面向异构AIGC服务的通算存资源协同优化策略

吴梦如; 高羽; 赵波; 徐波; 孙浩; 郭磊

doi:10.11999/JEIT251300

面向异构AIGC服务的通算存资源协同优化策略

doi: 10.11999/JEIT251300 cstr: 32379.14.JEIT251300

吴梦如¹,
高羽¹,
赵波^2, ,,
徐波³,
孙浩⁴,
郭磊⁵

1.
浙江工业大学信息工程学院杭州 310023
2.
西北工业大学网络空间安全学院西安 710072
3.
南京邮电大学通信与信息工程学院南京 210003
4.
香港城市大学工学院香港
5.
东北大学计算机科学与工程学院沈阳 110819

基金项目: 国家自然科学基金(62301490, 62501480, U2441226)，浙江省自然科学基金(LQ24F010013)，四川省自然科学基金(2026NSFSC1433)

详细信息

作者简介:
吴梦如：女，副研究员，研究方向为移动边缘计算、无人机通信、物理层安全等

高羽：女，硕士生，研究方向为移动边缘计算、深度学习等

赵波：男，副教授，研究方向为网络空间安全治理、轻量化大模型技术等

徐波：男，讲师，研究方向为移动边缘计算、大数据和分布式学习

孙浩：男，博士后，研究方向为矩阵补全，张量分解以及生成式人工智能在通信信号处理领域的应用

郭磊：男，教授，研究方向为光通信网络、无线通信网络

通讯作者:
赵波　bozhao@nwpu.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 修回日期: 2026-01-26
- 录用日期: 2026-01-26
- 网络出版日期: 2026-02-02

Communication, Computation, and Caching Resource Collaboration for Heterogeneous AIGC Service Provisioning

WU Mengru¹,
GAO Yu¹,
ZHAO Bo^{2
, ,},
XU Bo³,
SUN Hao⁴,
GUO Lei⁵

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
2.
School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
3.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
4.
School of Engineering, City University of Hong Kong, Hong Kong, China
5.
School of Computer Science and Engineering, North-eastern University, Shenyang 110819, China

Funds: The National Natural Science Foundation of China (62301490, 62501480, U2441226), The Natural Science Foundation of Zhejiang Province (LQ24F010013), Sichuan Provincial Natural Science Foundation (2026NSFSC1433)

