Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Kanika Sharma, Bernard Butler, Brendan Jennings

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computing-based service placement scheme.

Original languageEnglish
Pages (from-to)3076-3089
Number of pages14
JournalIEEE Transactions on Network and Service Management
Volume19
Issue number3
DOIs
Publication statusPublished - 01 Sep 2022

Keywords

  • Computational modeling
  • Flexible Service Model
  • Fog Computing
  • Intelligent Transport Systems
  • Internet of Things
  • Mathematical models
  • Resource Allocation.
  • Resource management
  • Sensors
  • Servers
  • Service Placement
  • Task analysis
  • Vehicle dynamics
  • Vehicular Cloud Computing
  • Vehicular Fog Computing (VFC)
  • Fog computing
  • vehicular fog computing (VFC)
  • flexible service model
  • resource allocation
  • service placement
  • vehicular cloud computing
  • intelligent transport systems

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