Real-Time Service Subscription and Adaptive Offloading Control in Vehicular Edge Computing

2025-12-15

AI Summary

In vehicular edge computing (VEC), latency-sensitive task offloading faces dual constraints of bandwidth and computational resources, stringent deadlines, and dynamic wireless channels. Method: This paper formulates a deadline-aware task offloading and resource allocation optimization model and proposes a real-time service subscription and adaptive offloading control framework. We design SARound—an algorithm uniquely integrating linear programming rounding with the local ratio method—to approximate the Deadline-Constrained Dynamic Offloading Assignment Problem (DOAP), improving the approximation ratio from 1/6 to 1/4. Additionally, we develop an online control mechanism supporting vehicle mobility and time-varying channel conditions. Results: Extensive OMNeT++/Simu5G co-simulations—using real taxi trajectories and object detection workloads—demonstrate that SARound achieves an 18.7% higher average task completion rate than state-of-the-art methods, a deadline satisfaction rate exceeding 92%, and per-decision latency under 15 ms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Vehicular Edge Computing (VEC) has emerged as a promising paradigm for enhancing the computational efficiency and service quality in intelligent transportation systems by enabling vehicles to wirelessly offload computation-intensive tasks to nearby Roadside Units. However, efficient task offloading and resource allocation for time-critical applications in VEC remain challenging due to constrained network bandwidth and computational resources, stringent task deadlines, and rapidly changing network conditions. To address these challenges, we formulate a Deadline-Constrained Task Offloading and Resource Allocation Problem (DOAP), denoted as $mathbf{P}$, in VEC with both bandwidth and computational resource constraints, aiming to maximize the total vehicle utility. To solve $mathbf{P}$, we propose $mathtt{SARound}$, an approximation algorithm based on Linear Program rounding and local-ratio techniques, that improves the best-known approximation ratio for DOAP from $frac{1}{6}$ to $frac{1}{4}$. Additionally, we design an online service subscription and offloading control framework to address the challenges of short task deadlines and rapidly changing wireless network conditions. To validate our approach, we develop a comprehensive VEC simulator, VecSim, using the open-source simulation libraries OMNeT++ and Simu5G. VecSim integrates our designed framework to manage the full life-cycle of real-time vehicular tasks. Experimental results, based on profiled object detection applications and real-world taxi trace data, show that $mathtt{SARound}$ consistently outperforms state-of-the-art baselines under varying network conditions while maintaining runtime efficiency.

Problem

• Optimizes task offloading and resource allocation in vehicular edge computing
• Addresses challenges of network constraints and strict task deadlines
• Improves vehicle utility with an efficient approximation algorithm

Innovation

• Proposed SARound algorithm with linear programming rounding
• Designed online service subscription and offloading control framework
• Developed VecSim simulator using OMNeT++ and Simu5G