DRL-based Task Scheduling Scheme in Vehicular Fog Computing: Cooperative and mobility aware approach

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks Pub Date : 2025-03-13 DOI:10.1016/j.adhoc.2025.103819

Mekala Ratna Raju , Sai Krishna Mothku , Manoj Kumar Somesula

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引用次数: 0

Abstract

In the realm of Vehicular Fog Computing (VFC), the dynamic nature of vehicular networks presents substantial challenges for effective task scheduling and resource allocation. The rapidly changing mobility patterns of vehicles complicate the management of service delays for vehicular requests and the energy usage of servers. Our research addresses these challenges by focusing on cooperative and mobility-aware task scheduling in VFC, aiming to optimize fog server performance and ensure the timely processing of vehicular tasks. We model vehicle mobility using a Markov renewal process to determine vehicle movements. The task scheduling problem is formulated as a mixed-integer non-linear programming (MINLP) problem, considering constraints such as task deadlines, resource limits, and vehicle mobility. To tackle this problem, we utilize a deep reinforcement learning (DRL) technique, which allows for adaptive and intelligent task scheduling and resource allocation. Through extensive simulations, our approach demonstrates significant improvements over existing benchmark techniques, achieving a 12% reduction in service delays and a 14% decrease in energy consumption.

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基于drl的车辆雾计算任务调度方案：协作和机动性感知方法

在车载雾计算（VFC）领域，车辆网络的动态性对有效的任务调度和资源分配提出了重大挑战。快速变化的车辆移动模式使车辆请求的服务延迟管理和服务器的能源使用复杂化。我们的研究通过关注VFC中的协作和移动感知任务调度来解决这些挑战，旨在优化雾服务器性能并确保车辆任务的及时处理。我们使用马尔可夫更新过程来确定车辆的移动。任务调度问题是一个混合整数非线性规划（MINLP）问题，考虑了任务期限、资源限制和车辆移动性等约束条件。为了解决这个问题，我们利用深度强化学习（DRL）技术，该技术允许自适应和智能的任务调度和资源分配。通过大量的模拟，我们的方法比现有的基准技术有了显著的改进，服务延迟减少了12%，能耗减少了14%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ad Hoc Networks 工程技术-电信学

CiteScore

10.20

自引率

4.20%

发文量

131

审稿时长

4.8 months

期刊介绍： The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.