Big Data for 5G Intelligent Network Slicing Management

Network slicing is a powerful tool to harness the full potential of 5G systems. It allows verticals to own and exploit independent logical networks on top of the same physical infrastructure. Motivated by the emergence of the big data paradigm, this article focuses on the enablers of big-databased intelligent network slicing. The article starts by revisiting the architecture of this technology that consists of data collection, storage, processing, and analytics before it highlights their relationship with network slicing concepts and the underlying trade-offs. It then proposes a complete framework for implementing big-data-driven dynamic slicing resource provisioning while respecting SLAs. This includes the development of low-complexity slices' traffic predictors, resource allocation models, and SLA enforcement via constrained deep learning. The article finally identifies the key challenges and open research directions in this emerging area.

[1]  Anna Brunstrom,et al.  SDN/NFV-Based Mobile Packet Core Network Architectures: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[2]  Marco Gramaglia,et al.  Mobile traffic forecasting for maximizing 5G network slicing resource utilization , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[3]  Mostafa Bellafkih,et al.  Real-time data analysis using Spark and Hadoop , 2018, 2018 4th International Conference on Optimization and Applications (ICOA).

[4]  Marco Fiore,et al.  DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[5]  Maya R. Gupta,et al.  Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints , 2018, ICML.

[6]  Wei Hu,et al.  A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks , 2018, ICLR.

[7]  Marco Fiore,et al.  How Should I Slice My Network?: A Multi-Service Empirical Evaluation of Resource Sharing Efficiency , 2018, MobiCom.

[8]  Stephen Marshall,et al.  Activation Functions: Comparison of trends in Practice and Research for Deep Learning , 2018, ArXiv.

[9]  J. Yosinski,et al.  Time-series Extreme Event Forecasting with Neural Networks at Uber , 2017 .

[10]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[11]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[12]  Paul Patras,et al.  Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks , 2017, MobiHoc.