Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012734
Stanislav Lange, Heegon Kim, Seyeon Jeong, Heeyoul Choi, Jae-Hyung Yoo, J. W. Hong
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.
{"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}
Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012733
K. Thar, Thant Zin Oo, Zhu Han, C. Hong
Recently, with big data and high computing power, deep learning models have achieved high accuracy in prediction problems. However, the challenging issues of utilizing deep learning into the content’s popularity prediction remains open. The first issue is how to pick the best-suited neural network architecture among the numerous types of deep learning architectures (e.g., Feed-forward Neural Networks, Recurrent Neural Networks, etc.). The second issue is how to optimize the hyperparameters (e.g., number of hidden layers, neurons, etc.) of the chosen neural network. Therefore, we propose the reinforcement (Q-Learning) meta-learning based deep learning model deployment scheme to construct the best-suited model for predicting content’s popularity autonomously. Also, we added the feedback mechanism to update the Q-Table whenever the base station calibrates the model to find out more appropriate prediction model. The experiment results show that the proposed scheme outperforms existing algorithms in many key performance indicators, especially in content hit probability and access delay.
{"title":"Meta-Learning-Based Deep Learning Model Deployment Scheme for Edge Caching","authors":"K. Thar, Thant Zin Oo, Zhu Han, C. Hong","doi":"10.23919/CNSM46954.2019.9012733","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012733","url":null,"abstract":"Recently, with big data and high computing power, deep learning models have achieved high accuracy in prediction problems. However, the challenging issues of utilizing deep learning into the content’s popularity prediction remains open. The first issue is how to pick the best-suited neural network architecture among the numerous types of deep learning architectures (e.g., Feed-forward Neural Networks, Recurrent Neural Networks, etc.). The second issue is how to optimize the hyperparameters (e.g., number of hidden layers, neurons, etc.) of the chosen neural network. Therefore, we propose the reinforcement (Q-Learning) meta-learning based deep learning model deployment scheme to construct the best-suited model for predicting content’s popularity autonomously. Also, we added the feedback mechanism to update the Q-Table whenever the base station calibrates the model to find out more appropriate prediction model. The experiment results show that the proposed scheme outperforms existing algorithms in many key performance indicators, especially in content hit probability and access delay.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"33 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":"124431796","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}
Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012666
Ralf Kundel, Leonhard Nobach, Jeremias Blendin, Hans-Joerg Kolbe, Georg Schyguda, V. Gurevich, B. Koldehofe, R. Steinmetz
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.
{"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}
Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012662
N. Seddigh, B. Nandy, Don Bennett, Yongli Ren, S. Dolgikh, Colin Zeidler, Juhandre Knoetze, Naveen Sai Muthyala
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.
{"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}
Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012713
Ali Edan Al-Issa, A. Bentaleb, A. Barakabitze, T. Zinner, B. Ghita
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.
{"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}
Pub Date : 2019-10-01DOI: 10.23919/cnsm46954.2019.9012724
{"title":"CNSM 2019 Index","authors":"","doi":"10.23919/cnsm46954.2019.9012724","DOIUrl":"https://doi.org/10.23919/cnsm46954.2019.9012724","url":null,"abstract":"","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"4 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":"126402100","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}
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.
基于服务器的nfv节点具有不同的内部结构，例如同时部署虚拟网络功能(VNFs)和分层软件抽象(包括虚拟交换机)。针对功能硬件耦合设备量身定制的传统操作无法应对相关组件的增加和设备内部复杂的报文转发路径。此外，由于数据平面开发工具包(Data-Plane Development Kit, DPDK)对资源的独家管理，导致nfv内部节点无法进行精确的故障排除，吸引电信运营商的vnf自主开发仍然是一项非常复杂的工作。OPNFV晴雨表提供了统计数据采集的手段，但内部数据的分组处理仍然是公开的。在本文中，我们提出了一个专门针对nfv节点的集成度量收集框架(NFV-VIPP)。NFV-VIPP提供了对节点中系统组件的无缝理解，并通过透明地公开与实现相关的指标来揭示内部情况。NFV-VIPP可以通过RESTful api集成到Barometer/收集中，以增强系统可见性，这意味着我们的框架将nfv节点内部连接到现有的管理框架。我们使用NFVVIPP获得的内部vnf指标来探索nfv节点管理。具体来说，我们证明了接收间轮询的cpu周期消耗是估计系统负载的驱动力。
{"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}
Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012667
Joseph McNamara, Liam Fallon, Enda Fallon
Services such as interactive video and real time gaming are ubiquitous on modern networks. The approaching realisation of 5G as well as the virtualisation and scalability of network functions made possible by technologies such as NFV and Kubernetes pushes the frontiers of what applications can do and how they can be deployed. However, managing such intangible services is a real challenge for network management systems. Adaptive Policy is an approach that can be applied to govern such services in an intent-based manner.In this work, we are exploring if the manner in which such services are deployed, virtualized, and scaled can be guided using real time context aware decision making. We are investigating how to apply Adaptive Policy to the problem of optimizing interactive video streaming delivery in a virtualized environment. We utilise components of our previously established test bed framework and implement a single layer neural network through Adaptive Policy, in which weights assigned to network metrics are continuously adjusted through supervised test cycles, resulting in weights in proportion to their associated impact on our video stream quality. We present the initial test results from our Perceptron inspired policy-based approach to video quality optimisation through weighted network resource evaluation.
