Dimensioning of V2X Services in 5G Networks through Forecast-based Scaling

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.

[1]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[2]  Adlen Ksentini,et al.  Smart Scaling of the 5G Core Network: An RNN-Based Approach , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[3]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[4]  Jian Li,et al.  Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network , 2019, Future Gener. Comput. Syst..

[5]  Tara N. Sainath,et al.  Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[7]  Koteswararao Kondepu,et al.  Orchestrating Edge- and Cloud-based Predictive Analytics Services , 2020, 2020 European Conference on Networks and Communications (EuCNC).

[8]  Wen Chen,et al.  Dynamic Allocation of 5G Transport Network Slice Bandwidth Based on LSTM Traffic Prediction , 2018, 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS).

[9]  Subutai Ahmad,et al.  Unsupervised real-time anomaly detection for streaming data , 2017, Neurocomputing.

[10]  Adlen Ksentini,et al.  An Efficient and Lightweight Load Forecasting for Proactive Scaling in 5G Mobile Networks , 2018, 2018 IEEE Conference on Standards for Communications and Networking (CSCN).

[11]  Adlen Ksentini,et al.  Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach , 2018, IEEE Network.

[12]  Haitao Li,et al.  Research on prediction of traffic flow based on dynamic fuzzy neural networks , 2015, Neural Computing and Applications.

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

[14]  Koteswararao Kondepu,et al.  Exploiting flexible functional split in converged software defined access networks , 2019, IEEE/OSA Journal of Optical Communications and Networking.

[15]  S. Kourtis,et al.  Modelling cell residence time of mobile terminals in cellular radio systems , 2002 .

[16]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[17]  Marco Fiore,et al.  Measurement-Based Modeling of Interarrivals for the Simulation of Highway Vehicular Networks , 2014, IEEE Communications Letters.

[18]  Mohammad Hossein Anisi,et al.  Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles , 2018, Sensors.

[19]  Yue Wang,et al.  Artificial Intelligence for Elastic Management and Orchestration of 5G Networks , 2019, IEEE Wireless Communications.

[20]  Lee-Ing Tong,et al.  Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..

[21]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[22]  Evangelos Spiliotis,et al.  Statistical and Machine Learning forecasting methods: Concerns and ways forward , 2018, PloS one.

[23]  Mahesh K. Marina,et al.  Network Slicing in 5G: Survey and Challenges , 2017, IEEE Communications Magazine.

[24]  Rashid Mehmood,et al.  Disaster Management in Smart Cities by Forecasting Traffic Plan Using Deep Learning and GPUs , 2017 .

[25]  Paolo Frasconi,et al.  Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

[26]  Yan Tian,et al.  Traffic flow prediction using LSTM with feature enhancement , 2019, Neurocomputing.

[27]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[28]  Arnab K. Pal,et al.  Extreme value statistics of correlated random variables , 2014, 1406.6768.

[29]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[30]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[31]  Barbara Pfeffer,et al.  Smoothing Forecasting And Prediction Of Discrete Time Series , 2016 .