Latency- and Load-Aware MEC Host Selection in Simu5G: A Lightweight Orchestration Policy for Delay-Sensitive 5G Applications

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Atakan Ozcan, Deniz Berke Ozsoy, Mohammed Al-Hubaishi
{"title":"Latency- and Load-Aware MEC Host Selection in Simu5G: A Lightweight Orchestration Policy for Delay-Sensitive 5G Applications","authors":"Atakan Ozcan,&nbsp;Deniz Berke Ozsoy,&nbsp;Mohammed Al-Hubaishi","doi":"10.1002/nem.70038","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Multiaccess edge computing (MEC) reduces end-to-end (E2E) latency by offloading computation from user equipment (UE) to nearby edge hosts, but host-selection policies in simulators such as <i>Simu5G</i> typically focus on service availability or CPU headroom. They do not explicitly account for dynamic UE–host latency and can therefore steer delay-sensitive flows to topologically distant or temporarily congested hosts, inflating tail delay and limiting effective throughput. We extend <i>Simu5G</i>'s ETSI-compliant MEC orchestrator with <span>LatencyAwareSelectionBased</span>, a lightweight host-selection policy that jointly accounts for (i) a dynamic UE–host delay estimate derived from measurable run-time delay statistics and smoothed via an exponential moving average and (ii) the host's normalized CPU load. These two signals are combined in a simple score, <span></span><math>\n <semantics>\n <mrow>\n <mi>S</mi>\n <mo>(</mo>\n <mi>h</mi>\n <mo>)</mo>\n <mo>=</mo>\n <mover>\n <mrow>\n <mi>L</mi>\n </mrow>\n <mo>˜</mo>\n </mover>\n <mo>(</mo>\n <mi>h</mi>\n <mo>)</mo>\n <mspace></mspace>\n <mfenced>\n <mrow>\n <mn>1</mn>\n <mo>+</mo>\n <mi>α</mi>\n <mspace></mspace>\n <mover>\n <mrow>\n <mi>C</mi>\n </mrow>\n <mo>˜</mo>\n </mover>\n <mo>(</mo>\n <mi>h</mi>\n <mo>)</mo>\n </mrow>\n </mfenced>\n </mrow>\n <annotation>$$ S(h)=\\tilde{L}(h)\\kern0.3em \\left(1+\\alpha \\kern0.3em \\tilde{C}(h)\\right) $$</annotation>\n </semantics></math>, controlled by a single trade-off parameter <span></span><math>\n <semantics>\n <mrow>\n <mi>α</mi>\n </mrow>\n <annotation>$$ \\alpha $$</annotation>\n </semantics></math>. The policy is implemented as a plug-in selection module activated through <i>Simu5G</i>'s native <span>selectionPolicy</span> hook, requiring only minimal changes to existing scenarios and preserving the standard orchestration pipeline. We evaluate the proposed policy in <i>Simu5G</i>'s <span>MultiMecHost</span> scenario under three operating points (Best, Moderate, and Worst) and an additional multitenant burst configuration with up to 20 UEs generating bursty arrivals. Using Simu5G scalar outputs, we take downlink MAC delay and per-UE throughput as primary metrics and also examine CPU-only, latency-only, and mixed-score variants for different values of <span></span><math>\n <semantics>\n <mrow>\n <mi>α</mi>\n </mrow>\n <annotation>$$ \\alpha $$</annotation>\n </semantics></math>. Across all configurations, the latency-aware policy consistently maintains low tail latency (95th-percentile downlink MAC delay below approximately <span></span><math>\n <semantics>\n <mrow>\n <mn>7</mn>\n <mspace></mspace>\n <mtext>ms</mtext>\n </mrow>\n <annotation>$$ 7\\kern0.3em \\mathrm{ms} $$</annotation>\n </semantics></math>) while improving per-UE throughput relative to <i>Simu5G</i>'s default CPU- and service-based strategies and avoiding persistent host hot spots. The resulting design provides a simple, reproducible baseline for latency-sensitive MEC orchestration and a practical integration point for future mobility-aware and learning-based host-selection schemes.</p>\n </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 2","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2026-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Network Management","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/nem.70038","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Multiaccess edge computing (MEC) reduces end-to-end (E2E) latency by offloading computation from user equipment (UE) to nearby edge hosts, but host-selection policies in simulators such as Simu5G typically focus on service availability or CPU headroom. They do not explicitly account for dynamic UE–host latency and can therefore steer delay-sensitive flows to topologically distant or temporarily congested hosts, inflating tail delay and limiting effective throughput. We extend Simu5G's ETSI-compliant MEC orchestrator with LatencyAwareSelectionBased, a lightweight host-selection policy that jointly accounts for (i) a dynamic UE–host delay estimate derived from measurable run-time delay statistics and smoothed via an exponential moving average and (ii) the host's normalized CPU load. These two signals are combined in a simple score, S ( h ) = L ˜ ( h ) 1 + α C ˜ ( h ) $$ S(h)=\tilde{L}(h)\kern0.3em \left(1+\alpha \kern0.3em \tilde{C}(h)\right) $$ , controlled by a single trade-off parameter α $$ \alpha $$ . The policy is implemented as a plug-in selection module activated through Simu5G's native selectionPolicy hook, requiring only minimal changes to existing scenarios and preserving the standard orchestration pipeline. We evaluate the proposed policy in Simu5G's MultiMecHost scenario under three operating points (Best, Moderate, and Worst) and an additional multitenant burst configuration with up to 20 UEs generating bursty arrivals. Using Simu5G scalar outputs, we take downlink MAC delay and per-UE throughput as primary metrics and also examine CPU-only, latency-only, and mixed-score variants for different values of α $$ \alpha $$ . Across all configurations, the latency-aware policy consistently maintains low tail latency (95th-percentile downlink MAC delay below approximately 7 ms $$ 7\kern0.3em \mathrm{ms} $$ ) while improving per-UE throughput relative to Simu5G's default CPU- and service-based strategies and avoiding persistent host hot spots. The resulting design provides a simple, reproducible baseline for latency-sensitive MEC orchestration and a practical integration point for future mobility-aware and learning-based host-selection schemes.

