5th Asia-Pacific Workshop on Networking (APNet 2021)最新文献

Exploring the Impact of Attacks on Ring AllReduce 探索攻击对Ring AllReduce的影响

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469676

Jiayu Wang, Peng Liu, Zehua Guo, Sen Liu, Chao Yao

引用次数: 0

A Sketch Algorithm to Monitor High Packet Delay in Network Traffic 一种监控网络流量中高数据包延迟的草图算法

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469398

Jiaqi Zhu, Kai Zhang, Qun Huang

{"title":"A Sketch Algorithm to Monitor High Packet Delay in Network Traffic","authors":"Jiaqi Zhu, Kai Zhang, Qun Huang","doi":"10.1145/3469393.3469398","DOIUrl":"https://doi.org/10.1145/3469393.3469398","url":null,"abstract":"Packet delay is a consensual indicator of network conditions. High delay packets of a flow indicate that there may be network congestion or network anomalies. In this paper, we consider Intra-FlowPacketDelay (IFPD), which is defined as the time span between two adjacent packets of a flow. In particular, we aim to detect packets that exhibit high IFPD. The key challenge is to simultaneously achieve high detection accuracy and preserve low resource usage. Existing measurement approaches reduce resource overheads by injecting probe packets or sampling. However, they can only measure an average delay of some packets but fail to monitor delay behavior of every single packet. To this end, we propose a sketch-based approach. Unfortunately, existing sketch-based methods cannot be directly applied to our high IFPD detection problem. That is because traditional sketch algorithms require that the update operation is additive, while measuring IFPD needs to deal with timestamps, which is not additive. We address this issue in three aspects: (i) using fingerprints to mitigate hash conflicts; (ii) a conservative update method that only selects one bucket to update; (iii) a replacement strategy that keeps potential flows with high IFPD in the sketch. Our experiments on real world traces demonstrate that our solution identifies high IFPD with nearly 99% recall rate and 99% precision with 600 KB memory, which outperforms existing sketch-based solutions.","PeriodicalId":291942,"journal":{"name":"5th Asia-Pacific Workshop on Networking (APNet 2021)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116029984","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

Physical-Layer Informed Multipath Redundancy Optimization for Mobile Real-Time Communication 面向移动实时通信的物理层多路径冗余优化

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469673

Jing Chen, Zili Meng, Mingwei Xu

引用次数: 0

pHPA: A Proactive Autoscaling Framework for Microservice Chain pHPA:微服务链的主动自动扩展框架

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469401

Byungkwon Choi, Jinwoo Park, Chunghan Lee, Dongsu Han

引用次数: 14

FLAG: Flow Representation Generator based on Self-supervised Learning for Encrypted Traffic Classification FLAG:基于自监督学习的流量表示生成器

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469394

Wenting Wei, Tianjie Ju, Han Liao, Weike Zhao, Huaxi Gu

{"title":"FLAG: Flow Representation Generator based on Self-supervised Learning for Encrypted Traffic Classification","authors":"Wenting Wei, Tianjie Ju, Han Liao, Weike Zhao, Huaxi Gu","doi":"10.1145/3469393.3469394","DOIUrl":"https://doi.org/10.1145/3469393.3469394","url":null,"abstract":"Due to its excellent ability in learning features from large scale raw data, deep learning (DL) has attracted much attention for encrypted traffic classification. However, most DL-based traffic classifiers usually rely on enormous labeled samples. Motivated by this, we investigate a self-supervised traffic classifier (FLAG) without sacrifice of identification accuracy, only depending on small labeled traffic samples and highly available unlabeled traffic samples. Specifically, focusing on local short-term characteristics of traffic, we design a preprocessing algorithm, termed as N-phrase Extration, to convert unlabeled raw traffic dataset into sequences of high-frequency phrases as input of Bidirectional Encoder. On account of their significance, potential timing characteristics from input sequences are mined by Bidirectional Encoder and embedded into robust representations with distributed vectors to enhance classifier’s performance significantly. Our comprehensive experiments indicate FLAG can achieve 98.65% in 100% of dataset and 98.07% in 10% of dataset in terms of true positive rate in UNB ISCX VPN-nonVPN dataset, which are better than p-FP, FS-Net and Deep Packet.","PeriodicalId":291942,"journal":{"name":"5th Asia-Pacific Workshop on Networking (APNet 2021)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114924283","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: 2

Flexible Routing with Policy Exchange 灵活的路由与策略交换

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469395

Bin Gui, Fangping Lan, Anduo Wang

{"title":"Flexible Routing with Policy Exchange","authors":"Bin Gui, Fangping Lan, Anduo Wang","doi":"10.1145/3469393.3469395","DOIUrl":"https://doi.org/10.1145/3469393.3469395","url":null,"abstract":"BGP and its alternatives alike, struggle with distributed policy making in the absence of a central authority: BGP prioritizes independence of the participating networks (e.g., ASes), imposes zero coordination, but has to tolerate inflexible policies each network can express. On the other hand, BGP alternatives (source routing, for example), through coordination, trade independence for flexibility, but only achieve flexibility partially. This paper asks, to achieve flexible routing, what is the fitting adjustment between network independence and coordination? To answer this question, we propose a simple principle that the sole end to interfere with the flexibility of a participating network is to prevent harms — decreasing the level of flexibility — to others. As an instantiation of this principle, we introduce the concept of policy exchange that dynamically adjusts independently set policies on the fly, and develop a preliminary implementation with conditional table, a strong knowledge representation system that allows us to distribute and manipulate policies with the usual SQL-like operators. Our preliminary experiments on realistic network topology and synthetic policies are encouraging.","PeriodicalId":291942,"journal":{"name":"5th Asia-Pacific Workshop on Networking (APNet 2021)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123484431","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

FlowDiviner: Spatio-Temporal Network Traffic Prediction Method Based on Graph Neural Network FlowDiviner:基于图神经网络的时空网络流量预测方法

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469671

L. Qin, Wenting Wei, Yinghao Ma

引用次数: 0

Learning Based Deadline Aware Congestion Control 基于学习的截止时间感知拥塞控制

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469675

Rongji Liao, Jinyao Yan, Tao Lin, Yuhao Chen

引用次数: 1

Towards Optimization for Large-scale Earth Observation Missions from a Global Perspective 从全球视角看大规模对地观测任务的优化

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469674

Yaning Guo, Jing Chen, Zili Meng, Mingwei Xu

引用次数: 0

Combined Shared-Memory and Buffered-Crossbar Architecture for High-Bandwidth Onboard Switching Fabric 高带宽板载交换结构的联合共享内存和缓冲横杆结构

5th Asia-Pacific Workshop on Networking (APNet 2021) Pub Date : 2021-06-24 DOI: 10.1145/3469393.3469672

Ling Zheng, Chen Yi, Weitao Pan, Huan Liu

引用次数: 0