TMLpSA-MEC: Transformer-based Mobility Aware Periodic Service Assignment in Mobile Edge Computing

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks Pub Date : 2025-05-06 DOI:10.1016/j.comnet.2025.111329

N. Rana Singha, Nityananda Sarma, Dilip Kumar Saikia

{"title":"TMLpSA-MEC: Transformer-based Mobility Aware Periodic Service Assignment in Mobile Edge Computing","authors":"N. Rana Singha, Nityananda Sarma, Dilip Kumar Saikia","doi":"10.1016/j.comnet.2025.111329","DOIUrl":null,"url":null,"abstract":"<div><div>Mobile edge computing (MEC) brings the power of cloud computing closer to where users are, enhancing network performance and improving user experience. However, as users move around and individual edge servers cover only limited areas, there is a need for intelligent service assignment to keep up with user demands and low turn around time (TAT) requirements. This paper introduces TMLpSA, a synergistic framework that combines advanced user mobility prediction and decision-making techniques to optimize service assignments in a periodic fashion. By leveraging a Transformer model to anticipate where users will go next, and integrating a DRL-based TOPSIS technique, TMLpSA predicts user trajectories and proactively identifies the most suitable edge servers to assign services along their anticipated paths. Simulation results highlight how TMLpSA minimizes average application TAT significantly by 23.32%, while not only reducing offload energy consumption but also improving task completion rate and resource utilization with reasonable service migration frequency relative to the second best benchmark approach.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"266 ","pages":"Article 111329"},"PeriodicalIF":4.4000,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128625002968","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}

引用次数: 0

Abstract

Mobile edge computing (MEC) brings the power of cloud computing closer to where users are, enhancing network performance and improving user experience. However, as users move around and individual edge servers cover only limited areas, there is a need for intelligent service assignment to keep up with user demands and low turn around time (TAT) requirements. This paper introduces TMLpSA, a synergistic framework that combines advanced user mobility prediction and decision-making techniques to optimize service assignments in a periodic fashion. By leveraging a Transformer model to anticipate where users will go next, and integrating a DRL-based TOPSIS technique, TMLpSA predicts user trajectories and proactively identifies the most suitable edge servers to assign services along their anticipated paths. Simulation results highlight how TMLpSA minimizes average application TAT significantly by 23.32%, while not only reducing offload energy consumption but also improving task completion rate and resource utilization with reasonable service migration frequency relative to the second best benchmark approach.

查看原文本刊更多论文

TMLpSA-MEC：移动边缘计算中基于变压器的移动感知周期服务分配

移动边缘计算（MEC）使云计算的力量更接近用户所在的位置，从而增强网络性能并改善用户体验。然而，随着用户的移动和单个边缘服务器只覆盖有限的区域，需要智能服务分配来跟上用户需求和低周转时间（TAT）要求。本文介绍了TMLpSA，这是一个结合了先进的用户移动性预测和决策技术的协同框架，以周期性的方式优化服务分配。通过利用Transformer模型预测用户下一步将去哪里，并集成基于drl的TOPSIS技术，TMLpSA预测用户轨迹，并主动识别最合适的边缘服务器，以便沿着预期路径分配服务。仿真结果表明，相对于第二优基准方法，TMLpSA不仅降低了卸载能耗，而且在合理的业务迁移频率下提高了任务完成率和资源利用率，显著降低了23.32%的平均应用TAT。

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

Computer Networks 工程技术-电信学

CiteScore

10.80

自引率

3.60%

发文量

434

审稿时长

8.6 months

期刊介绍： Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.