Simu5G中延迟和负载敏感的MEC主机选择:延迟敏感5G应用的轻量级业务流程策略
多访问边缘计算(MEC)通过将计算从用户设备(UE)卸载到附近的边缘主机来减少端到端(E2E)延迟,但Simu5G等模拟器中的主机选择策略通常侧重于服务可用性或CPU剩余空间。它们没有明确地考虑动态UE-host延迟,因此可以将延迟敏感流引导到拓扑距离较远或暂时拥塞的主机,从而增加尾部延迟并限制有效吞吐量。我们用LatencyAwareSelectionBased扩展了Simu5G符合etsi的MEC编排器,这是一种轻量级的主机选择策略,可以共同考虑(i)从可测量的运行时延迟统计数据中得出的动态ue -主机延迟估计,并通过指数移动平均线进行平滑,以及(ii)主机的标准化CPU负载。这两种信号组合成一个简单的分数,S (h) = L ~ (h) 1 + αC ~ (h) $$ S(h)=\tilde{L}(h)\kern0.3em \left(1+\alpha \kern0.3em \tilde{C}(h)\right) $$,由单个权衡参数α $$ \alpha $$控制。该策略是作为通过Simu5G的本机selectionPolicy钩子激活的插件选择模块实现的,只需要对现有场景进行最小的更改,并保留标准的编排管道。我们在三个操作点(最佳、中等和最差)和一个额外的多租户突发配置(最多20个ue产生突发到达)下评估了Simu5G的MultiMecHost场景中的建议策略。使用Simu5G标量输出,我们将下行链路MAC延迟和每ue吞吐量作为主要指标,并检查不同α $$ \alpha $$值的仅cpu,仅延迟和混合分数变体。在所有配置中,延迟感知策略始终保持低尾部延迟(第95百分位下行链路MAC延迟低于约7 ms $$ 7\kern0.3em \mathrm{ms} $$),同时相对于Simu5G的默认基于CPU和服务的策略提高了每ue吞吐量,并避免了持久的主机热点。最终的设计为延迟敏感的MEC编排提供了一个简单、可重复的基线,并为未来的移动性感知和基于学习的主机选择方案提供了一个实用的集成点。
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
International Journal of Network Management
International Journal of Network Management COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
5.10
自引率
6.70%
发文量
25
审稿时长
>12 weeks
期刊介绍: Modern computer networks and communication systems are increasing in size, scope, and heterogeneity. The promise of a single end-to-end technology has not been realized and likely never will occur. The decreasing cost of bandwidth is increasing the possible applications of computer networks and communication systems to entirely new domains. Problems in integrating heterogeneous wired and wireless technologies, ensuring security and quality of service, and reliably operating large-scale systems including the inclusion of cloud computing have all emerged as important topics. The one constant is the need for network management. Challenges in network management have never been greater than they are today. The International Journal of Network Management is the forum for researchers, developers, and practitioners in network management to present their work to an international audience. The journal is dedicated to the dissemination of information, which will enable improved management, operation, and maintenance of computer networks and communication systems. The journal is peer reviewed and publishes original papers (both theoretical and experimental) by leading researchers, practitioners, and consultants from universities, research laboratories, and companies around the world. Issues with thematic or guest-edited special topics typically occur several times per year. Topic areas for the journal are largely defined by the taxonomy for network and service management developed by IFIP WG6.6, together with IEEE-CNOM, the IRTF-NMRG and the Emanics Network of Excellence.
×
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:604180095
Book学术官方微信
小红书