摘要

摘要: 在智能物联网(Artificial Intelligence of Things, AIoT)中，边缘服务器可以通过利用存储的人工智能生成内容(AI-Generated Content, AIGC)模型向AIoT设备提供智能服务。然而，边缘服务器的计算能力和模型存储容量有限，难以支撑大规模的模型存储以实现异构AIGC服务。针对此问题，基于AIGC服务的异构性，将AIGC服务划分为请求轻量型、计算密集型以及预处理型三类，并提出了一种云边协同与边边协同相结合的通算存资源优化方案。该方案协同云计算与边缘计算的优势，在考虑边缘服务器计算和存储资源限制的基础上，联合优化AIoT设备和基站的发射功率、计算资源分配、AIGC模型部署及服务请求决策以最小化AIGC服务总时延。由于所构建的优化问题是一个混合整数非线性规划问题，因此设计了一种基于交替优化的算法，该算法将问题分解为三个子问题，并分别采用连续凸逼近方法、卡罗需-库恩-塔克条件和改进的哈里斯鹰算法进行求解。仿真结果表明，所提方案具有较快的收敛速度，并且与基准方案相比能够降低AIGC服务总时延。
- 人工智能生成内容 /
- 移动边缘计算 /
- 模型部署 /
- 资源分配
Abstract: Objective In the artificial intelligence of things (AIoT), edge servers (ESs) can provide intelligent content generation services to AIoT devices by utilizing their cached AI-generated content (AIGC) models. However, the limited computing resources and caching capacity of ESs make it difficult to support the large-scale caching demands of heterogeneous AIGC services. To address this issue, this paper proposes a communication, computation, and caching resource collaboration scheme that leverages a combined cloud-edge and edge-edge collaborative framework. This scheme focuses on three representative AIGC services, including lightweight AIGC services, computation-intensive AIGC services, and preprocessing-based AIGC services. Furthermore, the proposed approach aims to minimize the total AIGC service latency by jointly optimizing transmit power, computing resource allocation, model caching strategies, and offloading decisions. Methods This paper investigates communication, computation, and caching resource collaboration for supporting heterogeneous AIGC services. First, an AIGC service-oriented AIoT system model is proposed to incorporate both cloud-edge and edge-edge collaboration. Subsequently, an optimization problem is formulated with the objective of minimizing the total latency of AIGC services by jointly optimizing transmit power, computing resource allocation, model caching strategies, and offloading decisions. Since the formulated problem is non-convex, an alternating optimization (AO) algorithm is proposed, which decomposes the problem into three subproblems that are solved using the successive convex approximation (SCA) method, Karush-Kuhn-Tucker (KKT) conditions, and an improved Harris Hawks Optimization (HHO) algorithm, respectively. Results and Discussions In the simulations, the proposed joint optimization scheme is compared to three baselines, including particle swarm optimization (PSO), fixed resource allocation, and random offloading and caching. First, the convergence of the proposed AO algorithm is verified (Fig. 2). The results demonstrate that the algorithm achieves rapid convergence within a limited number of iterations across different sub-problems. Second, increasing the transmission bandwidth leads to a significant reduction in the total AIGC service latency (Fig. 3). This is because each device can occupy more bandwidth resources to send tasks. Similarly, the ES can allocate more bandwidth to send generated content in the downlink. Furthermore, the total AIGC service latency decreases with the ES’s storage capacity for all the schemes (Fig. 4). This is because an increase in storage capacity allows the ES to store more AIGC models, thus reducing the transmission delay between the ES and the cloud server. Additionally, as the required floating point operations per bit increase, the total AIGC service latency exhibits a significant upward trend across all schemes (Fig. 5). Finally, the total AIGC service latency for all schemes decreases as the BS’s maximum transmit power increases (Fig. 6). This trend is attributed to the fact that the improvement of the BS’s maximum transmit power strengthens the downlink signal-to-noise ratio, which improves the downlink transmission rate, thereby leading to a reduction in the total AIGC service latency. However, the proposed scheme mitigates this increase more effectively than the baselines, demonstrating its robustness in handling computationally demanding AIGC tasks. In conclusion, these simulation results confirm that, compared to baselines, the proposed schemes significantly minimize the total AIGC service latency. Conclusions This paper investigates communication, computation, and caching resource collaboration for supporting heterogeneous AIGC services. Our objective is to minimize the total latency of AIGC services by jointly optimizing the transmit power of AIoT devices and base stations, computing resource allocation, AIGC model deployment, and service offloading decisions, subject to computation and caching resource constraints. Since the formulated problem is a mixed-integer non-linear programming problem, an efficient AO algorithm is designed. This algorithm decomposes the original optimization problem into three sub-problems, which are solved via the SCA algorithm, KKT conditions, and the HHO algorithm, respectively. Simulation results demonstrate that the proposed algorithm can reduce the total AIGC service latency compared to baselines.
- AI-generated content (AIGC) /
- mobile edge computing /
- model deployment /
- resource allocation.

HTML全文

图 1 系统模型

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图 2 所提算法的收敛性

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图 3 传输带宽与AIGC服务总时延的关系

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图 4 BS缓存容量与AIGC服务总时延的关系

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图 5 单位比特所需FLOPs与AIGC服务总时延的关系

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图 6 BS最大发射功率与AIGC服务总时延的关系

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1 基于交替优化算法求解$ {\mathcal{P}}_{0} $

初始化参数：$ {\boldsymbol{P}}^{(0)} $，$ {\boldsymbol{F}}^{(0)} $，$ {\boldsymbol{X}}^{(0)} $，$ {\boldsymbol{Y}}^{(0)} $，迭代次数$ l=1 $；定　义最大迭代次数$ {L}_{\max } $
(1) While $ l\leq {L}_{\max } $ do
(2) 　给定$ \boldsymbol{F}={\boldsymbol{F}}^{(l-1)} $, $ \boldsymbol{X}={\boldsymbol{X}}^{(l-1)} $, $ \boldsymbol{Y}={\boldsymbol{Y}}^{(l-1)} $，求解问　　　题$ {\mathcal{P}}_{1} $获得发射功率$ {\boldsymbol{P}}^{(l)} $；
(3) 　给定$ \boldsymbol{P}={\boldsymbol{P}}^{(l)} $, $ \boldsymbol{X}={\boldsymbol{X}}^{(l-1)} $, $ \boldsymbol{Y}={\boldsymbol{Y}}^{(l-1)} $求解问题$ {\mathcal{P}}_{2} $ 　　　获得计算资源分配$ {\boldsymbol{F}}^{(l)} $；
(4) 　给定$ \boldsymbol{P}={\boldsymbol{P}}^{(l)} $, $ \boldsymbol{F}={\boldsymbol{F}}^{(l)} $求解问题$ {\mathcal{P}}_{3} $获得AIGC模型部　　　署决策$ {\boldsymbol{X}}^{(l)} $和服务请求决策$ {\boldsymbol{Y}}^{(l)} $；
(5) 　更新$ l=l+1 $；
(6) End While：收敛

下载: 导出CSV

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