REACT: Reinforcement learning and multi-objective optimization for task scheduling in ultra-dense edge networks

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Smithamol M.B. , Rajeswari Sridhar
{"title":"REACT: Reinforcement learning and multi-objective optimization for task scheduling in ultra-dense edge networks","authors":"Smithamol M.B. ,&nbsp;Rajeswari Sridhar","doi":"10.1016/j.adhoc.2025.103834","DOIUrl":null,"url":null,"abstract":"<div><div>This paper addresses the challenges of task scheduling and resource allocation in ultra-dense edge cloud (UDEC) networks, which integrate micro and macro base stations with diverse user equipment in 5G environments. To optimize system performance, we propose REACT, a novel two-level scheduling framework leveraging reinforcement learning (RL) for energy-efficient task scheduling. At the upper level, RL-based adaptive optimization replaces conventional power allocation techniques, dynamically minimizing transmission energy consumption under the Non-Orthogonal Multiple Access (NOMA) protocol. At the lower level, the joint task offloading and resource allocation problem is modeled as a multi-objective optimization challenge. This is solved using a hybrid approach combining meta-heuristic algorithms and Long Short-Term Memory (LSTM) predictive models, maximizing response rates and system throughput. Sensitivity analyses explore the effects of user density, channel quality, workload, and request size on performance. Comparative evaluations against state-of-the-art methods demonstrate the proposed framework’s superior efficiency in tackling dynamic scheduling challenges, achieving energy savings and enhancing user experience.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"174 ","pages":"Article 103834"},"PeriodicalIF":4.4000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870525000824","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper addresses the challenges of task scheduling and resource allocation in ultra-dense edge cloud (UDEC) networks, which integrate micro and macro base stations with diverse user equipment in 5G environments. To optimize system performance, we propose REACT, a novel two-level scheduling framework leveraging reinforcement learning (RL) for energy-efficient task scheduling. At the upper level, RL-based adaptive optimization replaces conventional power allocation techniques, dynamically minimizing transmission energy consumption under the Non-Orthogonal Multiple Access (NOMA) protocol. At the lower level, the joint task offloading and resource allocation problem is modeled as a multi-objective optimization challenge. This is solved using a hybrid approach combining meta-heuristic algorithms and Long Short-Term Memory (LSTM) predictive models, maximizing response rates and system throughput. Sensitivity analyses explore the effects of user density, channel quality, workload, and request size on performance. Comparative evaluations against state-of-the-art methods demonstrate the proposed framework’s superior efficiency in tackling dynamic scheduling challenges, achieving energy savings and enhancing user experience.
REACT:超密集边缘网络任务调度的强化学习和多目标优化
本文讨论了5G环境下超密集边缘云(UDEC)网络中任务调度和资源分配的挑战,该网络将微观和宏观基站与各种用户设备集成在一起。为了优化系统性能,我们提出了REACT,一种利用强化学习(RL)进行节能任务调度的新型两级调度框架。在上层,基于rl的自适应优化取代了传统的功率分配技术,在非正交多址(NOMA)协议下动态地最小化传输能耗。在较低层次上,将联合任务卸载和资源分配问题建模为一个多目标优化问题。这是通过结合元启发式算法和长短期记忆(LSTM)预测模型的混合方法来解决的,从而最大限度地提高响应率和系统吞吐量。敏感性分析探讨了用户密度、信道质量、工作负载和请求大小对性能的影响。与最先进方法的比较评估表明,所提出的框架在解决动态调度挑战、实现节能和增强用户体验方面具有卓越的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Ad Hoc Networks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信