AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems

Yong Xiao, Marwan Krunz

Research output: Contribution to journalArticlepeer-review

Abstract

Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month urban measurement study of the wireless access latency between connected vehicles and fog computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that a tolerable latency threshold for each supported type of service can be guaranteed. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two practical scenarios with finite- and infinite-horizon decision making processes for optimizing the short-term and long-term confidence are investigated. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30% to 50% improvement in the confidence level of fog/cloud latency.

Original languageEnglish (US)
JournalIEEE Transactions on Mobile Computing
DOIs
StateAccepted/In press - 2021

Keywords

  • cloud computing
  • Cloud computing
  • Computational modeling
  • connected vehicle
  • Edge computing
  • Fog computing
  • Long Term Evolution
  • low-latency
  • measurement study
  • Servers
  • Switches
  • Wireless communication

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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