2019 15th International Conference on Network and Service Management (CNSM)最新文献_第2页

Predicting VNF Deployment Decisions under Dynamically Changing Network Conditions 在动态变化的网络条件下预测VNF部署决策

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012734

Stanislav Lange, Heegon Kim, Seyeon Jeong, Heeyoul Choi, Jae-Hyung Yoo, J. W. Hong

{"title":"Predicting VNF Deployment Decisions under Dynamically Changing Network Conditions","authors":"Stanislav Lange, Heegon Kim, Seyeon Jeong, Heeyoul Choi, Jae-Hyung Yoo, J. W. Hong","doi":"10.23919/CNSM46954.2019.9012734","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012734","url":null,"abstract":"In addition to providing network operators with benefits in terms of flexibility and cost efficiency, softwarization paradigms like SDN and NFV are key enablers for the concept of Service Function Chaining (SFC). The corresponding networks need to support a wide range of services and applications with highly heterogeneous requirements that change dynamically during the network’s lifetime. Hence, efficient management and operation of such networks requires a high degree of automation that is paired with fast and proactive decisions in order to cope with these phenomena.In particular, determining the optimal number of VNF instances that is required for accommodating current and upcoming demands is a crucial task that also affects subsequent management decisions. To enable fast and proactive decisions in this context, we propose a machine learning-based approach that uses recent monitoring data to predict whether to adapt the current number of VNF instances of a given type. Furthermore, we present a methodology for generating labeled training data that reflects temporal dynamics and heterogeneous demands of real world networks. We demonstrate the feasibility of the approach using two different network topologies that represent WAN and mobile edge computing use cases, respectively. Additionally, we investigate how well the models generalize among networks and provide guidelines regarding the prediction horizon, i.e., how far ahead predictions can be performed in a reliable manner.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991006","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

Meta-Learning-Based Deep Learning Model Deployment Scheme for Edge Caching 基于元学习的边缘缓存深度学习模型部署方案

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012733

K. Thar, Thant Zin Oo, Zhu Han, C. Hong

引用次数: 6

P4-BNG: Central Office Network Functions on Programmable Packet Pipelines P4-BNG:基于可编程分组管道的中央局点网络功能

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012666

Ralf Kundel, Leonhard Nobach, Jeremias Blendin, Hans-Joerg Kolbe, Georg Schyguda, V. Gurevich, B. Koldehofe, R. Steinmetz

{"title":"P4-BNG: Central Office Network Functions on Programmable Packet Pipelines","authors":"Ralf Kundel, Leonhard Nobach, Jeremias Blendin, Hans-Joerg Kolbe, Georg Schyguda, V. Gurevich, B. Koldehofe, R. Steinmetz","doi":"10.23919/CNSM46954.2019.9012666","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012666","url":null,"abstract":"Large-scale telecommunications providers have to continuously challenge and evolve their network infrastructure to efficiently serve growing markets demands. They must increase performance, lower time-to-market, provide new services, and lower the cost of the infrastructure and its operation. Network Functions Virtualization (NFV) on commodity hardware offers an attractive, low-cost platform to establish innovations much faster than with purpose-built hardware products. Unfortunately, implementing NFV on commodity processors does not match the performance requirements of the high-throughput data plane components in large carrier access networks. In this article, we propose a way to offer residential network access with programmable packet processing architectures. Based on the highly flexible P4 programming language, we present a design and open source implementation of a BNG data plane that meets the challenging demands of Broadband Network Gateways in carrier-grade environments. The proposed evaluation results show the desired performance characteristics and our proposed design together with upcoming P4 hardware can offer a giant leap towards highest performance NFV network access.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129411795","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

A Framework & System for Classification of Encrypted Network Traffic using Machine Learning 基于机器学习的加密网络流量分类框架与系统

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012662

N. Seddigh, B. Nandy, Don Bennett, Yongli Ren, S. Dolgikh, Colin Zeidler, Juhandre Knoetze, Naveen Sai Muthyala

{"title":"A Framework & System for Classification of Encrypted Network Traffic using Machine Learning","authors":"N. Seddigh, B. Nandy, Don Bennett, Yongli Ren, S. Dolgikh, Colin Zeidler, Juhandre Knoetze, Naveen Sai Muthyala","doi":"10.23919/CNSM46954.2019.9012662","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012662","url":null,"abstract":"Traffic classification solutions are widely used by network operators and law enforcement agencies (LEA) for application identification. Widespread use of encryption reduces the accuracy of traditional traffic classification solutions such as DPI (Deep Packet Inspection). Machine Learning based solutions offer promise to fill the gap. However, enabling such systems to operate accurately in high speed networks remains a challenge. This paper makes multiple contributions. First, we report on the development of MLTAT, a high speed network classification platform which integrates DPI and machine learning and which supports flexible deployment of binary or multi-class classification solutions. Second, we identify a set of robust features which fulfill a dual-constraint - support 10Gbps computation rates and sufficient accuracy in the supervised machine learning models proposed for network traffic classification. Third, we develop a set of labeled data suitable for training the system and a framework for larger scale ground truth generation using co-training. Our findings indicate detection rates around 90% across 8 traffic classes, benchmarked in the system at 10Gbps rates.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128567967","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

Bandwidth Prediction Schemes for Defining Bitrate Levels in SDN-enabled Adaptive Streaming 在支持sdn的自适应流中定义比特率水平的带宽预测方案

