星地融合网络中平衡QoS的服务放置与任务卸载联合优化

戴翠琴; 王泓运; 廖荣鹏; 陈前斌

doi:10.11999/JEIT251294

星地融合网络中平衡QoS的服务放置与任务卸载联合优化

doi: 10.11999/JEIT251294 cstr: 32379.14.JEIT251294

重庆邮电大学通信与信息工程学院重庆 400065

详细信息

作者简介:
戴翠琴：女，教授，博士生导师，研究方向为6G星地融合通信中的关键技术

王泓运：男，硕士生，研究方向为平衡QoS的星地服务放置

廖荣鹏：男，硕士生，研究方向为基于跳波束的卫星波束调度

陈前斌：男，教授，博士生导师，研究方向为个人通信、多媒体信息处理与传输、异构蜂窝网络等

中图分类号: TN927.2
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出版历程
- 修回日期: 2026-03-16
- 录用日期: 2026-03-16
- 网络出版日期: 2026-04-06

Joint Optimization of Service Placement and Task Offloading for QoS Balancing in Satellite-Terrestrial Integrated Networks

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

摘要

摘要: 星地融合网络通过协同调度地面网络与卫星节点的计算资源，为用户提供全域随遇接入的计算服务与多样化业务支持。针对星地边缘节点服务放置、云边端协同任务卸载、任务服务质量(QoS)的时延、安全性与隐私等级需求问题，提出一种平衡QoS的服务放置与任务卸载联合优化方案(BQSPTO)。首先，采用终端侧、边缘侧、云服务器协同的方式搭建星地网络模型，考虑任务密钥被破解的概率定义任务避免攻击概率，通过任务模式和位置隐私定义任务隐私等级，结合任务完成时延、避免攻击概率、隐私等级构建QoS评估模型。其次，根据任务流行度预测和服务迁移的方式设计星地边缘节点服务放置策略，根据QoS值判定卸载位置和多节点协作方式设计云边端协同完全卸载策略，基于星地边缘节点服务放置和云边端协同任务卸载策略，考虑通信资源与计算资源约束，以最大化总任务QoS值为目标完成优化问题建模。然后，将联合优化问题解耦为服务放置与任务卸载子问题，通过基于多维QoS非支配排序遗传的服务放置算法与融合鲸鱼与灰狼优化云边端协作的任务卸载算法分别解决子问题并进行交替优化完成方案求解。仿真结果表明，所提出的BQSPTO方案不仅能够有效提升服务放置与任务卸载总QoS值，而且能够保障任务的时延、安全性与隐私等级需求。
- 星地融合网络 /
- 服务放置 /
- 任务卸载 /
- 服务质量
Abstract: Objective Satellite-Terrestrial Integrated Networks (STIN) integrate multi-source and multi-dimensional services from terrestrial networks and satellite networks, boasting unique advantages of wide coverage and flexible networking. They can provide users with an enhanced service experience featuring global coverage and ubiquitous access. However, the characteristics of STIN, such as dynamically varying network topology, heterogeneous and limited node resources, complicate the service placement problem of satellite-terrestrial edge nodes. This further exacerbates the difficulty in solving the task offloading problem that matches user service requests with edge server resources, resulting in the failure to guarantee the Quality of Service (QoS) requirements of users. To address this issue, this paper proposes a joint optimization scheme for QoS-balanced service placement and task offloading (BQSPTO). By integrating a Delay, Security, and Privacy-aware QoS (DSPQoS) evaluation model with satellite-terrestrial collaboration, inter-satellite collaboration, and service migration, the scheme realizes the joint optimization of cloud-edge-end collaborative service placement and task offloading. It not only can fully schedule node resources in the network to complete service placement and further provide more efficient task offloading solutions but also ensures the requirements of task latency, security, and privacy levels. Methods The proposed scheme integrates the satellite-terrestrial edge node service placement plan, cloud-edge-end collaborative task offloading strategy, and DSPQoS evaluation model, with its specific implementation divided into three steps. First, a satellite-terrestrial network model is established through collaboration among the terminal side, edge side, and cloud servers. The task attack avoidance probability is defined by considering the probability of task key cracking, and the task privacy level is defined based on task model and location privacy. A QoS evaluation model is constructed by integrating task completion latency, attack avoidance probability, and privacy level. Second, a satellite-terrestrial edge node service placement plan is designed based on task popularity prediction and service migration. A cloud-edge-end collaborative complete offloading strategy is developed by determining offloading locations and multi-node collaboration modes according to QoS values. Based on the satellite-terrestrial edge node service placement and cloud-edge-end collaborative task offloading strategy, an optimization problem is modeled with the objective of maximizing the total task QoS value. Third, the joint optimization problem is decoupled into service placement and task offloading subproblems. The subproblems are solved respectively by a multi-dimensional QoS-based non-dominated sorting genetic algorithm for service placement and a cloud-edge-end collaborative task offloading algorithm integrating Whale Optimization Algorithm and Grey Wolf Optimizer, with alternating optimization adopted to complete the algorithm solution. Results and Discussions The QoS performance of task processing in the proposed BQSPTO scheme is verified via MATLAB. This study analyzes the cloud-edge-end collaborative task processing mode (Fig. 2) and constructs the overall BQSPTO scheme (Fig. 3). The performance of the proposed scheme is compared with three baseline strategies: GWOBQ (Grey Wolf Optimization Algorithm-based BQSPTO Scheme), BSSLM (BQSPTO Scheme Without Service Migration), and HWGWTO (Hybrid Grey Wolf Optimization with Whale Algorithm Fusion for Task Offloading). Simulation results demonstrate that BQSPTO achieves favorable convergence while being more capable of escaping local optima to obtain the optimal QoS value. In contrast, the QoS values of GWOBQ, BSSLM, and HWGWTO grow slowly, with their final convergence values significantly lower than that of BQSPTO (Fig. 4). The QoS values of all algorithms show an upward trend as the number of tasks increases, and the BQSPTO algorithm consistently outperforms the other algorithms throughout the increase in task quantity (Fig. 5). As the number of tasks rises, the latency, security, and privacy metrics of all algorithms increase. Among them, the BQSPTO algorithm achieves superior performance in these three metrics; however, due to the characteristic of multi-objective balance, there may be cases where a certain metric fails to reach the absolute optimal effect in the given scenario (Fig. 6). The QoS value gradually decreases with the increase in the number of malicious users (Fig. 7).In addition, as the capacity of a single satellite increases, the number of service types that can be placed on it rises, and the QoS value of BQSPTO remains leading during the improvement of satellite capacity (Fig. 8). Conclusions To address the issues of strong resource heterogeneity, complex multi-objective constraints, and challenges in service placement strategies and offloading decisions in multi-task scenarios within satellite-terrestrial integrated networks (STIN), this paper proposes a joint optimization scheme for service placement and task offloading based on cloud-edge-end collaboration to balance QoS. First, a multi-objective adaptive Delay, Security, and Privacy-aware QoS (DSPQoS) evaluation model, along with corresponding service placement and task offloading schemes, is constructed for STIN, and the collaborative logic between service placement and task offloading is designed. Second, the joint optimization problem is decoupled into service placement and task offloading subproblems; offloading decisions are solved based on service placement schemes, and alternating optimization is performed. Finally, with the goal of maximizing the total system QoS, a BQSPTO scheme based on cloud-edge-end collaboration for satellite-terrestrial integration is designed and verified through simulations. The results show that the proposed scheme significantly outperforms traditional algorithms in scenarios with different task quantities, malicious user scales, and satellite capacities, achieving the optimal system gains in terms of QoS performance, convergence stability, and multi-objective balancing capability.
- Satellite-Terrestrial Integrated Network /
- Service Placement /
- Task Offloading /
- Quality of Service

