# A DAG-Based Offloading Strategy with Dynamic Parallel Factor Adjustment for Edge Computing in IoV

## TL;DR

This paper introduces a dynamic offloading strategy for edge computing in IoV to improve task efficiency and reduce delays.

## Contribution

The novel DPF algorithm adjusts parallel factors in DAGs based on real-time conditions for efficient task offloading in IoV.

## Key findings

- DPF significantly reduces task delay and improves task success rates compared to static strategies.
- The algorithm achieves better task completion times and resource utilization under high-load scenarios.
- Simulation experiments confirm the superior performance of DPF across different task load conditions.

## Abstract

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods.