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

TL;DR

This paper presents a graph-based heuristic for optimal placement of distributed video processing applications on moving vehicle clusters in vehicular fog computing, enhancing resource efficiency and reducing latency.

Contribution

It introduces a novel graph-based heuristic for service placement in vehicular fog computing, considering vehicle mobility, cluster stability, and resource optimization.

Findings

Heuristic outperforms integer linear programming and first-fit in resource utilization.

Approach reduces service latency for safety-critical applications.

Utilizes real vehicle density data for realistic mobility modeling.

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 on-board 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 are 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 minimise resource usage. We compare our heuristic to an integer linear program 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, which is a crucial requirement for safety-related applications.