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图 1 基于云边端协同的星地融合网络模型

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图 2 任务处理方式

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图 3 BQSPTO方案示意图

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图 4 不同方案算法最大QoS值收敛曲线对比

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图 5 随任务数增加下的不同方案QoS值

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图 6 随任务数增加下不同方案的时延、安全、隐私收益图

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图 7 随恶意用户增加下的不同方案QoS值

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图 8 随卫星容量提高下的不同方案QoS值

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1 多维QoS非支配排序遗传的服务放置算法

输入:$ \text{tasks} $、$ \text{sm} $、$ \text{mds} $、$ {\text{pop}}_{\text{size}} $、$ \text{cr} $、$ \text{mr} $、$ {G}_{\max } $
输出:$ \text{Best}\_\text{Placement} $
(1) 基于卫星存储容量约束与任务流行度，初始化服务放置种群　$ \text{Pop} $，且$ \sum\text{Pop}\times k\leq s $，定义多目标指标计算函数：$ {\eta }_{n} $、　$ {Q}_{n} $，初始化全局精英方案$ {\text{Global}}_{\text{elite}} $、帕累托最优集合$ {\text{Pareto}}_{\text{set}} $
(2) 遍历迭代次数$ g $从1到$ {G}_{\max } $:
(3) for $ g=1 $ to $ G $ do
(4) 　for $ X=1 $ to $ Pop $ do
(5) 　计算适应度$ \text{Fitness}(X) $ end for
(6) 　进行非支配排序$ \text{Fronts} $与拥挤度计算$ \text{Growd}(X) $
(7) 　for $ {\text{Pop}}_{\text{size}}/2 $do
(8) 　生成父代$ \text{Parents} $ end for
(9) 　for $ \text{Parents}({X}_{1},{X}_{2}) $do
(10) 　交叉生成子代$ \text{Offsprings} $ end for
(11) 　for $ X $ in $ \text{Offsprings} $ do
(12) 　按卫星变异优化子代$ \text{Offsprings} $ end for
(13) 　合并$ \text{Pop} $、$ \text{Offsprings} $、$ {\text{Global}}_{\text{elite}} $
(14) 　If $ \text{current}\_\text{best}\_\text{fitness} $ > $ {\text{Global}}_{\text{elite}} $
(15) 　得到新一代种群$ \text{Pop} $，更新$ {\text{Pareto}}_{\text{set}} $、$ {\text{Global}}_{\text{elite}} $
(16) 　end if
(17) end for
(18) 迭代结束，从$ {\text{Pareto}}_{\text{set}} $中选择总QoS高的个体
(19) 输出:$ \text{Best}\_\text{Placement} $、$ {\text{Pareto}}_{\text{set}} $、总QoS曲线

