{"title":"REACT: Reinforcement learning and multi-objective optimization for task scheduling in ultra-dense edge networks","authors":"Smithamol M.B. , 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.
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.