BCOOL: A Novel Blockchain Congestion Control Architecture Using Dynamic Service Function Chaining and Machine Learning for Next Generation Vehicular Networks


This talk will present the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL (BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract modules and ruled by an efficient and reliable communication protocol. BCOOL's first novel module aims at managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fog-based Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their chaining order in the context of SFCs w.r.t users' dynamic QoS priority demands. This talk will demonstrate how BCOOL exhibits a linear complexity and a strong resilience to failures.  Using simulation results I will show that BCOOL outperforms the next best comparable strategies by 80% and 100% reliability and efficiency gains in challenging data congestion environments. I will also show how BCOOL performance yields to fast, reliable and accurate congestion prediction decisions, ahead of time, and how it optimizes transaction validation processing time. Globally, the Byzantine resilience, complexity and attack mitigation strategies along with simulation results proved that BCOOL securely predicts the congestion and provides real-time monitoring, fast and accurate SFC deployment decisions while lowering both capital and operational expenditures (CAPEX/OPEX) costs.


  Date and Time




  • Date: 10 Aug 2021
  • Time: 06:00 PM to 07:00 PM
  • All times are (GMT-05:00) Canada/Eastern
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  • Montreal, Quebec
  • Canada

  • Starts 20 July 2021 11:32 PM
  • Ends 10 August 2021 05:32 PM
  • All times are (GMT-05:00) Canada/Eastern
  • No Admission Charge


Dr. Saida Maaroufi Dr. Saida Maaroufi


SAIDA MAAROUFI (Senior Member, IEEE) received the M.Sc. degree in computer engineering from the Mohammed V University, Morocco, and the Ph.D. degree in computer engineering from the École Polytechnique de Montréal. She is currently a Postdoctoral Fellow with Polytechnique Montreal. Prior to that, she was a Network Engineer with DELL. Her research broadly revolves around wireless and mobile computing with specific interests on context-aware ubiquitous computing, interworking architecture design, machine learning, network virtualization and orchestration, and network security. She serves as the Chair for IEEE Montreal Section and the Vice-Chair for IEEE Women In Engineering (WIE) Canada. She was a recipient of several awards among which are the 2020 IEEE J. J. Archambault Canada Medal Award and the 2016 IEEE Canada Women in Engineering Prize sponsored by the Judy Clift Fund.