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2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)最新文献

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Authentication of Multi-Dimensional Top-$K$ Query on Untrusted Server 不可信服务器上多维Top-$K$查询的身份验证
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624145
Xiaoyu Zhu, Jie Wu, Wei Chang, Guojun Wang, Qin Liu
Consider a database where each record has multiple attributes. An untrusted server is in charge of processing queries over this database, and we want to provide a mechanism for users to verify the correctness of their query results. Here each query, referred to as a multi-dimensional top-$k$ query, retrieves $k$ records whose output with user-supplied ranking function is among top $k$. Multi-dimensional top-$k$ query is widely used in real applications. However, as the traditional query authentication methods cannot be directly deployed on multi-dimensional top-$k$ query, it is still a challenging problem to authenticate the multi-dimensional top-$k$ query results. In this paper, we propose an authentication solution to support multi-dimensional top-$k$ query based on signature chain. By using signature chain for each record and its successors on each dimension, our solution allows users to efficiently verify the soundness and completeness of multi-dimensional top-$k$ query results. Through theoretical analysis and simulation, we demonstrate the effectiveness of our proposed solution.
考虑一个数据库,其中每条记录都有多个属性。一个不受信任的服务器负责处理该数据库上的查询,我们希望为用户提供一种机制来验证其查询结果的正确性。这里的每个查询(称为多维top- k查询)检索k条记录,这些记录的输出(使用用户提供的排序函数)位于top- k之间。多维top- k查询在实际应用中得到了广泛的应用。然而,由于传统的查询认证方法不能直接部署在多维top- k -$查询上,因此对多维top- k -$查询结果的认证仍然是一个具有挑战性的问题。本文提出了一种基于签名链的支持多维top- k查询的认证方案。通过对每个维度上的每个记录及其后续记录使用签名链,我们的解决方案允许用户有效地验证多维top- k查询结果的健全性和完整性。通过理论分析和仿真,验证了该方法的有效性。
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引用次数: 5
An Online Approximate Stream Processing Framework with Customized Error Control 带有自定义错误控制的在线近似流处理框架
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624132
Xiaohui Wei, Yuanyuan Liu, Xingwang Wang, Shang Gao
In online approximate stream processing, customers generally submit their requests with some specific quality requirements (e.g. maximum error). This raises a critical problem that online quality control is necessary to meet customized requirements. Since continuous arriving data needs to be processed immediately, it brings the difficulty of acquiring knowledge which significantly affects the efficiency of sampling. Hence, it's more challenging to ensure a prescribed level of quality without knowledge about data. In this paper, we present an adaptive approximate processing framework for online stream applications to address the challenges mentioned above. Specially, we first design a new data knowledge learning scheme to stratify the arriving stream data. Then, based on the online learning results, we propose a dynamic sampling strategy with the consideration of the stream rate. Finally, we further present a double-check error control mechanism to manage the output quality. Experiments with real world datasets show that the proposed approximate framework is not only applicable to different data distributions, but also provides a customized error control.
在在线近似流处理中,客户通常提交带有一些特定质量要求(例如最大误差)的请求。这就提出了一个关键问题,即在线质量控制是满足定制需求的必要条件。由于连续到达的数据需要立即处理,这带来了获取知识的困难,严重影响了采样的效率。因此,在不了解数据的情况下确保规定的质量水平更具挑战性。在本文中,我们为在线流应用程序提出了一个自适应近似处理框架,以解决上述挑战。特别地,我们首先设计了一种新的数据知识学习方案,对到达的流数据进行分层。然后,基于在线学习结果,提出了考虑流率的动态采样策略。最后,我们进一步提出了一种双重检查错误控制机制来管理输出质量。在实际数据集上的实验表明,该近似框架不仅适用于不同的数据分布,而且提供了自定义的误差控制。
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引用次数: 1
Time-Efficient RFID-Based Stocktaking with a Coarse-Grained Inventory List 基于rfid的基于时间效率的粗粒度库存清单盘点
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624125
Weiping Zhu, Xing Meng, Xiaolei Peng, Jiannong Cao, M. Raynal
RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.
