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2021 IEEE 29th International Conference on Network Protocols (ICNP)最新文献

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The Trapezoidal Sketch for Frequency Estimation in Network Flow 网络流中频率估计的梯形草图
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651949
Ning Li, Xin Yuan, José-Fernán Martínez, Vicente Hernández Díaz
The sketch is one of the typical and widely-used data structures for estimating the frequencies of items in data streams. However, since the counter sizes in traditional rectangular sketch (r-sketch) are the same, it is hard to achieve small space usage, high capacity (i.e., the maximum frequency can be recorded), and high estimated accuracy simultaneously. Moreover, when considering the high skewness of data streams, this problem will become even worse. Consequently, we propose the trapezoidal sketch (t-sketch) in this paper. In the t-sketch, different from the r-sketch, the counter sizes in different layers are different. Therefore, the low space usage and high capacity can be achieved simultaneously in the t-sketch. Moreover, based on the basic t-sketch, we propose the space-saving t-sketch and the capacity-improvement t-sketch, and analyze the properties of these two t-sketches. Compared with the CM sketch, CU sketch, C sketch, and A sketch, the simulation results show that the performances on space usage, capacity, and estimation accuracy are improved successfully by the space-saving t-sketch and the capacity-improvement t-sketch.
草图是用于估计数据流中项目频率的典型且广泛使用的数据结构之一。然而,由于传统矩形草图(r-sketch)中的计数器尺寸是相同的,因此很难同时实现小空间占用、高容量(即可以记录的最大频率)和高估计精度。而且,当考虑到数据流的高度偏度时,这个问题会变得更加严重。因此,本文提出了梯形草图(t-草图)。在t-sketch中,不同于r-sketch,不同层的计数器大小是不同的。因此,在t型草图中可以同时实现低空间占用和高容量。在基本t-草图的基础上,提出了节省空间的t-草图和提高容量的t-草图,并分析了这两种t-草图的性质。与CM草图、CU草图、C草图和A草图相比,仿真结果表明,节省空间的t草图和提高容量的t草图在空间利用率、容量和估计精度方面都有显著提高。
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引用次数: 0
Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms 羚羊:一个动态选择拥塞控制算法的框架
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651912
Jianer Zhou, Xinyi Qiu, Zhenyu Li, Gareth Tyson, Qing Li, Jingpu Duan, Yi Wang
Most congestion control mechanisms are designed for specific network environments. Hence, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising such a one-size-fits-all algorithm, we propose a system to dynamically switch between the most suitable congestion control mechanisms for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure to use the most suitable congestion control mechanism for an individual flow. We build a machine learning approach to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically adjusting congestion control algorithms. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. We show that in WAN, DCN, and cellular networks, Antelope achieves an average 16% improvement in throughput compared with BBR; compared with Cubic, Antelope achieves an average 19% improvement in throughput and 10% reduction in delay.
大多数拥塞控制机制都是为特定的网络环境设计的。因此,没有已知的算法可以在所有流的所有场景中实现一致的良好性能。而不是设计这样一个一刀切的算法,我们提出了一个系统,以动态切换最适合的拥塞控制机制之间的特定环境中的特定流。这带来了许多挑战,我们通过羚羊的设计和实现来解决这些挑战,羚羊是一个可以动态重新配置的系统,可以为单个流使用最合适的拥塞控制机制。我们构建了一种机器学习方法来学习哪种算法最适合各个条件,并实现了对动态调整拥塞控制算法的内核级支持。我们已经在Linux中实现了Antelope,并在模拟网络和生产网络中对其进行了评估。研究表明,在WAN、DCN和蜂窝网络中,Antelope比BBR平均提高了16%的吞吐量;与Cubic相比,Antelope的吞吐量平均提高了19%,延迟减少了10%。
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引用次数: 5
A Blockchain-based Trusted Testing System of Electric Power Materials 基于区块链的电力材料可信测试系统
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651966
Bing Tian, Xiaofeng Chen, Jiawen Wang, Dong Li, Dong Wang, Liangliang Zhi, Keting Yin
In order to curb the illegal activities in power resources detection, improve the credibility and contribution rate of the industry, and promote the development of high-quality services, this paper proposes a secure and reliable trusted testing system of electric power materials based on blockchain. Firstly, a device and personal information query authorization mechanism is established to provide solutions for personnel and testing equipment authorization. It can help ensure the reliability of testing data on the premise of security. Secondly, we propose a method to deal with the difficulties of testing information management. Lastly, we introduce the case of electricity management helping the power authorities to supervise effectively and increasing the credibility of power material procurement evidence to prove the feasibility of this system.
