一种改善多接入边缘计算网络切片服务质量的双层闭环协同资源分配框架

徐骏涛; 范兴刚; 徐常福; 沈民扬; 梁玉珠; 王田

doi:10.11999/JEIT260156

一种改善多接入边缘计算网络切片服务质量的双层闭环协同资源分配框架

doi: 10.11999/JEIT260156 cstr: 32379.14.JEIT260156

1.
浙江工业大学计算机科学与技术学院/软件学院杭州 310023
2.
浙江工业大学之江学院绍兴 312030
3.
江西财经大学软件与物联网工程学院南昌 330013
4.
北京师范大学人工智能与未来网络研究院珠海 519085

基金项目: 国家自然科学基金(U25A20436)，山东省重点研发计划(2025TSGCCZZB0026)

详细信息

作者简介:
徐骏涛：男，硕士生，研究方向为5G，移动计算和边缘计算

范兴刚：男，教授，研究方向为物联网，边缘计算

徐常福：男，博士，研究方向为边缘计算，AIGC

沈民扬：男，硕士生，研究方向为物联网，边缘计算

梁玉珠：男，博士，研究方向为边缘计算，移动计算和人工智能

王田：男，教授，研究方向为物联网，边缘计算和移动计算

通讯作者:
范兴刚　xgfan@zjut.edu.cn

中图分类号: TN929.5; TP393
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出版历程
- 收稿日期: 2026-02-06
- 修回日期: 2026-06-17
- 录用日期: 2026-06-18
- 网络出版日期: 2026-06-30

A Two-layer Closed-loop Cooperative Resource Allocation Framework for Improving QoS of MEC Network Slicing

1.
College of Computer Science and Technology Zhejiang University of Technology, Hangzhou 310023, China
2.
Zhijiang College, Zhejiang University of Technology, Shaoxing 312030, China
3.
School of Software and IoT Engineering Jiangxi University of Finance and Economics, Nanchang 330013, China
4.
Institute of AI and Future Networks Beijing Normal University, Zhuhai 519085, China

Funds: The National Natural Science Foundation of China (U25A20436), Shandong Provincial Key R&D Program (2025TSGCCZZB0026)

摘要

摘要: 在5G/6G驱动的多接入边缘计算(MEC)环境中，提高资源利用率的同时，确保异构网络切片的服务质量(QoS)具有重要意义。然而，当前方法在异构环境下缺乏动态适应性，无法协同优化缓存、带宽和计算资源，存在资源利用率下降、服务成功率下降等问题。该文首先形式化定义了网络切片资源分配问题，并通过将多维0-1背包问题归约到该问题来证明其是NP-难问题。然后提出了一种具有缓存感知的双层闭环协同资源分配框架，上层构建一种群体协同的全局搜索机制，通过候选解演化、历史最优引导与自适应参数调整，在复杂约束条件下生成高质量资源候选分配方案，下层构建一个多维权重评估模型以准确将低延迟、高带宽的服务需求指标转化为QoS约束，通过上下双层闭环协同实现缓存、带宽和计算资源的联合优化，进而改善了异构网络切片的QoS。大量实验表明，与基线方法相比，所提方法使资源利用率提升2.29%～24.50%，用户评分提升4.13%～59.34%，为异构MEC环境提供了一种鲁棒的资源分配解决方案。
- 多接入边缘计算 /
- 网络切片 /
- 资源分配 /
- QoS保障
Abstract: Objective Driven by 5G/6G networks, Multi-access Edge Computing (MEC) environments face major challenges in ensuring Quality of Service (QoS) for heterogeneous network slices while improving resource utilization. Existing resource allocation methods often lack dynamic adaptability in heterogeneous settings. They also fail to jointly optimize caching, bandwidth, and computing resources, which reduces resource utilization and service success rates. This paper addresses these limitations by proposing a robust framework for joint multi-dimensional resource optimization under strict QoS constraints. The framework provides a tailored solution for heterogeneous MEC environments. Methods The network slicing resource allocation problem is first formally defined. Its NP-hardness is proved by reducing the NP-hard multidimensional 0-1 knapsack problem to this problem. To solve this complex optimization problem, a cache-aware two-layer closed-loop cooperative framework, termed QCache, is proposed. The framework uses a synergistic “generate-evaluate-feedback” loop. In the upper Global Exploration Layer, a hybrid heuristic algorithm is designed by combining a population evolution strategy, including selection, crossover, and mutation, with a particle update mechanism guided by historical individual and global best positions. This layer broadly explores the solution space and generates high-quality candidate resource allocation schemes under complex constraints. Adaptive parameter adjustment and elite retention strategies are used to avoid local optima. In the lower Multi-Dimensional Weight Evaluation Layer, a quantitative assessment model is constructed. This model converts low-latency and high-bandwidth service demands into explicit QoS constraints by normalizing key performance indicators, including delay and rate, and by dynamically assigning weights through the entropy weight method. The weighted score reflects different slice priorities. The evaluated score (SCORE) from this layer is fed back to the upper layer as the fitness value, guiding the iterative evolution of candidate solutions until convergence. Results and Discussions Extensive simulations are conducted to validate the effectiveness of QCache against several baseline methods, including Genetic Algorithm (GA), PSO-Leader, GraphSAGE, and the No-Consideration-of-Service-Quality (NCSQ) scheme. Under identical resource and user demand scenarios, the overall comparison (Fig. 3) shows that QCache achieves the highest resource utilization rate of 80.30% and the best average service score of 1.109. Compared with the baseline methods, QCache improves resource utilization by 2.29% to 24.50% and increases user service scores by 4.13% to 59.34%. Experiments with varying total cache resources (Fig. 4) show that QCache maintains superior performance across different cache states. It improves resource utilization by 2.43% to 27.53% and service scores by 10.81% to 119.56%, confirming its cache-aware adaptability. Tests with increasing user numbers (Fig. 5) show that QCache scales effectively, achieving up to 85.83% resource utilization and a service score of 3.06. These results demonstrate its ability to handle dense access scenarios. Experiments with time-varying user demands (Fig. 6) further confirm the dynamic robustness of the framework. In these tests, QCache achieves average improvements of 9.20% in resource utilization and 23.45% in service score over the baselines. Conclusions This paper studies the NP-hard resource allocation problem in dynamic MEC environments with heterogeneous network slices. The proposed cache-aware two-layer closed-loop cooperative framework, QCache, jointly optimizes caching, bandwidth, and computing resources under explicit QoS constraints. The upper-layer hybrid heuristic provides strong global search capability. The lower-layer multi-dimensional weight model supports accurate QoS quantification and dynamic feedback. Comprehensive experimental results show that QCache outperforms existing methods in both resource utilization efficiency and user QoS satisfaction. Future work will explore reinforcement learning and traffic prediction mechanisms to further improve the response of the framework to bursty traffic and anomalous demands. This may support more intelligent and autonomous MEC network slice resource management.
- Multi-access Edge Computing(MEC) /
- Network slicing /
- Resource allocation /
- Quality of Service(QoS) guarantee