基于RFID的盘点使用RFID技术来验证一个区域(如仓库或图书馆)中物体的存在。用于此目的的现有方法假设RFID读取器查询区域中的对象清单是已知的。在某些情况下并非如此。例如,对于手持式RFID读取器,只能知道较大区域(例如,仓库)中的对象,而不能知道其查询区域中的对象。额外的对象大大增加了盘点所需的时间。为了解决这一问题,本文提出了一种基于粗粒度库存列表的库存盘点算法CLS。我们将该问题转化为缺失率较大的缺失标签识别问题。CLS允许多个缺失对象散列到单个时隙,从而一起验证它们。CLS还通过利用更多种类的RFID碰撞改进了现有的方法,并减少了阅读器发送的大约四分之一的数据量。进行了大量的仿真,结果表明CLS优于现有的最佳算法。
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引用次数: 2
Scalable Ground-Truth Annotation for Video QoE Modeling in Enterprise WiFi 企业WiFi中视频QoE建模的可扩展地基真值注释
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624138
Mallesham Dasari, Shruti Sanadhya, C. Vlachou, Kyu-Han Kim, Samir R Das
Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of myriads of applications from video telephony and streaming to virtual reality, network administrators face the challenge to provide high quality of experience (QoE) in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map quality of service (QoS) to QoE by training machine learning (ML) models without requiring user feedback, these techniques are limited to only few applications (e.g., Skype), due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score (MOS) from more than 200 users and 800 video samples. Our content-independent metrics significantly reduce the MOS prediction error of previous works and are validated over three popular video telephony applications – Skype, FaceTime and Google Hangouts.
移动视频业务在蜂窝和企业无线网络中占主导地位。随着从视频电话和流媒体到虚拟现实的无数应用的出现,网络管理员面临着在面对各种无线条件和应用内容时提供高质量体验(QoE)的挑战。然而,最先进的网络缺乏对QoE的分析,因为这需要应用程序或用户反馈的支持。虽然现有的技术可以通过训练机器学习(ML)模型,在不需要用户反馈的情况下将服务质量(QoS)映射到QoE,但由于ML的QoE基础事实注释不足,这些技术仅限于少数应用(例如Skype)。为了解决这些限制,我们将重点放在视频电话应用和时空视频QoE的关键工件模型上。我们的主要贡献是设计与内容和设备无关的指标,并在不同的WiFi条件下进行培训。通过与来自200多个用户和800个视频样本的平均意见得分(MOS)进行比较,我们的指标达到了中位数90%的准确率。我们的内容独立指标显著降低了以前工作的MOS预测误差,并在三种流行的视频电话应用程序- Skype, FaceTime和Google Hangouts上进行了验证。
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引用次数: 5
Reducing Web Latency Through Dynamically Setting TCP Initial Window with Reinforcement Learning 基于强化学习的动态设置TCP初始窗口减少Web延迟
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624175
Xiaohui Nie, Youjian Zhao, Dan Pei, Guo Chen, Kaixin Sui, Jiyang Zhang
Latency, which directly affects the user experience and revenue of web services, is far from ideal in reality, due to the well-known TCP flow startup problem. Specifically, since TCP starts from a conservative and static initial window (IW, 2∼4 or 10), most of the web flows are too short to have enough time to find its best congestion window before the session ends. As a result, TCP cannot fully utilize the available bandwidth in the modern Internet. In this paper, we propose to use group-based reinforcement learning (RL) to enable a web server, through trial-and-error, to dynamically set a suitable IW for a web flow before its transmission starts. Our proposed system, SmartIW, collects TCP flow performance metrics (e.g., transmission time, loss rate, RTT) in real-time without any client assistance. Then these metrics are aggregated into groups with similar features (subnet, ISP, province, etc.) to satisfy RL's requirement. SmartIW has been deployed in one of the top global search engines for more than a year. Our online and testbed experiments show that, compared to the common practice of $boldsymbol{IW}=10$, SmartIW can reduce the average transmission time by 23% to 29%.
