2020 IEEE 28th International Conference on Network Protocols (ICNP)最新文献_第6页

Search-tree Based SDN Candidate Selection in Hybrid IP/SDN Network 基于搜索树的混合IP/SDN网络SDN候选对象选择

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259394

Ning Li, Yue Shi, Zhaoxin Zhang, José-Fernán Martínez, Xin Yuan

{"title":"Search-tree Based SDN Candidate Selection in Hybrid IP/SDN Network","authors":"Ning Li, Yue Shi, Zhaoxin Zhang, José-Fernán Martínez, Xin Yuan","doi":"10.1109/ICNP49622.2020.9259394","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259394","url":null,"abstract":"The link failure recovery is important to the Internet. For improving the performance of link failure recovery in the IP network, the software defined networking (SDN) is applied to achieve this target. The SDN is effective on solving this kind of issue. However, considering the deployment cost, only a few IP routers can be replaced by the SDN switches. Thus, to minimize the number of SDN switches, the greedy-based approach is proposed to select the most appropriate deployment locations. But the greedy-based approach has disadvantages. For addressing these disadvantages, in this paper, we proposed the search-tree based SDN candidate selection (SCS) algorithm. In this algorithm, for achieving better performance than the greedy-based approach, three algorithms are proposed, which are the search-tree based feasible solutions calculation algorithm, the most appropriate feasible solution selection algorithm, and the most appropriate designated SDN switch selection algorithm. Based on these algorithms, the performance of the search-tree based SCS algorithm is improved greatly compared with the greedy-based algorithms.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115668991","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: 3

A Multi-agent Reinforcement Learning Perspective on Distributed Traffic Engineering 分布式交通工程中的多智能体强化学习

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259413

Nan Geng, Tian Lan, V. Aggarwal, Yuan Yang, Mingwei Xu

{"title":"A Multi-agent Reinforcement Learning Perspective on Distributed Traffic Engineering","authors":"Nan Geng, Tian Lan, V. Aggarwal, Yuan Yang, Mingwei Xu","doi":"10.1109/ICNP49622.2020.9259413","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259413","url":null,"abstract":"Traffic engineering (TE) in multi-region networks is a challenging problem due to the requirement that each region must independently compute its routing decisions based on local observations, yet with the goal of optimizing global TE objectives. Traditional approaches often lack the agility to adapt to changing traffic patterns and thus may suffer hefty performance loss under highly dynamic traffic demands. In this paper, we propose a data-driven framework for multi-region TE problems, which makes novel use of multi-agent deep reinforcement learning. In particular, we propose two reinforcement learning agents for each region, namely T-agents and O-agents, to control the terminal traffic and outgoing traffic, respectively. These distributed agents collect local link utilization statistics within their regions, optimize local routing decisions, and observe the resulting congestion-related reward. To facilitate these agents for optimizing global TE objectives, we tailor the agent design carefully including input, output, and reward functions. The proposed framework is evaluated extensively using real-world network topologies (e.g., Telstra and Google Cloud) and synthetic traffic patterns (e.g., the Gravity model). Numerical results show that comparing with existing protocols and single-agent learning algorithms, our solution can significantly reduce congestion and achieve nearly-optimal performance with both superior scalability and robustness. Throughout our simulations, over 90% of tests limit congestion within 1.2 times the global optimal solution.1","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127523492","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: 22

Demo: A Blockchain Based Protocol for Federated Learning 演示:基于区块链的联邦学习协议

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259388

Qiong Zhang, P. Palacharla, M. Sekiya, Junichi Suga, T. Katagiri

引用次数: 8

SCIONLAB: A Next-Generation Internet Testbed SCIONLAB:下一代互联网测试平台

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259355

Jonghoon Kwon, J. A. García-Pardo, M. Legner, F. Wirz, Matthias Frei, D. Hausheer, A. Perrig

