2021 IEEE 29th International Conference on Network Protocols (ICNP)最新文献_第6页

ISP Self-Operated BGP Anomaly Detection Based on Weakly Supervised Learning 基于弱监督学习的ISP自运行BGP异常检测

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651957

Yutao Dong, Qing Li, R. Sinnott, Yong Jiang, Shutao Xia

引用次数: 6

Speeding Up TCP with Selective Loss Prevention 基于选择性防丢失的TCP加速算法

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651983

Zhenyu Zhou, Xiaowei Yang

{"title":"Speeding Up TCP with Selective Loss Prevention","authors":"Zhenyu Zhou, Xiaowei Yang","doi":"10.1109/ICNP52444.2021.9651983","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651983","url":null,"abstract":"Low latency is an important design goal for reliable data transmission protocols such as TCP and QUIC. However, timeout-based loss recovery can unnecessarily increase end-to-end latency. Previous work in reducing timeout-based loss recovery latency either duplicates every packet to avoid loss or focuses on fine-tuning the timeout timers to shorten the timeout latency without causing spurious packet retransmissions. In this work, we propose a new mechanism called Selective Loss Prevention (SLP) to reduce the loss recovery latency of a reliable transport protocol. Through extensive trace analysis, we find that not all lost packets are equal. The loss of packets with certain flags, such as SYN and PSH, is more likely to cause timeouts than other packets. Based on this observation, we propose to selectively duplicate an \"important\" packet whose loss is likely to increase a connection's latency. We design an algorithm to determine when to duplicate a lost packet proactively and incorporate it into TCP's congestion control algorithm so that duplicate packets will not congest the network. We incorporate SLP into Linux's kernel and evaluate its performance. Our results show that SLP can reduce timeout-based latency caused by the loss of important packets in a connection, and its overhead is low.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"91 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132476906","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

Recycling Wasted Energy for Mobile Charging 回收废能源作流动充电

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651967

Yu Sun, Chi Lin, Haipeng Dai, Pengfei Wang, Jiankang Ren, Lei Wang, Guowei Wu

{"title":"Recycling Wasted Energy for Mobile Charging","authors":"Yu Sun, Chi Lin, Haipeng Dai, Pengfei Wang, Jiankang Ren, Lei Wang, Guowei Wu","doi":"10.1109/ICNP52444.2021.9651967","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651967","url":null,"abstract":"The rapid popularization of wireless power transfer (WPT) technology promotes the wide adoption of wireless rechargeable sensor networks (WRSNs). Traditional methods only focus on how to optimize network performance, and most of them overlook the energy waste issue induced by WPT. In this paper, we explore the potentials of recycling wasted energy when using WPT by means of freeloading. Specifically, with a slight modification on hardware, we expand the functionality of the mobile chargers (MCs), enabling them to harvest and recycle the WPT-induced wasted energy in the air to serve more sensors, which promotes energy efficiency. We model the problem, termed MEFree, as maximizing network energy efficiency by utilizing a limited number of freeloading MCs and scheduling their freeloading behaviors. Through jointly scheduling freeloading and charging tasks, the proposed scheme is able to solve the problem with a (1 − 1/e)/2 approximation ratio with a slightly relaxed budget. Extensive simulations are conducted and corresponding numerical results show that our proposed scheme significantly improves network energy efficiency by at least 18.8% and outperforms baseline algorithms by 19.1% on average in various aspects. Our test-bed experiments further demonstrate the practicability of our scheme in actual scenes.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"46 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":"131966882","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

Highway On-Ramp Merging for Mixed Traffic: Recent Advances and Future Trends 混合交通的高速公路入口匝道合并:最新进展和未来趋势

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651989

S. A. Fernandez, M. A. M. Marinho, M. Vakilzadeh, A. Vinel

引用次数: 2

Poster: Private Internet: A Global End-to-End Service Model 海报:私有互联网:全球端到端服务模式

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651920

Souvik Das, K. Saraç

引用次数: 0

Federated Traffic Engineering with Supervised Learning in Multi-region Networks 多区域网络中具有监督学习的联邦流量工程

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651918

Minghao Ye, Junjie Zhang, Zehua Guo, H. J. Chao

{"title":"Federated Traffic Engineering with Supervised Learning in Multi-region Networks","authors":"Minghao Ye, Junjie Zhang, Zehua Guo, H. J. Chao","doi":"10.1109/ICNP52444.2021.9651918","DOIUrl":"https://doi.org/10.1109/ICNP52444.2021.9651918","url":null,"abstract":"Network operators usually adopt Traffic Engineering (TE) to configure the routing in their networks to achieve good load balancing performance and high resource utilization. While centralized TE can effectively improve network performance with a global view of the network, distributed TE has been considered as an alternative to manage large-scale networks that are usually partitioned into multiple regions. However, it is challenging for distributed TE to reach a global optimal performance since each region can make its local routing decisions only based on partially observed network states. In this paper, we propose a novel distributed TE scheme called FedTe, which leverages supervised learning coupled with a collaborative approach to improve the overall load balancing performance for multi-region networks. FedTe learns from the global optimal routing strategy in a centralized offline manner and predicts the optimal distribution of cross-region traffic among different regions through distributed deployment in real time. The predicted cross-region traffic distribution is integrated with measured local traffic to construct each region’s optimal regional traffic matrix, which is used to perform intra-region TE optimization. FedTe can also handle dynamic traffic variation and link failures with a 2-layer hierarchical graph neural network architecture. To validate the effectiveness of the proposed scheme, we evaluate FedTe with two real-world network topologies and a large-scale synthetic topology. Extensive evaluation results show that FedTe can achieve near-optimal load balancing performance and outperform state-of-the-art distributed TE approaches by up to 28.9% on average.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"5 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":"116229852","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: 9

Poster: Enabling Fast Forwarding in Hybrid Software-Defined Networks 海报:在混合软件定义网络中启用快速转发

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651943

Yijun Sun, Zehua Guo, Songshi Dou, Junjie Zhang, Changlin Li, Xiang Ouyang

引用次数: 0

Poster: Data Collection for ML Classification of Encrypted Messaging Applications 海报:加密消息应用的ML分类数据收集

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651948

Jason Hussey, Ethan Taylor, Kerri Stone, T. Camp

引用次数: 0

ISRaft Consensus Algorithm for Autonomous Units 自治单元的ISRaft一致性算法

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651979

Linir Zamir, Aman Shaan, Mehrdad Nojoumian

引用次数: 3

STIP: A new model of trusted network 一种新的可信网络模型

2021 IEEE 29th International Conference on Network Protocols (ICNP) Pub Date : 2021-11-01 DOI: 10.1109/ICNP52444.2021.9651913

Sara Bitan, Adi Molkho

引用次数: 1