Session Poster-Session-1

Poster Session 1

8:00 PM — 10:00 PM EDT
Jul 7 Tue, 8:00 PM — 10:00 PM EDT

Near Optimal Network-wide Per-Flow Measurement

Ran Ben Basat (Harvard University, USA); Gil Einziger (Ben-Gurion University Of The Negev, Israel); Bilal Tayh (Ben Gurion University of the Negev, Israel)

Network-wide flow measurements are a fundamental building block for applications such as identifying attacks, detecting load imbalance, and performing traffic engineering. Due to the rapid line rates, flow measurements use the fast SRAM memory that is too small to monitor all flows but the existing methods make sub-optimal memory use or rely on strong assumptions about the traffic. Our work introduces novel cooperative flow monitoring algorithms that achieve near-optimal flow coverage without strong assumptions.

Scalable and Interactive Simulation for IoT Applications with TinySim

Gonglong Chen, Wei Dong, Fujian Qiu, Gaoyang Guan, Yi Gao and Siyu Zeng (Zhejiang University, China)

Modern IoT applications are characterized by three important features, i.e., the device heterogeneity, the long-range communication and the cloud-device integration. The above features cause difficulties for IoT application developers in predicting and evaluating the performance of the entire system. To tackle the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and high interactivity. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the amount of simulation events, greatly speeding up the simulation process. TinySim can simulate representative IoT applications such as smart flower spot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4,000 devices within 11.2 physical-minutes for 10 simulation-minutes, which is about 3× faster than the state-of-art approach

Parallel VM Placement with Provable Guarantees

Itamar Cohen (Ben-Gurion University of the Negev, Israel); Gil Einziger (Ben-Gurion University Of The Negev, Israel); Maayan Goldstein and Yaniv Sa'ar (Nokia Bell Labs, Israel); Gabriel Scalosub (Ben-Gurion University of the Negev, Israel); Erez Waisbard (Nokia Bell Labs, Israel)

Efficient on-demand deployment of VMs is at the core of cloud infrastructure but the existing resource management approaches are too slow to fulfill this promise. Parallel resource management is a promising direction for boosting performance, but when applied naively, it significantly increases the communication overhead and the decline ratio of deployment attempts. We propose a new dynamic and randomized algorithm, APSR, for parallel assignment of VMs to hosts in a cloud environment. APSR is guaranteed to satisfy an SLA containing decline ratio constraints, and communication overheads constraints. Furthermore, via extensive simulations, we show that APSR obtains a higher throughput than other commonly employed policies (including those used in OpenStack) while achieving a reduction of up to 13x in decline ratio and a reduction of over 85% in communication overheads.

Measurement and Analysis of Cloud User Interest: A Glance From BitTorrent

Lei Ding (University of Alberta, Canada); Yang Li (University of Minnesota Duluth, USA); Haiyang Wang (University of Minnesota at Duluth, USA); Ke Xu (Tsinghua University, China)

Cloud computing has recently emerged as a compelling method for deploying and delivering services over the Internet. In this paper, we aim to shed new light on the learning of cloud user interest. Our study for the first time shows the existence of cloud users in such real-world content distribution systems as BitTorrent. Based on this observation, we further explore the similarity of content preferences between cloud and non-cloud users. Surprisingly, our statistical model analysis indicates that the users in the cloud AS have significantly different interests from all the observed non-cloud ASes. More dedicated researches are therefore required to better manage this elevating yet unique cloud traffic in the future.

Connection-based Pricing for IoT Devices: Motivation, Comparison, and Potential Problems

Yi Zhao (Tsinghua University, China); Wei Bao (The University of Sydney, Australia); Dan Wang (The Hong Kong Polytechnic University, Hong Kong); Ke Xu (Tsinghua University, China); Liang Lv (Tsinghua, China)

Most existing data plans are data volume oriented. However, due to the small data volume from Internet of Things (IoT) devices, these plans cannot bring satisfactory monetary benefits to ISPs, but the frequent data transmission introduces substantial overhead. ISPs, such as China Telecom, propose novel data plans for IoT devices that charge users based on their total number of connections per month. How does such model differ from the current volume-oriented (VO) charging models, that is, will this bring benefit to ISPs, how does this affect the users and the network ecosystem as a whole? In this paper, we answer these questions by developing a model for connection-based pricing, i.e., frequency-oriented (FO) plans. We first discuss the motivation of connection-based pricing and formally develop the model. We then compare connection-based pricing with volume-oriented pricing. Based on such results, we predict that there may be potential problems in the future, and connection-based pricing calls for further study.

Always Heading for the Peak: Learning to Route with Domain Knowledge

Jing Chen, Zili Meng and Mingwei Xu (Tsinghua University, China)

Recently, learning-based methods have been applied to routing optimization to achieve both high performance and high efficiency. However, existing solutions rarely address the challenge of making routing decisions under constraints, which drastically degrades the performance in real-world topologies. In this poster, inspired by the hill-climbing process, we introduce a new decision variable, altitude, to guide the flows towards destinations. Our approach could efficiently meet the constraints in the routing optimization with this improved expression of the routing strategy. Our preliminary results show that our approach reduces maximum-link-utilization by up to 29% compared with heuristics in real-world topologies.