引用次数: 26

Time-efficient Range Detection in Commodity RFID Systems 商品RFID系统的时效性距离检测

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259371

Jia Liu, Xingyu Chen, Haisong Liu, Hualin Gong, Yanyan Wang, Lijun Chen

{"title":"Time-efficient Range Detection in Commodity RFID Systems","authors":"Jia Liu, Xingyu Chen, Haisong Liu, Hualin Gong, Yanyan Wang, Lijun Chen","doi":"10.1109/ICNP49622.2020.9259371","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259371","url":null,"abstract":"RFID is becoming ubiquitously available in our daily life. After RFID tags are deployed to make attached objects identifiable, a natural next step is to communicate with the tags and collect their information for the purpose of tracking tagged objects or monitoring their surroundings in real time. In this paper, we study an under-investigated problem range detection in a commodity RFID system, which aims to check if there are any tags with the data between an upper and lower boundary in a time-efficient way. This is important especially in a large RFID system, which can help users quickly pinpoint the target tags (if any) and give an early warning to users for taking urgent actions and reducing the potential risk in the nascent stage. We propose two tailored protocols, selective query and range query (RQ), to achieve range detection within the scope of the C1G2 standard. The novelty is that, instead of querying each tag, we exploit the capability of C1G2-compatible selection and quickly separate target tags from others by silencing most of tags. The final result is that our best protocol RQ is able to achieve a range detection with only one query command. We implement the proposed protocols in commodity RFID systems, without any modifications of hardware. Extensive experiments show that RQ is able to improve the time efficiency by near 30×, compared with the baseline.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781484","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: 3

TurboNet: Faithfully Emulating Networks with Programmable Switches TurboNet:用可编程交换机忠实地模拟网络

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259358

Jiamin Cao, Yu Zhou, Y. Liu, Mingwei Xu, Yongkai Zhou

{"title":"TurboNet: Faithfully Emulating Networks with Programmable Switches","authors":"Jiamin Cao, Yu Zhou, Y. Liu, Mingwei Xu, Yongkai Zhou","doi":"10.1109/ICNP49622.2020.9259358","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259358","url":null,"abstract":"Faithfully emulating networks is critical for verifying the correctness and effectiveness of new networking-related designs. Existing network experiment platforms either cannot faithfully emulate functionality and performance of production networks or cannot scale well because of cost limitations. In this paper, we propose TurboNet, a new network emulator that leverages one programmable switch to enable faithful emulation of both network data plane and control plane. For data plane emulation, we present a series of key designs such as port mapper, queue mapper, and delayed queue to emulate network topologies and performance metrics with high flexibility and accuracy. For control plane emulation, we support static routing configurations, distributed routing agents, and the centralized routing controller. Meanwhile, we provide API for operators to simplify network emulation tasks. We implement TurboNet on a Tofino switch. The evaluation results show that: (1) TurboNet can flexibly emulate various topologies such as the 8-ary fat-tree on the data plane and support about 200 BGP agents with 25% CPU usage on the control plane; (2) TurboNet can accurately emulate different network performance metrics, including 400Gbps linerate background traffic injection, as small as 10–8 link loss, and microsecond-level to millisecond-level link delay.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131686457","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: 8

HOLNET: A Holistic Traffic Control Framework for Datacenter Networks HOLNET:数据中心网络的整体流量控制框架

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259410

Zhijun Wang, Akshit Singhal, Yunxiang Wu, Chuwen Zhang, Hao Che, Hong Jiang, B. Liu

{"title":"HOLNET: A Holistic Traffic Control Framework for Datacenter Networks","authors":"Zhijun Wang, Akshit Singhal, Yunxiang Wu, Chuwen Zhang, Hao Che, Hong Jiang, B. Liu","doi":"10.1109/ICNP49622.2020.9259410","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259410","url":null,"abstract":"In this paper, we put forward a HOListic traffic control framework for datacenter NETworks (HOLNET). HOLNET reformulates the network utility maximization (NUM) framework into a HOLNET NUM framework that fully harnesses the potential of the existing NUM-based solutions to allow large families of traffic control protocols of various degrees of sophistication to be developed, i.e., host-based, single or multiple Class-of-Service (CoS) enabled, single or multi-path congestion control, with or without in-network load balancing. Unlike the existing solutions that are largely empirical and point by design, HOLNET is a principled, systematic framework. All the protocols in a family developed under HOLNET share a common, user-defined global optimization objective and fairness criterion. As a result, the protocols in a family can be fairly compared and carefully selected to fully explore the performance, scalability and design complexity tradeoffs. Case studies, based on both a single and a multi-path host-based solutions, demonstrate the viability and flexibility in HOLNET design space exploration. To further test the backward compatibility and performance with respect to some existing lightweight solutions, we develop HOLNET-UTA, an integrated congestion control and load balancing protocol, achieving TCP-fair resource allocation. HOLNET-UTA is found by simulation to improve the average flow completion time (FCT) by more than 20%, compared to DRILL with DCTCP.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127606257","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 Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification 一种用于网络流量分类的半监督堆叠自编码器方法

