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

The Trapezoidal Sketch for Frequency Estimation in Network Flow 网络流中频率估计的梯形草图

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651949

Ning Li, Xin Yuan, José-Fernán Martínez, Vicente Hernández Díaz

{"title":"The Trapezoidal Sketch for Frequency Estimation in Network Flow","authors":"Ning Li, Xin Yuan, José-Fernán Martínez, Vicente Hernández Díaz","doi":"10.1109/ICNP52444.2021.9651949","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651949","url":null,"abstract":"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.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms","authors":"Jianer Zhou, Xinyi Qiu, Zhenyu Li, Gareth Tyson, Qing Li, Jingpu Duan, Yi Wang","doi":"10.1109/ICNP52444.2021.9651912","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651912","url":null,"abstract":"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.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126970017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651982

Wen Wang, Xin Liu, Yao Yao, Ting Zhu

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

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651978

Viyom Mittal, Mohammad Jahanian, K. Ramakrishnan

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

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651977

Peirui Cao, Shizhen Zhao, Min Yee Teh, Yunzhuo Liu, Xinbing Wang

{"title":"TROD: Evolving From Electrical Data Center to Optical Data Center","authors":"Peirui Cao, Shizhen Zhao, Min Yee Teh, Yunzhuo Liu, Xinbing Wang","doi":"10.1109/ICNP52444.2021.9651977","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651977","url":null,"abstract":"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.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129381216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651984

Qianru Li, Chunyi Peng

{"title":"Reconfiguring Cell Selection in 4G/5G Networks","authors":"Qianru Li, Chunyi Peng","doi":"10.1109/ICNP52444.2021.9651984","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651984","url":null,"abstract":"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).","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133173662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651941

Zuoyin Tang, Jianhua He, Steven Knowles Flanagan, Phillip Procter, Ling Cheng

{"title":"Cooperative Connected Smart Road Infrastructure and Autonomous Vehicles for Safe Driving","authors":"Zuoyin Tang, Jianhua He, Steven Knowles Flanagan, Phillip Procter, Ling Cheng","doi":"10.1109/ICNP52444.2021.9651941","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651941","url":null,"abstract":"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.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"544 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123442206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[ICNP 2021 Front cover] [ICNP 2021封面]

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/icnp52444.2021.9651975

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

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651916

Yuhang Wang, Ying He, Minhui Dong

{"title":"Resource Allocation in Vehicular Networks with Multi-UAV Served Edge Computing","authors":"Yuhang Wang, Ying He, Minhui Dong","doi":"10.1109/ICNP52444.2021.9651916","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651916","url":null,"abstract":"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.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130991798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1