Session Chair

Xiaowen Gong (Auburn University)

Session Poster-Session-2

Poster Session 2

8:00 PM — 10:00 PM EDT
Jul 7 Tue, 8:00 PM — 10:00 PM EDT

Joint Optimization of Service Function Chain Elastic Scaling and Routing

Weihan Chen and Zhiliang Wang (Tsinghua University, China); Han Zhang (Beihang University, China); Xia Yin and Xingang Shi (Tsinghua University, China); Lei Sun (Lenovo, China)

The main problem of current Virtualization Network Function (VNF) elastic scaling mechanism is that it does not consider the routing cost variation of the whole Service Function Chain (SFC) after scaling operation. In some network environment, the routing cost may increase considerably. In this poster, we propose a SFC elastic scaling algorithm with routing optimization to reduce additional routing cost caused by scaling operation. The main idea is to force the scaling of VNF, which does not require scaling operations, to optimize the routing cost of traffic forwarding paths. The simulation results indicated that the proposed algorithm can reduce 27% total cost compared with traditional scaling algorithm.

An Unsupervised Two-Layer Multi-Step Network Attack Detector

Su Wang, Zhiliang Wang, Xia Yin and Xingang Shi (Tsinghua University, China)

Nowadays, attackers tend to perform several steps to complete a cyber attack named multi-step network attack which is different from the traditional network attack. Plenty of studies carried on multi-step attack detection use rule-based intrusion detection system (IDS) alerts as source while rule-based IDS relies heavily on its rule set. It is hard for IDS rule set to detect every anomaly behavior and once some attack steps do not cause alert, the subsequent multi-step attack detection will be affected. In this poster, we present a novel unsupervised two-layer multi-step attack detector. In the first layer, we propose Dynamic Threshold Time Decay Frequent Item Mining to detect those steps IDS cannot generate alert and in the second layer, we utilize Heuristic Alarm Clustering method to detect the multi-step attack scenario. The results of evaluation on IDS2012 dataset show that our detector can significantly reduce the false negative rate (FNR) of Suricata IDS.

Towards Ambient Backscatter as an Anti-jamming Solution for IoT Devices

Wonwoo Jang and Wonjun Lee (Korea University, Korea (South))

Reliable communication for Internet of Things is challenging when strong jamming signals are transmitted. The difficulty lies in the fact that in order to enable reliable communication, the system needs to create a communication channel beyond jamming signals. In this paper, we propose a first jamming resilient technique by switching the channel from Wi-Fi to ambient backscatter. Our vision is that ambient backscatter makes the transmitter to be jamming resilient through frequency shifting and modulation to existing signals. The proposed technique is effective than other anti-jamming solutions in that it reduces frame error rate more than 70% despite the existence of jamming signals.

Poster Abstract: An Open Source Approach to Field Testing of WLAN up to IEEE 802.11ad at 60 GHz Using Commodity Hardware

Florian Klingler, Fynn Hauptmeier and Christoph Sommer (Paderborn University, Germany); Falko Dressler (TU Berlin, Germany)

We present a methodology for flexible field testing supporting WLAN including the most recent IEEE 802.11ad standard operating in the 60 GHz frequency band. The system requires only minimal interaction from the user side to gather a wide range of key performance metrics such as received signal strength, communication delay, and goodput. Our implementation is based on OpenWrt and can be deployed on a wide range of commodity hardware, down to the two-digit price range, allowing large scale field tests of novel applications. As a proof-of-concept, we used the TP-LINK Talon AD7200 Wireless Routers for indoor experiments at 60 GHz. We see our Open Source implementation as a reference for a huge variety of large scale experimentation.

IRS Assisted Multiple User Detection for Uplink URLLC Non-Orthogonal Multiple Access

Lei Feng (Beijing University of Posts and Telecommunications, China); Xiaoyu Que (Beijing Univertsity of Posts and Telecommunications, China); Peng Yu and Li Wenjing (Beijing University of Posts and Telecommunications, China); Qiu Xuesong (Beijing University of Posts & Telecommunications (BUPT), China)

Intelligent reflecting surface (IRS) has been recognized as a cost-effective technology to enhance spectrum and energy efficiency in the next generation (5G) wireless communication networks, which is expected to support stable transmission for ultra reliable and low latency communications (URLLC). This paper focuses on the usage of IRS in uplink URLLC system and proposes a compressive sensing based IRS assisted multiple user detection method to deal with the sparsity and relativity characteristic of user signal in URLLC system. Simulation results demonstrate that our proposed algorithm achieves better performance than that of other MUD algorithms with similar computational complexity in terms of reliability and low latency.

Poster Abstract: Environment-Independent Electronic Device Detection using Millimeter-Wave

Yeming Li (Zhejiang University EmNets Group, China); Wei Dong and Yuxiang Lin (Zhejiang University, China)

As the volume of electronic devices tends to be miniaturized, covert electronic devices (e.g., spy camera, tiny bomb initiator) have played an important role in some malicious attacks. However, there is no method that can effectively detect covert electronic devices yet. To this end, we proposed a millimeter-wave based electronic detection system. Due to the existence of a large number of nonlinear components (e.g., diodes, capacitors) in electronic devices, when an electronic device is irradiated with RF signals, it will reflect a special signal containing the characteristics of the electronic device. This signal is called a nonlinear response. We collect the nonlinear response signals of electronic devices in three different environments using a commercial mmWave radar. After that, we use wavelet-transform and power normalization to preprocess the raw data. Finally, we apply domain-adaptation neural network to extract environment-independent features and determine the existence of electronic devices. Results show that our system can achieve high-precision detection and its recognition accuracy reaches 99.61% in lab environment and 96.41% in different environments.

Session Chair

Zhangyu Guan (University at Buffalo)

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