Proceedings of the 2018 Workshop on Network Meets AI & ML最新文献

Tracking Groups in Mobile Network Traces 移动网络跟踪中的跟踪组

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229552

Kun Tu, Bruno Ribeiro, A. Swami, D. Towsley

引用次数: 3

Deep-Q: Traffic-driven QoS Inference using Deep Generative Network Deep- q:使用深度生成网络的流量驱动的QoS推理

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229549

Shihan Xiao, Dongdong He, Zhibo Gong

{"title":"Deep-Q: Traffic-driven QoS Inference using Deep Generative Network","authors":"Shihan Xiao, Dongdong He, Zhibo Gong","doi":"10.1145/3229543.3229549","DOIUrl":"https://doi.org/10.1145/3229543.3229549","url":null,"abstract":"In today's IP network, it is important to provide the Quality of Service (QoS) guarantee for network services. However, in real networks with highly dynamic traffic demands, it is difficult to build an accurate QoS model even with a high cost of human expert analysis. In this paper, we present Deep-Q, a data-driven system to learn the QoS model directly from traffic data without human analysis. This function is achieved by utilizing the power of state-of-the-art deep generative networks in the deep learning area. Deep-Q provides a novel inference structure of a variational auto-encoder (VAE) enhanced by the long short-term memory (LSTM). A specially-designed module named Cinfer-loss is further applied to improve the QoS inference accuracy. By training with real traffic data, Deep-Q can infer a variety of QoS metrics over different networks given traffic conditions in real-time. We build testbeds for both the data center network and overlay IP network. Extensive experiments with 5.7TB traffic traces demonstrate that Deep-Q can achieve on average 3x higher inference accuracy than traditional queuing-theory-based solution in real networks while keeping inference time within 100ms.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127395658","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: 46

Adaptive Multiple Non-negative Matrix Factorization for Temporal Link Prediction in Dynamic Networks 动态网络中时间链路预测的自适应多重非负矩阵分解

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229546

Kai Lei, Meng Qin, B. Bai, Gong Zhang

{"title":"Adaptive Multiple Non-negative Matrix Factorization for Temporal Link Prediction in Dynamic Networks","authors":"Kai Lei, Meng Qin, B. Bai, Gong Zhang","doi":"10.1145/3229543.3229546","DOIUrl":"https://doi.org/10.1145/3229543.3229546","url":null,"abstract":"The prediction of mobility, topology and traffic is an effective technique to improve the performance of various network systems, which can be generally represented as the temporal link prediction problem. In this paper, we propose a novel adaptive multiple non-negative matrix factorization (AM-NMF) method from the view of network embedding to cope with such problem. Under the framework of non-negative matrix factorization (NMF), the proposed method embeds the dynamic network into a low-dimensional hidden space, where the characteristics of different network snapshots are comprehensively preserved. Especially, our new method can effectively incorporate the hidden information of different time slices, because we introduce a novel adaptive parameter to automatically adjust the relative contribution of different terms in the uniform model. Accordingly, the prediction result of future network topology can be generated by conducting the inverse process of NMF form the shared hidden space. Moreover, we also derive the corresponding solving strategy whose convergence can be ensured. As an illustration, the new model will be applied to various network datasets such as human mobility networks, vehicle mobility networks, wireless mesh networks and data center networks. Experimental results show that our method outperforms some other state-of-the-art methods for the temporal link prediction of both unweighted and weighted networks.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133717165","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: 17

IFS-RL: An Intelligent Forwarding Strategy Based on Reinforcement Learning in Named-Data Networking 命名数据网络中基于强化学习的智能转发策略