{"title":"A Hybrid Machine Learning/Policy Approach to Optimise Video Path Selection","authors":"Joseph McNamara, Liam Fallon, Enda Fallon","doi":"10.23919/CNSM46954.2019.9012667","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012667","url":null,"abstract":"Services such as interactive video and real time gaming are ubiquitous on modern networks. The approaching realisation of 5G as well as the virtualisation and scalability of network functions made possible by technologies such as NFV and Kubernetes pushes the frontiers of what applications can do and how they can be deployed. However, managing such intangible services is a real challenge for network management systems. Adaptive Policy is an approach that can be applied to govern such services in an intent-based manner.In this work, we are exploring if the manner in which such services are deployed, virtualized, and scaled can be guided using real time context aware decision making. We are investigating how to apply Adaptive Policy to the problem of optimizing interactive video streaming delivery in a virtualized environment. We utilise components of our previously established test bed framework and implement a single layer neural network through Adaptive Policy, in which weights assigned to network metrics are continuously adjusted through supervised test cycles, resulting in weights in proportion to their associated impact on our video stream quality. We present the initial test results from our Perceptron inspired policy-based approach to video quality optimisation through weighted network resource evaluation.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"88 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":"134106683","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}
Pub Date : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012673
Tom Goethals, D. Kerkhove, B. Volckaert, F. Turck
For years, containers have been a popular choice for lightweight virtualization in the cloud. With the rise of more powerful and flexible edge devices, container deployment strategies have arisen that leverage the computational power of edge devices for optimal workload distribution. This move from a secure data center network to heterogenous public and private networks presents some issues in terms of security and network topology that can be partially solved by using a Virtual Private Network (VPN) to connect edge nodes to the cloud. In this paper, the scalability of VPN software is evaluated to determine if and how it can be used in large-scale clusters containing edge nodes. Benchmarks are performed to determine the maximum number of VPN-connected nodes and the influence of network degradation on VPN performance, primarily using traffic typical for edge devices generating IoT data. Some high level conclusions are drawn from the results, indicating that WireGuard is an excellent choice of VPN software to connect edge nodes in a cluster. Analysis of the results also shows the strengths and weaknesses of other VPN software.
{"title":"Scalability evaluation of VPN technologies for secure container networking","authors":"Tom Goethals, D. Kerkhove, B. Volckaert, F. Turck","doi":"10.23919/CNSM46954.2019.9012673","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012673","url":null,"abstract":"For years, containers have been a popular choice for lightweight virtualization in the cloud. With the rise of more powerful and flexible edge devices, container deployment strategies have arisen that leverage the computational power of edge devices for optimal workload distribution. This move from a secure data center network to heterogenous public and private networks presents some issues in terms of security and network topology that can be partially solved by using a Virtual Private Network (VPN) to connect edge nodes to the cloud. In this paper, the scalability of VPN software is evaluated to determine if and how it can be used in large-scale clusters containing edge nodes. Benchmarks are performed to determine the maximum number of VPN-connected nodes and the influence of network degradation on VPN performance, primarily using traffic typical for edge devices generating IoT data. Some high level conclusions are drawn from the results, indicating that WireGuard is an excellent choice of VPN software to connect edge nodes in a cluster. Analysis of the results also shows the strengths and weaknesses of other VPN software.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"62 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463498","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}
Pub Date : 2019-10-01DOI: 10.23919/cnsm46954.2019.9012712
Pan Zhao, Xiaoyang Li, Lei Feng, Qinghui Zhang, Weidong Yang, Fei Zheng
To meet the immensely diverse service requirements, heterogeneous cloud radio access network (H-CRAN) architecture and D2D communication is embraced. Consequently, the resource allocation between D2D pairs and current users is a challenge. In this paper, a joint power control and sub-channel allocation scheme is proposed. The original mixed-integer nonlinear programming problem is decomposed into power and sub-channel allocation. Geometric Vertex Search approach and 3-dimensional (3-D) matching method are used to solve them. Finally, numerical results verify the proposed scheme has about 35% and 60% improvement in total throughput comparing with other approaches.
{"title":"3-D Matching-based Resource Allocation for D2D Communications in H-CRAN Network","authors":"Pan Zhao, Xiaoyang Li, Lei Feng, Qinghui Zhang, Weidong Yang, Fei Zheng","doi":"10.23919/cnsm46954.2019.9012712","DOIUrl":"https://doi.org/10.23919/cnsm46954.2019.9012712","url":null,"abstract":"To meet the immensely diverse service requirements, heterogeneous cloud radio access network (H-CRAN) architecture and D2D communication is embraced. Consequently, the resource allocation between D2D pairs and current users is a challenge. In this paper, a joint power control and sub-channel allocation scheme is proposed. The original mixed-integer nonlinear programming problem is decomposed into power and sub-channel allocation. Geometric Vertex Search approach and 3-dimensional (3-D) matching method are used to solve them. Finally, numerical results verify the proposed scheme has about 35% and 60% improvement in total throughput comparing with other approaches.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"47 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":"133474824","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}
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