为了遏制电力资源检测中的违法违规行为,提高行业的公信力和贡献率,促进优质服务的发展,本文提出了一种基于区块链的安全可靠的电力材料可信检测系统。首先,建立设备和个人信息查询授权机制,为人员和检测设备的授权提供解决方案。在保证安全的前提下,有助于保证检测数据的可靠性。其次,提出了一种解决考试信息管理难题的方法。最后,通过电力管理的案例,帮助电力部门有效监管,提高电力物资采购证据的可信度,证明了该系统的可行性。
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引用次数: 0
Exploiting WiFi AP for Simultaneous Data Dissemination among WiFi and ZigBee Devices 利用WiFi AP在WiFi和ZigBee设备之间同时传输数据
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651982
Wen Wang, Xin Liu, Yao Yao, Ting Zhu
Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.
跨技术通信(CTC)的最新进展为异构物联网设备之间的合作打开了新的大门,以支持无处不在的应用,如智能家居和智能办公室。然而,现有的工作主要集中在物理层的性能改进上。在本文中,我们探讨了如何利用最新的CTC技术来提高网络层的性能。具体来说,我们介绍了Waves,它利用WiFi到ZigBee CTC和WiFi接入点的自适应发射功率控制技术,在低占空比ZigBee网络中实现可靠和快速的数据传播。我们在各种环境下广泛评估我们的设计。评估结果表明,Waves可以提供可靠的数据传播,在传播时间方面比最先进的协议快33.5倍。
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引用次数: 7
DEMO: FLARE: Federated Active Learning Assisted by Naming for Responding to Emergencies 演示:FLARE:通过命名辅助的联邦主动学习,以响应紧急情况
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651978
Viyom Mittal, Mohammad Jahanian, K. Ramakrishnan
Name-based pub/sub allows for efficient and timely delivery of information to interested subscribers. A challenge is assigning the right name to each piece of content, so that it reaches the most relevant recipients. An example scenario is the dissemination of social media posts to first responders during disasters. We present FLARE, a framework using federated active learning assisted by naming. FLARE integrates machine learning and name-based pub/sub for accurate timely delivery of textual information. In this demo, we show FLARE’s operation.
基于名称的发布/订阅允许向感兴趣的订阅者有效和及时地传递信息。一个挑战是为每条内容分配正确的名称,以便它到达最相关的收件人。一个示例场景是在灾难期间向第一响应者传播社交媒体帖子。我们提出了FLARE,这是一个通过命名辅助的联邦主动学习框架。FLARE集成了机器学习和基于名称的发布/订阅,以准确及时地传递文本信息。在这个演示中,我们展示FLARE的操作。
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引用次数: 0
TROD: Evolving From Electrical Data Center to Optical Data Center TROD:从电气数据中心到光学数据中心的演变
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651977
Peirui Cao, Shizhen Zhao, Min Yee Teh, Yunzhuo Liu, Xinbing Wang
Despite the bandwidth scaling limit of electrical switching and the high cost of building Clos data center networks (DCNs), the adoption of optical DCNs is still limited. There are two reasons. First, existing optical DCN designs usually face tremendous deployment complexity. Second, these designs are not full-optical and the performance benefit against the non-blocking Clos DCN is not clear.After exploring the design tradeoffs of the existing optical DCN designs, we propose TROD (Threshold Routing based Optical Datacenter), a low-complexity optical DCN with superior performance than other optical DCNs. There are two novel designs in TROD that contribute to its success. First, TROD performs robust topology optimization based on the recurring traffic patterns and thus does not need to react to every traffic change, which lowers deployment and management complexity. Second, TROD introduces tVLB (threshold-based VLB), which can avoid network congestion as much as possible even under unexpected traffic bursts. We conduct simulation based on both Facebook’s real DCN traces and our synthesized highly bursty DCN traces. TROD reduces flow completion time (FCT) by at least 2× compared with the existing optical DCN designs, and by approximately 2.4-3.2× compared with expander graph DCN. Compared with the non-blocking Clos, TROD reduces the hop count of the majority packets by one, and could even outperform the non-blocking Clos with proper bandwidth over-provision at the optical layer. Note that TROD can be built with commercially available hardware and does not require host modifications.
