamin Cao, Ying Liu, Mingxing Liu and Lin He (Tsinghua University, China); Yihao Jia (Huawei Technologies Co., Ltd., China); Fei Yang (Beijing Huawei Digital Technologies Co., Ltd., China)

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Source IP address spoofing has been a major vulnerability of the Internet for many years. Although much work has been done to study the problem extensively, spoofing continues to occur frequently and has led to many serious network attacks. Inter-AS source address validation (SAV) is considered an important defense method for AS to filter spoofed packets. However, existing work has been unable to drive inter- AS SAV deployment into practice due to the lack of deployment incentives and trust foundation. In this paper, we propose a practical and decentralized inter-AS SAV service framework, pSAV, to promote inter-AS SAV deployment. pSAV increases deployment incentives by treating SAV as a payable service and dividing the participant ASes into service subscribers, providers, and auditors. On the control plane, pSAV leverages blockchain as a trust foundation to provide service subscriptions and audits with automatic incentive allocation. On the data plane, pSAV leverages P4-programmable switches to provide flexible and high-performance SAV services. We prototype the pSAV control plane based on Hyperledger Fabric and implement various SAV techniques on Barefoot Tofino switches. The evaluation results show that (1) on the control plane, pSAV blockchain can provide high-performance service transactions (hundreds of transactions per second with second latency), and (2) on the data plane, pSAV can provide various high-throughput (hundreds of Gbps) SAV services using only one programmable switch.

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies

Shizhen Zhao, Xiao Zhang and Peirui Cao (Shanghai Jiao Tong University, China); Xinbing Wang (Shanghai Jiaotong University, China)

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We study how to design edge server placement and server scheduling policies under workload uncertainty for 5G networks. We introduce a new metric called resource pooling factor to handle unexpected workload bursts. Maximizing this metric offers a strong enhancement on top of robust optimization against workload uncertainty. Using both real traces and synthetic traces, we show that the proposed server placement and server scheduling policies not only demonstrate better robustness against workload uncertainty than existing approaches, but also significantly reduce the cost of service providers. Specifically, in order to achieve close-to-zero workload rejection rate, the proposed server placement policy reduces the number of required edge servers by about 25% compared with the state-of-the-art approach; the proposed server scheduling policy reduces the energy consumption of edge servers by about 13% without causing much impact on the service quality.

To share or not to share: reliability assurance via redundant cellular connectivity in Connected Cars

Emeka Obiodu (King's College London, United Kingdom (Great Britain)); Abdullahi Abubakar (University of Surrey, United Kingdom (Great Britain)); Aravindh Raman (Telefonica I+D, Spain); Nishanth Sastry (University of Surrey, United Kingdom (Great Britain)); Simone Mangiante (Vodafone, United Kingdom (Great Britain))

0
Adoption of connected cars (CCs) is growing across society with Analyses Mason projecting there will be over 831 million of them globally by 2027. Most CCs currently rely on 2G/3G/4G networks, but the expectation is that 5G will better support safety-critical vehicle-to-everything (V2X) use cases. While regulations mandate back-up for some services (e.g. e-Call in Europe), operationally, most relationships between cellular network providers and car manufacturers or users are exclusive, providing a single network connectivity, with at best an occasional option of a back-up plan if the single network is unavailable. We question if this setup can provide QoS assurance for V2X use cases. Accordingly, in this paper, we investigate the role of redundancy in providing QoS assurance for cellular connectivity for CCs. Using our bespoke Android measurement app, we did a drive-through test on 380 kilometers of major and minor roads in South East England. We measured round trip times, jitter, page load times, packet loss, network type, uplink speed and downlink speeds on the four UK networks for 14 UK-centric websites every five minutes. In addition, we did the same measurement using a much more expensive universal SIM card provider that promises to fall back on any of the four UK networks to assure reliability. By comparing actual performance on the best performing network versus the universal SIM, and then projected performance of a two/three/four multi-operator setup, we make three major contributions. First, and most importantly, the use of redundant multi-connectivity, especially if managed by the demand-side, will deliver superior performance (up to 28 percentage points in some cases). Second, despite costing 95x more per GB of data, the universal SIM performed worse than the best performing network except for uplink speed, highlighting how the choice of parameter to monitor can influence operational decisions. Third, any assessment of CC connectivity reliability based on availability is sub-optimal as it can hide significant under-performance.

ModelCoder: A Fault Model based Automatic Root Cause Localization Framework for Microservice Systems

Yang Cai and Biao Han (National University of Defense Technology, China); Jie Li (Shanghai Jiao Tong University, Japan); Na Zhao (National University of Defense Technology, China); Jinshu Su (National University of Defence Technology, China)

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Microservice system is an architectural style to develop a single application as a suite of small services running in its process and communicating with lightweight message mechanisms. Although microservice architecture enables rapid, frequent and reliable delivery of large, complex applications, it is increasingly challenging for operational staffs to locate the root cause of a microservice fault, which usually occurs on a service node and propagates to affect the entire system. To this end, in this paper, we first introduce the concept of deployment graph and service dependency graph to depict the deployment status and calling relationship between service nodes. Then we formulate the root cause localization problem in microservice systems based on the constructed graphs, in which fault model is defined to capture the characteristics of a fault's root cause. A fault model based automatic root cause localization framework called ModelCoder is later developed to figure out the root cause of unknown faults by comparing with the predefined fault models. We evaluate ModelCoder on a real-world microservice system monitoring data set spanning 15 days. Through extensive experiments, it is revealed that ModelCoder can localize the fault root cause nodes within 80 seconds on average and improve the root cause localization accuracy (to 93%) by 12% compared with the state-of-the-art root cause localization algorithm.

QoE-assured Live Video Streaming Based on Coalition Game in 5G eMBMS Networks

Xiaobin Tan and Simin Li (University of Science and Technology of China, China); Yangyang Liu (University of Science & Technology of China, China); Quan Zheng and Dezheng Liu (University of Science and Technology of China, China)

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The scenario that quantities of users subscribing to a same live video content cluster together in a spatially local area poses challenges to cellular operators even in 5G unicast networks. In this regard, we propose a network paradigm exploiting eMBMS and edge computing, which relieves resource starvation in both backbone and wired access networks by grouping users and distributing the desired content to each multicast group only once. However, problems are raised by operators to perform an optimal server-side decision: how to partition users with the heterogeneity and dynamic of channel conditions, how to fairly and optimally allot resources considering both unicast and multicast users, and how to maximize the overall QoE of this live video service. To cope with these coupled problems, we formulate an optimization model based on coalition game with QoE assured, which defines a fair allocation strategy according to respective contributions and a dynamic grouping method. Subsequently, we propose a heuristic algorithm with a low-time complexity that guarantees QoE for most users and shows conspicuous reduction of annoying stalling events. Noticeably, numerical simulations reveal the fairness and near-optimality of our algorithm compared with state-of-the-art approaches in multiple scenarios.

Session Chair

Peng Li, University of Aizu, Japan

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Closing Session

Closing Session

Conference
5:30 PM — 5:40 PM JST
Local
Jun 28 Mon, 4:30 AM — 4:40 AM EDT

Session Chair

Ruidong Li, Kanazawa University, Japan

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