Dimensioning V2N services in 5G networks through forecast-based scaling Articles uri icon

authors

  • MARTIN PEREZ, JORGE
  • KONDEPU, KOTESWARARAO
  • Vleeschauwer, Danny De
  • REDDY, VENKATARAMI
  • MAGALHAES GUIMARAES, CARLOS EDUARDO
  • SGAMBELLURI, ANDREA
  • VALCARENGHI, LUCA
  • Papagianni, Chrysa
  • BERNARDOS CANO, CARLOS JESUS

publication date

  • January 2022

start page

  • 9587

end page

  • 9602

volume

  • 10

International Standard Serial Number (ISSN)

  • 2169-3536

Electronic International Standard Serial Number (EISSN)

  • 2169-3536

abstract

  • 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.

subjects

  • Telecommunications

keywords

  • vehicle-to-network; scaling; forecasting; time-series; machine learning