由于众所周知的TCP流启动问题,延迟在现实中远非理想,直接影响到web服务的用户体验和收益。具体来说,由于TCP从一个保守和静态的初始窗口(IW, 2 ~ 4或10)开始,大多数web流太短,没有足够的时间在会话结束前找到最佳拥塞窗口。因此,TCP不能充分利用现代Internet的可用带宽。在本文中,我们提出使用基于组的强化学习(RL)使web服务器能够通过试错,在web流开始传输之前动态地为其设置合适的IW。我们提出的系统SmartIW可以实时收集TCP流性能指标(例如,传输时间、损失率、RTT),而无需任何客户端协助。然后将这些指标聚合成具有相似特征(子网、ISP、省等)的组,以满足RL的需求。SmartIW已经在全球顶级搜索引擎之一中部署了一年多。我们的在线和测试平台实验表明,与常用的$boldsymbol{IW}=10$相比,SmartIW可以将平均传输时间减少23%至29%。
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引用次数: 16
Throughput, Coverage and Scalability of LoRa LPWAN for Internet of Things 物联网LoRa LPWAN的吞吐量、覆盖范围和可扩展性
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624157
Asif M. Yousuf, Edward M. Rochester, Behnam Ousat, Majid Ghaderi
LoRa is a leading Low-Power Wide-Area Network (LPWAN) technology for Internet of Things (IoT). While LoRa networks are rapidly being deployed around the world, it is important to understand the capabilities and limitations of this technology in terms of its throughput, coverage and scalability. Using a combination of real-world measurements and high fidelity simulations, this paper aims at characterizing the performance of LoRa. Specifically, we present and analyze measurement data collected from a city-wide LoRa deployment in order to characterize the throughput and coverage of LoRa. Moreover, using a custom-built simulator tuned based on our measurement data, we present extensive simulation results in order to characterize the scalability of LoRa under a variety of traffic and network settings. Our measurement results show that as few as three gateways are sufficient to cover a dense urban area within an approximately 15 Km radius. Also, a single gateway can support as many as 105 end devices, each sending 50 bytes of data every hour with negligible packet drops. On the negative side, while a throughput of up to 5.5 Kbps can be achieved over a single 125 KHz channel at the physical layer, the throughput achieved at the application layer is substantially lower, less than 1 Kbps, due to the network protocols overhead.
LoRa是一种领先的物联网(IoT)低功耗广域网(LPWAN)技术。虽然LoRa网络正在世界各地迅速部署,但了解这种技术在吞吐量、覆盖范围和可伸缩性方面的功能和局限性非常重要。结合实际测量和高保真度仿真,本文旨在表征LoRa的性能。具体来说,我们提出并分析了从全市LoRa部署中收集的测量数据,以表征LoRa的吞吐量和覆盖范围。此外,使用基于我们的测量数据调优的定制模拟器,我们提供了广泛的仿真结果,以表征LoRa在各种流量和网络设置下的可扩展性。我们的测量结果表明,只需三个网关就足以覆盖半径约15公里的密集城区。此外,单个网关可以支持多达105个终端设备,每个设备每小时发送50字节的数据,数据包丢失可以忽略不计。在负面方面,虽然在物理层的单个125 KHz通道上可以实现高达5.5 Kbps的吞吐量,但由于网络协议开销,应用层实现的吞吐量大大降低,不到1 Kbps。
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引用次数: 64
Edge Computing Empowered Generative Adversarial Networks for Realtime Road Sensing 基于边缘计算的生成对抗网络用于实时道路感知
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624148
Yiting He, Xiaoyi Fan, Feng Wang, Fangxin Wang, Jiangchuan Liu
Automobiles have become one of the necessities of modern life and deeply penetrated into our daily activities. They unfortunately also introduce numerous social problems, among which traffic accidents are most notoriously threatening automobile drivers and other road users. Advanced driver-assistance systems (ADAS) are under rapid development in recent years, which can necessarily reduce or even eliminate the driver errors, significantly relieving on drivers suffering or stress. These state-of-the-art ADAS mainly rely on built-in cameras, radars and ultrasound sensors to provide road sensing services for object detection, which are further advanced by recent explosion of vision and neural network technologies.
汽车已经成为现代生活的必需品之一,并深入到我们的日常生活中。不幸的是,它们也带来了许多社会问题,其中交通事故是最臭名昭著的威胁汽车司机和其他道路使用者。先进驾驶辅助系统(ADAS)是近年来发展迅速的一种技术,它可以减少甚至消除驾驶员的失误,极大地减轻驾驶员的痛苦或压力。这些先进的ADAS主要依靠内置摄像头、雷达和超声波传感器为目标检测提供道路传感服务,最近视觉和神经网络技术的爆炸式发展进一步推动了这些服务的发展。
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引用次数: 4
Shaping Deadline Coflows to Accelerate Non-Deadline Coflows 塑造截止流程以加速非截止流程
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624122
Renhai Xu, Wenxin Li, Keqiu Li, Xiaobo Zhou
Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely focus on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this paper, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, mixCoflow, to schedule deadline coflows with the objective of minimizing and balancing their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, mixCoflow is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that mixCoflow can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.
