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Can vehicular cloud replace edge computing?

Rosario Patanè, Nadjib Achir, Andrea Araldo, Lila Boukhatem

TL;DR

This paper investigates whether Vehicular Cloud Computing (VCC) can substitute Edge Computing (EC) for latency-sensitive tasks in dense urban environments. It introduces a three-layer Cloud-Edge-VCC model with a centralized Controller at an AP that routes tasks to EC, VCC, or CC based on workload, vehicle availability, and latency constraints, and evaluates configurations via NS-3 and SUMO simulations. The key findings show that VCC can replace EC for many low-latency tasks (LL and LL+) in scenarios with sufficient vehicle density and resource distribution, while very high or ultra-low latency requirements (LL++) still rely on EC; the transition depends on workload, the number of end users, vehicles, and per-vehicle compute. A high-level cost analysis indicates significant operator savings with VCC compared to EC, driven by leveraging in-place vehicular resources and avoiding extensive EC deployment. The work highlights practical implications for urban deployments and identifies directions for future work, including intelligent vehicle selection, higher-speed mobility scenarios, and larger-scale urban/hybrid environments.

Abstract

Edge computing (EC) consists of deploying computation resources close to the users, thus enabling low-latency applications, such as augmented reality and online gaming. However, large-scale deployment of edge nodes can be highly impractical and expensive. Besides EC, there is a rising concept known as Vehicular Cloud Computing (VCC). VCC is a computing paradigm that amplifies the capabilities of vehicles by exploiting part of their computational resources, enabling them to participate in services similar to those provided by the EC. The advantage of VCC is that it can opportunistically exploit part of the computation resources already present on vehicles, thus relieving a network operator from the deployment and maintenance cost of EC nodes. However, it is still unknown under which circumstances VCC can enable low-latency applications without EC. In this work, we show that VCC has the potential to effectively supplant EC in urban areas, especially given the higher density of vehicles in such environments. The goal of this paper is to analyze, via simulation, the key parameters determining the conditions under which this substitution of EC by VCC is feasible. In addition, we provide a high level cost analysis to show that VCC is much less costly for a network operator than adopting EC.

Can vehicular cloud replace edge computing?

TL;DR

This paper investigates whether Vehicular Cloud Computing (VCC) can substitute Edge Computing (EC) for latency-sensitive tasks in dense urban environments. It introduces a three-layer Cloud-Edge-VCC model with a centralized Controller at an AP that routes tasks to EC, VCC, or CC based on workload, vehicle availability, and latency constraints, and evaluates configurations via NS-3 and SUMO simulations. The key findings show that VCC can replace EC for many low-latency tasks (LL and LL+) in scenarios with sufficient vehicle density and resource distribution, while very high or ultra-low latency requirements (LL++) still rely on EC; the transition depends on workload, the number of end users, vehicles, and per-vehicle compute. A high-level cost analysis indicates significant operator savings with VCC compared to EC, driven by leveraging in-place vehicular resources and avoiding extensive EC deployment. The work highlights practical implications for urban deployments and identifies directions for future work, including intelligent vehicle selection, higher-speed mobility scenarios, and larger-scale urban/hybrid environments.

Abstract

Edge computing (EC) consists of deploying computation resources close to the users, thus enabling low-latency applications, such as augmented reality and online gaming. However, large-scale deployment of edge nodes can be highly impractical and expensive. Besides EC, there is a rising concept known as Vehicular Cloud Computing (VCC). VCC is a computing paradigm that amplifies the capabilities of vehicles by exploiting part of their computational resources, enabling them to participate in services similar to those provided by the EC. The advantage of VCC is that it can opportunistically exploit part of the computation resources already present on vehicles, thus relieving a network operator from the deployment and maintenance cost of EC nodes. However, it is still unknown under which circumstances VCC can enable low-latency applications without EC. In this work, we show that VCC has the potential to effectively supplant EC in urban areas, especially given the higher density of vehicles in such environments. The goal of this paper is to analyze, via simulation, the key parameters determining the conditions under which this substitution of EC by VCC is feasible. In addition, we provide a high level cost analysis to show that VCC is much less costly for a network operator than adopting EC.
Paper Structure (19 sections, 3 equations, 4 figures, 2 tables)

This paper contains 19 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. System Model
  4. Offloading architecture
  5. Considered offloading scenarios and network protocols
  6. Task model
  7. Cloud model
  8. Edge model
  9. Vehicular cloud model
  10. Simulation results
  11. Simulation environment
  12. Impact of the number of end users
  13. Impact of the number of vehicles
  14. Impact of the computational resources deployed into vehicles
  15. Impact of task workload
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Model architecture
  • Figure 2: Left: Average offloading time ($T^i_\text{EC}, T^i_\text{VCC}$) of ECFirst and VCCFirst scenarios. Right: Contribution of the different components of the offloading time in the VCCFirst scenario.
  • Figure 3: Offloading time of VCCFirst scenario. Left: Impact of the number of vehicles. Right: Impact of computational capacity $C_\text{VCC}$ of each vehicle.
  • Figure 4: Left: ECFirst and VCCFirst offloading time. Right: The VCC offloading time for the VCCFirst strategy.