Author ORCID Identifier
Document Type
Article
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences, Communication engineering and systems, telecommunications
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 communitydetection 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 computingbased service placement scheme.
DOI
https://doi.org/10.1109/TNSM.2022.3173913
Recommended Citation
K. Sharma, B. Butler and B. Jennings, "Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters," in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2022.3173913.
Funder
Science Foundation Ireland
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Details
IEEE Transactions on Network and Service Management
To appear, 2022.