数据并行应用程序会生成有截止日期和没有截止日期的混合流。截止日期的协同流是任务关键型的,必须在截止日期内完成,而非截止日期的协同流则希望尽快完成。调度这种混合流是现代数据中心的一个重要问题。然而,现有的解决方案只关注两种类型的共流中的一种:它们要么只关注满足截止日期感知的共流的截止日期,要么只关注减少非截止日期共流的共流完成时间(cct)。本文研究了同时优化截止日期和非截止日期共流的问题。为此,我们提出了一个新的优化框架mixCoflow来调度截止日期共流,其目标是最小化和平衡其带宽占用,从而使非截止日期共流可以尽早调度。具体地说,我们建立了数学模型,并将最后期限共流的调度问题表述为字典排序的最小-最大整数线性规划(ILP)问题。通过严格的理论分析,证明了该线性规划问题等价于线性规划问题,可以用标准线性规划求解器求解。通过解决这个LP, mixCoflow能够在保证截止日期的同时平衡截止日期共流的带宽占用。因此,非截止日期的协同流可以在它们到达时尽快安排。为了证明我们工作的有效性,我们基于广泛使用的Facebook数据跟踪进行了大量模拟。仿真结果表明,与现有方案相比,mixCoflow在不增加截止日期共流的截止日期错过率的情况下,显著提高了非截止日期共流的平均CCT。
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引用次数: 7
Deep and Broad Learning Based Detection of Android Malware via Network Traffic 基于网络流量的深度和广泛学习的Android恶意软件检测
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624143
Shanshan Wang, Zhenxiang Chen, Qiben Yan, Ke Ji, Lin Wang, Bo Yang, M. Conti
In recent years, the scale and diversity of malicious software on mobile networks are constantly increasing, thereby causing considerable danger to users' property and personal privacy. In this study, we devise a method that uses the URLs visited by applications to identify malicious apps. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of the input automatically and distribute soft attention weights to focus on different features of input. Multiple views preserve rich semantic information from input for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method has a certain timeliness. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance.
近年来,移动网络上恶意软件的规模和种类不断增加,给用户的财产和个人隐私造成了相当大的危害。在本研究中,我们设计了一种使用应用程序访问的url来识别恶意应用程序的方法。利用多视图神经网络建立了一个强调深度和宽度的恶意软件检测模型。该神经网络可以自动创建输入的多个视图,并分配软注意权值来关注输入的不同特征。多个视图从输入中保留丰富的语义信息用于分类,而不需要复杂的特征工程。此外,我们进行了全面的实验,将所提出的方法与其他方法进行了比较,验证了检测模型的有效性。实验结果表明,该方法具有一定的时效性。它不仅可以有效地检测出一年中不同月份发现的恶意软件,还可以检测出第三方应用市场中潜在的恶意应用。我们还将提出的方法与10种流行的反病毒扫描器对野生应用程序的检测结果进行了比较,最终结果表明,我们的方法在检测性能方面排名第二。
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引用次数: 24
Improving Quality of Experience for Mobile Broadcasters in Personalized Live Video Streaming 提高个性化视频直播中移动广播商的体验质量
Pub Date : 2018-06-01 DOI: 10.1109/IWQoS.2018.8624178
Q. Ren, Yong Cui, Wenfei Wu, Changfeng Chen, Yuchi Chen, Jiangchuan Liu, Hongyi Huang
Ensuring high video quality of experience (QoE) on the broadcaster side is critical for interactive live streaming. However, measurements on multiple live streaming platforms show that they all suffer from broadcaster-side video quality degradation in the presence of transient bandwidth fluctuations. This paper presents Greedy Variable Bitrate (GVBR), a suite of solutions that optimizes the QoE through an approriate keyframe interval that trades cross-frame compression for lowered inter-frame interdependency, a simple-yet-efficient frame dropping strategy to prevent excessive frame drops, and a bitrate adaptation strategy customized for broadcasters who have shallow buffer. We compare GVBR with state-of-art algorithms in different network conditions, and find that GVBR can cut video interruption incidents by 90%, while achieving comparable bitrate.
确保广播方的高视频体验质量(QoE)对于交互式直播至关重要。然而,对多个直播平台的测量表明,在存在瞬态带宽波动的情况下,它们都受到广播方视频质量下降的影响。本文提出了贪心可变比特率(GVBR),一套通过适当的关键帧间隔来优化QoE的解决方案,该解决方案通过交换跨帧压缩来降低帧间的相互依赖性,一种简单而有效的帧丢弃策略来防止过多的帧丢弃,以及为具有浅缓冲区的广播商定制的比特率自适应策略。我们将GVBR与最先进的算法在不同的网络条件下进行了比较,发现GVBR可以将视频中断事件减少90%,同时达到相当的比特率。
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引用次数: 5
期刊
2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)
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