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229547

Yi Zhang, B. Bai, Kuai Xu, Kai Lei

{"title":"IFS-RL: An Intelligent Forwarding Strategy Based on Reinforcement Learning in Named-Data Networking","authors":"Yi Zhang, B. Bai, Kuai Xu, Kai Lei","doi":"10.1145/3229543.3229547","DOIUrl":"https://doi.org/10.1145/3229543.3229547","url":null,"abstract":"Named-Data Networking (NDN) is a new communication paradigm where network primitives are based on named-data rather than host identifiers. Compared with IP, NDN has a unique feature that forwarding plane enables each router to select the next forwarding hop independently without relying on routing. Therefore, forwarding strategies play a significant role for adaptive and efficient data transmission in NDN. Most of the existing forwarding strategies use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and application demands. In this paper, We propose IFS-RL, an intelligent forwarding strategy based on reinforcement learning. IFS-RL trains a neural network model which chooses appropriate interfaces for the forwarding of Interest based on observations collected by routing node. Not relying on pre-programmed models, IFS-RL learns to make decisions solely through observations of the resulting performance of past decisions. Therefore, IFS-RL can implement intelligent forwrarding which adapt to a wide range of network conditions. Besides, we also researches the learning granularity and the enhancement for network topology change. We compare IFS-RL to state-of-the-art forwarding strategies in ndnSIM. Experimental results show that IFS-RL can achieve higher throughput and lower packet drop rates.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121418413","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: 15

Empowering Sketches with Machine Learning for Network Measurements 授权草图与机器学习网络测量

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229545

Tong Yang, Lun Wang, Yulong Shen, Muhammad Shahzad, Qun Huang, Xiaohong Jiang, Kun Tan, Xiaoming Li

引用次数: 24

Improving TCP Congestion Control with Machine Intelligence 利用机器智能改进TCP拥塞控制

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229550

Yiming Kong, H. Zang, Xiaoli Ma

引用次数: 61

HiPS: Hierarchical Parameter Synchronization in Large-Scale Distributed Machine Learning HiPS:大规模分布式机器学习中的分层参数同步

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229544

Jinkun Geng, Dan Li, Yang Cheng, Shuai Wang, Junfeng Li

引用次数: 25

Assessing the Impact of Network Events with User Feedback 用用户反馈评估网络事件的影响

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229553

Shobha Venkataraman, Jia Wang

引用次数: 4

Efficient Distribution-Derived Features for High-Speed Encrypted Flow Classification 高速加密流分类的高效分布衍生特征

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229548

Johan Garcia, Topi Korhonen

{"title":"Efficient Distribution-Derived Features for High-Speed Encrypted Flow Classification","authors":"Johan Garcia, Topi Korhonen","doi":"10.1145/3229543.3229548","DOIUrl":"https://doi.org/10.1145/3229543.3229548","url":null,"abstract":"Flow classification is an important tool to enable efficient network resource usage, support traffic engineering, and aid QoS mechanisms. As traffic is increasingly becoming encrypted by default, flow classification is turning towards the use of machine learning methods employing features that are also available for encrypted traffic. In this work we evaluate flow features that capture the distributional properties of in-flow per-packet metrics such as packet size and inter-arrival time. The characteristics of such distributions are often captured with general statistical measures such as standard deviation, variance, etc. We instead propose a Kolmogorov-Smirnov discretization (KSD) algorithm to perform histogram bin construction based on the distributional properties observed in the data. This allows for a richer, histogram based, representation which also requires less resources for feature computation than higher order statistical moments. A comprehensive evaluation using synthetic data from Gaussian and Beta mixtures show that the KSD approach provides Jensen-Shannon distance results surpassing those of uniform binning and probabilistic binning. An empirical evaluation using live traffic traces from a cellular network further shows that when coupled with a random forest classifier the KSD-constructed features improve classification performance compared to general statistical features based on higher order moments, or alternative bin placement approaches.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122404260","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

DeepCache: A Deep Learning Based Framework For Content Caching DeepCache:基于深度学习的内容缓存框架

Proceedings of the 2018 Workshop on Network Meets AI & ML Pub Date : 2018-08-07 DOI: 10.1145/3229543.3229555

Arvind Narayanan, Saurabh Verma, Eman Ramadan, Pariya Babaie, Zhi-Li Zhang

引用次数: 115