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图 1 支持MEC的网络切片体系结构图

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图 2 双层闭环协同优化框架示意图

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图 3 相同场景下各方案总体比较

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图 4 总缓存资源变化下各方案比较

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图 5 总用户数量变化下各方案比较

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图 6 用户需求变化下各方案比较

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1 缓存资源分配计算算法

输入：用户需求集REQ，基站缓存集CACHE，切片资源配置集　SLICE；
输出：基站缓存资源最优分配集UPDATE。
(1) 使用随机资源分配策略初始化种群$ {\mathrm{pop}} $，初始化种群　$ {\mathrm{fitness}} $、个体最优$ {\mathrm{pbest}} $和种群最优$ {\mathrm{gbest}} $的适应度以及基站的　缓存策略$ {\mathrm{UPDATE}} $；
(2) for 种群的每次迭代 do
(3) 基于多维权重模型计算个体的$ {\mathrm{fitness}} $；
(4) 更新$ {\mathrm{pbest}} $和$ {\mathrm{gbest}} $；
(5) 基于$ {\mathrm{fitness}} $从大到小对$ {\mathrm{pop}} $进行排序；
(6) 选择$ {\mathrm{pop}} $小于精英比例的部分为$ {\mathrm{pop1}} $，其余部分为$ {\mathrm{pop2}} $；
(7) 对$ {\mathrm{pop1}} $执行锦标赛选择、单点交叉和高斯变异操作，对　$ {\mathrm{pop2}} $执行更新位置和速度操作；
(8) end for
(9) 基于多维权重模型计算每个个体$ {\mathrm{fitness}} $；
(10) 输出缓存分配最适合的策略$ {\mathrm{UPDATE}} $。

下载: 导出CSV

2 多维度权重评估计算算法

输入：用户需求UREQ，用户与基站的距离DIS，用户数据包　PACK，基站缓存状态STATE，切片给用户分配的资源配置集　RES；
输出：用户的综合评分SCORE。
(1) 根据式(4)计算$ {\mathrm{SINR}} $，根据式(7)和式(8)计算$ {\mathrm{rate}} $，根据　式(11)计算$ {\mathrm{delay}} $；
(2) if 所需数据没有在基站中缓存
(3) delay需根据式(14)加上额外的时延；
(4) end if
(5) 归一化速率$ {\mathrm{rate}} $和时延$ {\mathrm{delay}} $等属性；
(6) 计算各属性的信息熵；
(7) 计算各属性的冗余度；
(8) 输出每个指标加权后的分数SCORE。

下载: 导出CSV

表 1 仿真参数^[17,29]

参数名称	参数取值
切片数量	4
总带宽资源	75 MHz
总计算资源	150 GHz
总缓存资源	75 GB
每个切片分配的带宽资源	[0,75] MHz
每个切片分配的计算资源	[0,150] GHz
每个切片分配的缓存资源	[0,75] GB
数据包大小	[5,125] MB
数据缓存标识$ {x}_{s,l,u,t} $	0,1
最大计算卸载延时	[0.02,0.2] s
最大内容传输延时	[0.5,5] s
回程链路传输速率	[50,200] Mbit/(s·Hz)

下载: 导出CSV

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