尽管存在电交换的带宽扩展限制和建设Clos数据中心网络(DCNs)的高成本,但光DCNs的采用仍然受到限制。有两个原因。首先,现有的光DCN设计通常面临巨大的部署复杂性。其次,这些设计不是全光学的,相对于非阻塞Clos DCN的性能优势尚不清楚。在研究了现有光DCN设计的设计权衡之后,我们提出了基于阈值路由的光数据中心(TROD),这是一种低复杂度的光DCN,具有优于其他光DCN的性能。在TROD中有两个新颖的设计促成了它的成功。首先,TROD基于重复出现的流量模式执行健壮的拓扑优化,因此不需要对每个流量变化做出反应,从而降低了部署和管理的复杂性。其次,TROD引入了tVLB(基于阈值的负载均衡),即使在意外的流量突发情况下,也可以尽可能地避免网络拥塞。我们基于Facebook的真实DCN轨迹和我们合成的高度突发DCN轨迹进行了模拟。与现有的光学DCN设计相比,TROD将流动完井时间(FCT)减少了至少2倍,与扩展图DCN设计相比减少了大约2.4-3.2倍。与非阻塞Clos相比,TROD将大多数数据包的跳数减少了1,并且在光层适当的带宽过剩下甚至可以优于非阻塞Clos。注意,TROD可以用商用硬件构建,不需要修改主机。
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引用次数: 6
Reconfiguring Cell Selection in 4G/5G Networks 在4G/5G网络中重新配置小区选择
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651984
Qianru Li, Chunyi Peng
In cellular networks, cell selection plays a critical role in providing and maintaining ubiquitous radio access. It follows standardized procedures with operator-specific polices pre-configured by tunable parameters. These parameters specify the criteria to determine whether and how to select new serving cell(s), thus impacting access quality and user experience. Recent studies reveal that today’s cell selection fails to offer good performance as it can. This is because it is configured for seamless connectivity, and thus performance is offered at "best effort". In this work, we attempt to re-configure these parameters by taking performance into consideration. We first conduct a measurement study in one big city in the US to demonstrate that reconfiguration indeed helps improve the overall performance, without compromising connectivity. This implies that 4G/5G networks are capable of offering better performance but such potentials are under-utilized in practice. We further explore proactive reconfiguration to prevent such unnecessary performance losses. We examine technical challenges, factors and even limitations to reconfigure cell selection in a standard-compatible manner, and finally devise a simple reconfiguration algorithm based on profiling and heuristic searching to efficiently pursue promising performance gains. The evaluation over AT&T and T-Mobile in two US cities has validated its effectiveness. Performance gains outweigh losses. Reconfiguration boosts data speed in more than 30% of instances, which exceeds the ratio of losses by at least 16%; The median speed gain is at least 89.1% (up to 217 fold).
在蜂窝网络中,小区选择在提供和维持无处不在的无线接入方面起着关键作用。它遵循标准化的过程,使用由可调参数预先配置的特定于操作人员的策略。这些参数指定了确定是否以及如何选择新的服务单元的标准,从而影响访问质量和用户体验。最近的研究表明,今天的细胞选择不能提供良好的性能,因为它可以。这是因为它被配置为无缝连接,因此可以“尽最大努力”提供性能。在这项工作中,我们试图通过考虑性能来重新配置这些参数。我们首先在美国的一个大城市进行了一项测量研究,以证明重新配置确实有助于提高整体性能,而不会影响连通性。这意味着4G/5G网络能够提供更好的性能,但这种潜力在实践中没有得到充分利用。我们进一步探索主动重新配置,以防止此类不必要的性能损失。我们研究了以标准兼容的方式重新配置单元选择的技术挑战、因素甚至限制,并最终设计了一种基于分析和启发式搜索的简单重新配置算法,以有效地追求有希望的性能提升。在美国两个城市对AT&T和T-Mobile的评估验证了其有效性。性能收益大于损失。在超过30%的实例中,重新配置提高了数据速度,这比损失的比率至少高出16%;速度增益中位数至少为89.1%(高达217倍)。
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引用次数: 4
Cooperative Connected Smart Road Infrastructure and Autonomous Vehicles for Safe Driving 协同互联智能道路基础设施和自动驾驶汽车安全驾驶
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651941
Zuoyin Tang, Jianhua He, Steven Knowles Flanagan, Phillip Procter, Ling Cheng
Connected vehicles (CV) and automated vehicles (AV) are promising technologies for reducing road accidents and improving road efficiency. Significant advances have been achieved for AV and CV technologies, but they both have inherent shortcomings such line of sight sensing for AV. Connected autonomous vehicles (CAV) has been proposed to address the problems through sharing sensing and cooperative driving. While the focus of the research on CAV has been on the vehicles so far, cooperative and connected smart road infrastructure can play a critical role to enhance CAV and safe driving. In this paper we present an investigation of connected smart road infrastructure and AVs (CRAV). We discuss the potentials and challenges of CRAV, then propose a scalable simulation framework for the CRAV to facilitate fast, economic and quantitative study of CRAV. A case study of CRAV on smart road side unit (RSU) assisted vulnerable road users (VRU) collision warning is conducted, where the identification of VRU such as pedestrians on the road by the AVs is compared with and without RSU assistance. The impact of the location of RSUs on avoiding potential collisions is evaluated for vehicles with different sensor configurations. Preliminary simulation results show that with the support of smart RSUs, the CAVs could be notified of the existence of the VRUs on the road by the RSUs much earlier than they can detect with their own onboard sensors, and collisions with VRUs can be reduced. This study demonstrates the effectiveness of the proposed CRAV simulation framework and the great potentials of CRAV.