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259390

Ons Aouedi, Kandaraj Piamrat, Dhruvjyoti Bagadthey

{"title":"A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification","authors":"Ons Aouedi, Kandaraj Piamrat, Dhruvjyoti Bagadthey","doi":"10.1109/ICNP49622.2020.9259390","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259390","url":null,"abstract":"Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by de-noising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115812201","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: 18

MoGAN: GAN based Next PoA Selection for Proactive Mobility Management MoGAN:基于GAN的主动移动管理下一个PoA选择

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259368

Boyun Jang, S. M. Raza, Moonseong Kim, Hyunseung Choo

{"title":"MoGAN: GAN based Next PoA Selection for Proactive Mobility Management","authors":"Boyun Jang, S. M. Raza, Moonseong Kim, Hyunseung Choo","doi":"10.1109/ICNP49622.2020.9259368","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259368","url":null,"abstract":"Current reactive mobility management in cellular networks becomes a bottleneck for ultra-low latency 5G services and severely degrades the QoS. To satisfy the ultra-low latency requirement of 5G services, proactive mobility management is essential where next PoA of the user is predicted with minimal error. Recent studies have used different deep learning algorithms for this purpose, but their results are unacceptable in real networks due to low accuracy. This paper exploits the distributional learning capability of Generative Adversarial Network (GAN) to propose MoGAN for the prediction of user’s next PoA. The generator in MoGAN uses Gated Recurrent Unit to learn the distribution of time-series data and generates the next PoA. Meanwhile, the discriminator evaluates the generated output against the real data to determine its correctness. The model is trained in adversary mode by using the output from the discriminator. The dataset utilized in training and evaluation is collected from one of the university campuses, and the results show 96.33% of prediction accuracy, which is 5% higher than the previous study. Furthermore, MoGAN is more robust under limited data conditions, as it achieves above 90% accuracy with only 50% of the dataset.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123911638","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: 3

Preventing Route Leaks using a Decentralized Approach: An Experimental Evaluation 使用分散式方法防止路由泄漏:一个实验评估

2020 IEEE 28th International Conference on Network Protocols (ICNP) Pub Date : 2020-10-13 DOI: 10.1109/ICNP49622.2020.9259367

Miquel Ferriol Galmés, Roger Coll Aumatell, A. Cabellos-Aparicio, Shoushou Ren, Xinpeng Wei, Bingyang Liu

{"title":"Preventing Route Leaks using a Decentralized Approach: An Experimental Evaluation","authors":"Miquel Ferriol Galmés, Roger Coll Aumatell, A. Cabellos-Aparicio, Shoushou Ren, Xinpeng Wei, Bingyang Liu","doi":"10.1109/ICNP49622.2020.9259367","DOIUrl":"https://doi.org/10.1109/ICNP49622.2020.9259367","url":null,"abstract":"In the inter-domain routing infrastructure, a route leak is defined as a violation of the routing policy agreed between two Autonomous Systems (AS). Route leaks have resulted in large-scale outages on the Internet, taking down several services. Although route leaks seem a simple problem, the solution is complex because: (i) ASes consider -partially- routing policy private, (ii) lack of a formal and standard language to express routing policy and (iii) BGP lacks adequate cryptographic-based security. In this paper, we present an experimental analysis of a distributed ledger-based architecture that provides a solution to route leaks. Specifically, the routing policy is unambiguously expressed using a formal language, that is then stored in a blockchain. This decentralized architecture allows private policies and interfaces seamlessly with the current BGP infrastructure, requiring no changes to routers. We build a prototype to evaluate our proposed architecture using Hyperledger, we analyze its performance using a real-world BGP dataset. Our results show that our architecture scales linearly with relevant metrics. Additionally, we validate the architecture preventing an artificially introduced route leak in a realistic 10 AS topology.","PeriodicalId":233856,"journal":{"name":"2020 IEEE 28th International Conference on Network Protocols (ICNP)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124612521","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