An Efficient Resource Allocation Algorithm for Task Offloading in the Internet of Vehicles

Computer and Knowledge Engineering

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 21 آبان 1403   

نوع مقاله: Special Issue

شناسه دیجیتال (DOI): 10.22067/cke.2024.89721.1129

نویسندگان

Ahmad Salehi¹؛ Sadoon Azizi^{* 2}

¹Department of Computer Engineering and IT, University of Kurdistan, Sanandaj, Iran

²Department of Computer Engineering and IT, University of Kurdistan, Sannadaj, Iran

چکیده

The Internet of Vehicles (IoV) represents a transformative paradigm in Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles, infrastructure, and cloud platforms to improve traffic management, safety, and efficiency. However, the resource limitations in vehicles pose significant challenges for delay-sensitive applications such as autonomous driving and automated navigation. Vehicular Edge Computing (VEC) offers a promising solution by offloading tasks to edge servers near vehicles, reducing transmission delays and enhancing computational efficiency. In this paper, we address the complex task offloading and resource allocation problem in VEC environments. We model this challenge as an Integer Linear Programming (ILP) problem, aiming to maximize the system’s overall profit. To mitigate the computational complexity of solving the ILP problem, we propose an efficient heuristic algorithm. This approach considers various task types, accounting for the diversity and specific requirements of each. The algorithm optimizes CPU resource allocation based on task generation rates, average task sizes, and a calculated weight coefficient for each task type. Simulation results demonstrate that the proposed algorithm reduces memory costs and penalties from rejected tasks, while improving overall system profit. In particular, it outperforms existing algorithms by an average of 18.26% in terms of profit, demonstrating its effectiveness in practical VEC applications.

کلیدواژه‌ها

Internet of vehicle؛ vehicular edge computing؛ task offloading؛ resource allocation؛ profit maximization