Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

Nakao, Akihiro; Du, Ping

doi:10.1587/transcom.2017cqi0002

Cited by 38 publications

(22 citation statements)

References 15 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous work [15], [16], we have proposed the deep learning-based application identification architecture, where a small number of customized supervising phones are used to generate training data in real-time and apply deep learning at the packet gateway (P-GW). We reuse the traffic classification architecture to tag the downlink packets with app_name from P-GW to eNB.…”

Section: Designmentioning

confidence: 99%

Intelligent Network Slicing With Edge Computing for Internet of Vehicles

Nakao

Zhong

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this paper, we present an application-specific Multi-Access Edge Computing (MEC) network architecture by leveraging the Control and User Plane Separation (CUPS) in mobile core networks to offload data processing from central servers to edge servers to reduce the transmitted traffic volume and also the response latency of connected vehicle mobility service. We first apply deep learning to classify packets of different applications to different Radio Access Networks (RAN) slices for application-specific spectrum scheduling. Then, we slice Evolved Packet Core (EPC) and deploy EPC data plane slices on-demand for each application and route packets from RAN slices to edge servers. By applying network slicing, multiple RAN, EPC and MEC slices that support different categories of services with different quality of service (QoS) requirements can be deployed in the same physical infrastructure. We prototype the proposed application-specific CUPS architecture using modified open source software OpenAirInterface on our deeply programmable platform. The preliminary experimental results show the feasibility and efficiency of proposed application-specific CUPS architecture, which can achieve a significant decrease in transmission data volume and latency.

show abstract

Section: Designmentioning

confidence: 99%

Intelligent Network Slicing With Edge Computing for Internet of Vehicles

Nakao

Zhong

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Combined with the adaptation of SDN/NFV techniques within 5G networks, deep learning presents an opportunity for accurate identification and classification of mobile applications and automates the creation of adaptive network slicing [28], among other possibilities. Figure 3 shows examples of four common deep learning models, which are explained in the following subsections.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

“…The technique was improved by using social media and other data sources to analyze the effect of key events in a city, such as sporting games, and how this affects the network demand [27]. From here, DL can be used to classify traffic without privacy-invading techniques, such as packet inspection or strict classification based on ports or packet signatures [28]. Once the traffic type is understood, network operators can take advantage of network virtualization to create E2E slices per application and dynamically meet each SLA independently [54], while still achieving optimal resource usage.…”

Section: Slicingmentioning

confidence: 99%

Deep Learning at the Mobile Edge: Opportunities for 5G Networks

2020

View full text Add to dashboard Cite

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

show abstract

“…To overcome this issue, other works propose classifiers based on deep learning, that work directly on input data by automatically distilling structured and complex feature representations at the expense of a higher training complexity and need for larger datasets [14]. In wireless networks, this approach has been considered via variational autoencoder networks [21], convolutional networks [22] or multi-modal classifiers [6] [23]. Nonetheless, as explained above, using SL flow-based classifiers in mobile networks requires a large training dataset and implies installing probes in the core network, which is undesirable for network operators.…”

Section: Related Workmentioning

confidence: 99%

“…Then, more precise traffic classification methods are required. In future 5G networks, identifying the traffic mix will be key to design fine-grained slices with QoE control, mobile edge/multiaccess computing and network functions optimized per service [6].…”

Section: Introductionmentioning

confidence: 99%

Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks

et al. 2020

View full text Add to dashboard Cite

Traffic classification will be a key aspect in the operation of future 5G cellular networks, where services of very different nature will coexist. Unfortunately, data encryption makes this task very difficult. To overcome this issue, flow-based schemes have been proposed based on payload-independent features extracted from the Internet Protocol (IP) traffic flow. However, such an approach relies on the use of expensive traffic probes in the core network. Alternatively, in this paper, an offline method for encrypted traffic classification in the radio interface is presented. The method divides connections per service class by analyzing only features in radio connection traces collected by base stations. For this purpose, it relies on unsupervised learning, namely agglomerative hierarchical clustering. Thus, it can be applied in the absence of labeled data (seldom available in operational cellular networks). Likewise, it can also identify new services launched in the network. Method assessment is performed over a real trace dataset taken from a live Long Term Evolution (LTE) network. Results show that traffic shares per application class estimated by the proposed method are similar to those provided by a vendor report. INDEX TERMS Traffic classification, radio access network, trace, unsupervised learning, clustering.

show abstract

Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

Cited by 38 publications

References 15 publications

Intelligent Network Slicing With Edge Computing for Internet of Vehicles

Intelligent Network Slicing With Edge Computing for Internet of Vehicles

Deep Learning at the Mobile Edge: Opportunities for 5G Networks

Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks

Contact Info

Product

Resources

About