网联汽车(CV)和自动驾驶汽车(AV)是减少道路事故和提高道路效率的有前途的技术。自动驾驶汽车(AV)和自动驾驶汽车(CV)技术已经取得了显著的进步,但它们都存在固有的缺陷,比如自动驾驶汽车的视线感知。人们提出了联网自动驾驶汽车(CAV),通过共享感知和协作驾驶来解决这些问题。虽然目前对自动驾驶汽车的研究主要集中在车辆上,但协同互联的智能道路基础设施对于提高自动驾驶汽车的性能和安全驾驶具有至关重要的作用。在本文中,我们提出了连接智能道路基础设施和自动驾驶汽车(CRAV)的研究。在此基础上,提出了一种可扩展的CRAV仿真框架,以促进对CRAV的快速、经济和定量研究。以智能路侧单元(RSU)辅助弱势道路使用者(VRU)碰撞预警的CRAV为例,比较了在有RSU辅助和没有RSU辅助的情况下,自动驾驶汽车对道路上行人等弱势道路使用者的识别情况。在不同传感器配置的情况下,评估了rsu位置对避免潜在碰撞的影响。初步仿真结果表明,在智能rsu的支持下,自动驾驶汽车可以比车载传感器更早地收到道路上有vru存在的通知,从而减少与vru的碰撞。该研究证明了所提出的CRAV仿真框架的有效性和CRAV的巨大潜力。
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引用次数: 0
[ICNP 2021 Front cover] [ICNP 2021封面]
Pub Date : 2021-11-01 DOI: 10.1109/icnp52444.2021.9651975
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引用次数: 0
Resource Allocation in Vehicular Networks with Multi-UAV Served Edge Computing 基于多无人机服务边缘计算的车联网资源分配
Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651916
Yuhang Wang, Ying He, Minhui Dong
With the rapid development of intelligent transportation systems, there is an increasingly strong demand for low-latency and high-bandwidth vehicular services, such as automatic driving assistance, emergency alarm, and infotainment. However, in some cases (e.g., traffic congestion, remote areas), the ground communication networks alone cannot meet the vast needs of vehicles. Unmanned aerial vehicles (UAVs) are flexible and deployable, which can be used as a supplement to the ground networks, to relieve the communication pressure on ground facilities, such as base stations. In this paper, we use multiple UAVs to provide services for vehicles and model the multi-UAV scenario as a collaborative multi-agent system. All UAVs share limited bandwidth resources and equip with edge computing servers to serve the vehicles. In addition, serious consequences may be caused if the delay requirements of vehicles are not satisfied. Therefore, we take vehicle safety as the top priority and the delay requirement as the constraints. Then we exploit the Lagrange multiplier to combine the constraint function and cost function, so as to reduce the resource consumption as much as possible on the premise of ensuring the safety of the vehicles. The influence of channel efficiency and computing power should also be taken into account when allocating resources. We adopt the multi-agent reinforcement learning to train the UAVs, and meanwhile introduce the attention mechanism so that each UAV can optimize itself better with the information of other UAVs. Through a large number of experiments, the effectiveness of our proposed method is verified. Particularly, in the case of strictly limiting bandwidth resources, resources can still be allocated according to vehicle needs under the premise of ensuring vehicle safety.
随着智能交通系统的快速发展,对自动驾驶辅助、紧急报警、信息娱乐等低延迟、高带宽的车载服务需求日益强烈。然而,在某些情况下(如交通拥堵、偏远地区),仅靠地面通信网络无法满足车辆的庞大需求。无人机(uav)具有灵活性和可部署性,可以作为地面网络的补充,缓解基站等地面设施的通信压力。在本文中,我们使用多无人机为车辆提供服务,并将多无人机场景建模为一个协作的多智能体系统。所有无人机共享有限的带宽资源,并配备边缘计算服务器为车辆服务。此外,如果不满足车辆的延迟要求,可能会造成严重的后果。因此,我们以车辆安全为首要任务,以延迟要求为约束条件。然后利用拉格朗日乘子将约束函数和成本函数结合起来,在保证车辆安全的前提下,尽可能的减少资源消耗。在分配资源时还应考虑信道效率和计算能力的影响。我们采用多智能体强化学习对无人机进行训练,同时引入注意机制,使每架无人机能够更好地利用其他无人机的信息进行自我优化。通过大量的实验,验证了该方法的有效性。特别是在严格限制带宽资源的情况下,在保证车辆安全的前提下,仍然可以根据车辆的需要进行资源分配。
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引用次数: 1
期刊
2021 IEEE 29th International Conference on Network Protocols (ICNP)
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