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012713

Ali Edan Al-Issa, A. Bentaleb, A. Barakabitze, T. Zinner, B. Ghita

{"title":"Bandwidth Prediction Schemes for Defining Bitrate Levels in SDN-enabled Adaptive Streaming","authors":"Ali Edan Al-Issa, A. Bentaleb, A. Barakabitze, T. Zinner, B. Ghita","doi":"10.23919/CNSM46954.2019.9012713","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012713","url":null,"abstract":"The majority of Internet video traffic today is delivered via HTTP Adaptive Streaming (HAS). Recent studies concluded that pure client-driven HAS adaptation is likely to be sub-optimal, given clients adjust quality based on local feedback. In [1], we introduced a network-assisted streaming architecture (BBGDASH) that provides bounded bitrate guidance for a video client while preserving quality control and adaptation at the client. Although BBGDASH is an efficient approach for video delivery, deploying it in a wireless network environment could result in sub-optimal decisions due to the high fluctuations. To this end, we propose in this paper an intelligent streaming architecture (denoted BBGDASH+), which leverages the power of time series forecasting to allow for an accurate and scalable networkbased guidance. Further, we conduct an initial investigation of parameter settings for the forecasting algorithms in a wireless testbed. Overall, the experimental results indicate the potential of the proposed approach to improve video delivery in wireless network conditions.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"595 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123144721","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: 10

CNSM 2019 Index

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/cnsm46954.2019.9012724

引用次数: 0

NFV-VIPP: Catching Internal Figures of Packet Processing for Accelerating Development and Operations of NFV-nodes NFV-VIPP:捕捉数据包处理的内部数据，加速nfv节点的发展和运营

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012728

Masahiro Dodare, Yuki Taguchi, Ryota Kawashima, Hiroki Nakayama, Tsunemasa Hayashi, H. Matsuo

{"title":"NFV-VIPP: Catching Internal Figures of Packet Processing for Accelerating Development and Operations of NFV-nodes","authors":"Masahiro Dodare, Yuki Taguchi, Ryota Kawashima, Hiroki Nakayama, Tsunemasa Hayashi, H. Matsuo","doi":"10.23919/CNSM46954.2019.9012728","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012728","url":null,"abstract":"Server-based NFV-nodes have disparate internals, such as simultaneous deployment of Virtual Network Functions (VNFs) and layered software abstractions including a virtual switch. The traditional operations tailored for function-hardware-coupled devices cannot cope with the increase of related components as well as complicated packet forwarding paths inside. Besides, self-development of VNFs attracting Telcos is still highly complicated work, due to lack of exact troubleshooting of internal NFV-nodes caused by exclusive resource management by Data-Plane Development Kit (DPDK). OPNFV Barometer provides means of stats acquisition, but internal figures of packet processing are still unveiled. In this paper, we propose an integrated metrics collection framework (NFV-VIPP) specialized to NFV-nodes. NFV-VIPP provides seamless understandings of system components in a node, and reveals the inside by transparently exposing implementation-related metrics. NFV-VIPP can be incorporated into Barometer/collectd via RESTful APIs to reinforce system visibility, meaning that our framework bridges NFV-node internals to existing management frameworks. We explore NFV-node management using intra-VNF metrics obtained by NFVVIPP. Specifically, we prove that CPU-cycle consumption of inter-receive-polling is a driving force to estimate system load.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121266313","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

Concurrent Traffic Queuing Game in Smart Home 智能家居中的并发交通排队博弈

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012720

Maroua Ben Attia, K. Nguyen, M. Cheriet

引用次数: 2

Towards Content-Centric Control Plane Supporting Efficient Anomaly Detection Functions 支持高效异常检测功能的以内容为中心的控制平面

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012668

H. Mai, G. Doyen, Wissam Mallouli, Edgardo Montes de Oca, O. Festor

{"title":"Towards Content-Centric Control Plane Supporting Efficient Anomaly Detection Functions","authors":"H. Mai, G. Doyen, Wissam Mallouli, Edgardo Montes de Oca, O. Festor","doi":"10.23919/CNSM46954.2019.9012668","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012668","url":null,"abstract":"Anomaly detection remains a challenging task due to both the ever more complex functions that need to be executed and the evolution of current networking devices which induces limitation of computational resources such as the Internet of Things (IoT). Furthermore, results of anomaly function computations can be repeated gradually over time or executed in neighboring nodes, thus leading to a waste of such limited computing resources in constrained nodes. To tackle these issues, the content-centric paradigm enhanced with computing features offers a promising solution to reduce the computation resources and finally improve the scalability of anomaly detection functions. In this paper, we propose a first step toward a content-oriented control plane which enables the distribution of the processing and the sharing of results of anomaly detection functions in the network. We present the way we leverage NFN to support Bayesian Network inference to detect anomalies in network traffic. The relevance and performance of our proposed approach are demonstrated by considering the Content Poisoning Attack (CPA) through numerous experiment data.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125566473","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

Quantitative Analysis of Dynamically Provisioned Heterogeneous Network Services 动态配置异构网络服务的定量分析

2019 15th International Conference on Network and Service Management (CNSM) Pub Date : 2019-10-01 DOI: 10.23919/CNSM46954.2019.9012659

Hadi Razzaghi Kouchaksaraei, H. Karl

引用次数: 2