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Modeling Edge-to-Cloud Offloading Workloads for Autonomous Vehicles

Longkun Li, Evangelos Pournaras

Abstract

Autonomous vehicles generate large volumes of data for applications such as fleet monitoring, model retraining, and high-definition map updates. Existing studies often rely on generic traffic traces, which do not capture the characteristics of autonomous driving workloads. This paper proposes a system-level workload modeling framework for vehicle-to-cloud data. We classify offloaded data into three types: telemetry, event-driven fleet learning, and high-definition map updates, while we model their generation using a parameterized formulation based on empirical data. Using a real-world mobility trace from Munich, we analyze the resulting workloads over time and space. The results show that workload scales with vehicle penetration, exhibits temporal structure and spatial imbalance across access points, and is distinguished from baseline traffic models.

Modeling Edge-to-Cloud Offloading Workloads for Autonomous Vehicles

Abstract

Autonomous vehicles generate large volumes of data for applications such as fleet monitoring, model retraining, and high-definition map updates. Existing studies often rely on generic traffic traces, which do not capture the characteristics of autonomous driving workloads. This paper proposes a system-level workload modeling framework for vehicle-to-cloud data. We classify offloaded data into three types: telemetry, event-driven fleet learning, and high-definition map updates, while we model their generation using a parameterized formulation based on empirical data. Using a real-world mobility trace from Munich, we analyze the resulting workloads over time and space. The results show that workload scales with vehicle penetration, exhibits temporal structure and spatial imbalance across access points, and is distinguished from baseline traffic models.
Paper Structure (14 sections, 10 equations, 6 figures, 2 tables)

This paper contains 14 sections, 10 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Research Gap and Motivation
  3. Research Gap
  4. Motivation
  5. Workload Modeling Framework
  6. System Overview
  7. Model Parameters
  8. Vehicle-Level Offloading Model
  9. Fleet-Level Workload
  10. Workload Instantiation and Results
  11. Experimental Setup
  12. Parameter Instantiation
  13. Results
  14. Conclusion and Future Work

Figures (6)

  • Figure 1: Representative multimodal sensor outputs from a Waymo frame.
  • Figure 2: Vehicle-to-cloud data offloading architecture.
  • Figure 3: Time-varying fleet-learning intensity $\lambda_L(t)$ derived from Munich traffic accident data.
  • Figure 4: Fleet offloading workload under varying AV penetration levels.
  • Figure 5: Comparison of AV workload and MAWI IoT traffic rate.
  • ...and 1 more figures