A comprehensive survey of Network Function Virtualization

Yi, Bo; Wang, Xingwei; Li, Keqin; Das, Sajal K.; Huang, Min

doi:10.1016/j.comnet.2018.01.021

Cited by 398 publications

(168 citation statements)

References 89 publications

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“…One way to achieve independence from the link layer is through Network Function Virtualization (NFV), which can dynamically scale the resource reservations based on the flow demands. However, such NFV mechanisms would require hypervisor and control plane management [371]- [375]. NFV also provides a platform for centralized control plane management through the SDN framework.…”

Section: B Deterministic Networking (Detnet)mentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

429

203

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Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

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Section: B Deterministic Networking (Detnet)mentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

429

203

View full text Add to dashboard Cite

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“…The emergence of software‐defined networking (SDN) makes it possible to solve this problem. Different from conventional networks, the control plane and data plane is decoupled, whereas the logically centralized control is adopted in SDN . Moreover, the control plane is programmable to support the flexible deployment of new network functions.…”

Section: Introductionmentioning

confidence: 99%

“…Different from conventional networks, the control plane and data plane is decoupled, whereas the logically centralized control is adopted in SDN. [14][15][16][17][18][19] Moreover, the control plane is programmable to support the flexible deployment of new network functions. The distinguishing features enable SDN to provide support for traffic engineering to improve network performance and simplify network management.…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based network application classification for SDN

Zhang

Wang

et al. 2018

Trans Emerging Tel Tech

Self Cite

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Accurate application classification is important and useful to improve network performance. However, with the continuous expansion of network scale and the rapid increase of network users, it is very difficult for the existing application classification methods to accurately identify and classify network applications. Currently, most classification methods are suitable for small‐scale data sets and cannot achieve high classification accuracy because of the shallow learning structure and the limited learning ability. The emergence of deep learning technology and software‐defined networking (SDN) enables the application classification method to process large‐scale data. In this paper, by leveraging the SDN architecture, we present a novel hybrid deep neural network–based application classification method, which achieves high classification accuracy without the manual feature selection and extraction. In the proposed application classification framework, by taking the advantage of the logical centralized control and powerful computing capability, the massive network traffic is easily collected and processed by the SDN controller. The processed data is used to train the hybrid deep neural network, which is composed of the stacked autoencoder and softmax regression layer. The deep flow features can be obtained from the stacked autoencoder automatically instead of the manual feature selection and extraction. The softmax regression layer is used as the classifier to realize the application classification. Finally, simulation results demonstrate that our proposed classification method is effective and gets higher classification accuracy than the support vector machine–based classification method.

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“…2,3 On one hand, different from the conventional architectures, the control plane is decoupled from the data plane and a logically centralized controller is adopted in SDN. 8,9 More importantly, in NFV, the network service can be represented as a network service function chain. These distinguished features enable SDN to simplify the service deployment and provisioning.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the software-based functional services are decoupled from the hardware-based network equipment (eg, switch and router) in NFV. 8,9 More importantly, in 1. We study the problem of the network services chains deployment across multiple SDN domains and mathematically formulate it as an optimization problem by minimizing the service maintenance cost and network resource consumption cost at the same time.…”

mentioning

confidence: 99%

Network service chains deployment across multiple SDN domains

Zhang

Wang

et al. 2018

Int J Communication

Self Cite

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The integration of software-defined networking (SDN) and network function virtualization (NFV) enables the flexible deployment of network services and significant reduction in service provisioning cost. In particular, SDN, as a new networking paradigm, supports the flexible provisioning of new network services. Meanwhile, in NFV, the service request from end user can be represented as the service function chain (SFC). However, with the continuously increasing of network users and constant enlargement in network scale, the deployment of network services in the large-scale SDN network, which is composed of multiple SDN domains, becomes more challenging since these SDN domains are managed and maintained by different network operators and service providers. To address the above challenge, in this paper, we study the problem of how to deploy network services across multiple SDN domains optimally. Specifically, we first formulate this problem as an optimization problem by jointly minimizing the total number of SDN domains occupied by SFCs and the corresponding network resource consumption cost. We propose a novel heuristic deployment algorithm to solve this optimization problem. The simulation results demonstrate that the proposed heuristic deployment algorithm is effective and outperforms the comparison algorithms in terms of the total service cost. KEYWORDS multidomain network, network service deployment, SDN, SFC Int J Commun Syst. 2018;31:e3826.wileyonlinelibrary.com/journal/dac

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A comprehensive survey of Network Function Virtualization

Cited by 398 publications

References 89 publications

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Deep learning–based network application classification for SDN

Network service chains deployment across multiple SDN domains

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