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2 融合鲸鱼与灰狼优化云边端协作的任务卸载算法

输入:$ \text{tasks} $、$ \text{sm} $、$ \text{mds} $、$ \text{Best}\_\text{Placement} $、$ {\text{Wolves}}_{\text{size}} $、$ {\text{T}}_{\text{max}} $
输出:$ \text{Best}\_\text{offloading} $
(1) 基于卫星存储容量约束与任务流行度，初始化任务卸载灰狼　种群$ \text{Wolves} $，且$ V \leq s, \sum_{l=1}^{N} \mathbb{R}(X(f)=s) \Sigma N_{m=x, t} $、定义多　目标指标计算函数：$ {\eta }_{n} $、$ {Q}_{n} $，初始化全局最优个体$ {X}_{\alpha } $、次优　个体$ {X}_{\beta } $、第三优个体$ {X}_{\delta } $
(2) 遍历迭代次数$ t $从1到$ {T}_{\max } $:
(3) for $ t=1 $ to $ {T}_{\max } $do
(4) 　for $ X=1 $ to $ \text{Wolves} $do
(5) 　计算适应度$ \text{Fitness}(X) $end for
(6) 　更新个体，取前三名分别为$ {X}_{\alpha } $、$ {X}_{\beta } $、$ {X}_{\delta } $
(7) 　　计算收敛因子$ a=2-2*t/{T}_{\max } $，混合策略概率　　　　$ p=0.5+t/2T $
(8) 　for$ \text{Wolves} $ do
(9) 　　if $ \text{rand}() \lt P(t) $ (灰狼协作更新)
(10) 　　计算$ \overrightarrow{\boldsymbol{A}} $、$ \overrightarrow{\boldsymbol{C}} $得到$ \overrightarrow{\boldsymbol{X}}(t+1) $
(11) 　　else
(12) 　　计算$ \overrightarrow{\mathbf{Levy}} $、$ \overrightarrow{\mathbf{Lx}} $得到$ \overrightarrow{\boldsymbol{X}}(t+1) $
(13)　　 end if
(14) 　end for
(15) 　约束修复，确保狼群个体$ \mathrm{Wolves} $为任务有效解
(16) 　精英保留，保留适应度前$ \mathrm{Wolve}{\mathrm{s}}_{\mathrm{size}} $个体
(17) end for
(18) 迭代结束，选择$ {X}_{\alpha } $重塑为$ \text{Best}\_\text{offloading} $
(19) 输出:收敛曲线，保存$ \text{Best}\_\text{offloading} $

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3 BQSPTO方案算法

输入:$ \text{tasks} $、$ \text{sm} $、$ \text{mds} $、$ {\text{pop}}_{\text{size}} $、$ {\text{Wolves}}_{\text{size}} $、$ {\text{D}}_{\text{max}} $
输出:$ \text{Best}\_\text{Placement} $、$ \text{Best}\_\text{offloading} $
(1) 初始化核心参数与变量：生成初始服务放置策略Placement 　与任务卸载方案$ \text{offloading} $
(2) 定义联合优化原问题公式(21)、服务放置子问题公式(22)与　任务卸载子问题公式(23)
(3) 交替优化迭代循环$ d $，直至$ d={D}_{\max } $:
(4) for $ d=1 $ to $ D $do
(5) 　第t轮：固定任务卸载方案$ \text{offloading} $、求解服务放置子问　　　题(多维QoS非支配排序遗传的服务放置算法)
(6) 　第t轮：固定服务放置策略$ \text{Placement} $，求解任务卸载子问　　　题(融合鲸鱼与灰狼优化云边端协作的任务卸载算法)
(7) 　收敛准则判断，并更新迭代次数$ d=d+1 $，记录$ {\eta }_{n} $、　　　 $ {Q}_{n} $，保存$ {\text{offloading}}_{d} $、$ {\text{Placement}}_{d} $
(8) end for
(9) 迭代结束，选择总QoS值最大的$ \text{Placement} $、$ \text{offloading} $组合　　作为$ \text{Best}\_\text{Placement} $和$ \text{Best}\_\text{offloading} $
(10) 输出并保存:$ \text{Best}\_\text{Placement} $、$ \text{Best}\_\text{offloading} $

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表 1 仿真参数表^[23–24]

参数	数值	参数	数值
$ U $	40	$ {f}_{i,0} $	[0.5, 1] Gcycles/s
$ {M}_{k} $	[200, 600] KB	$ {f}_{i,j} $	50 Gcycles/s
$ K $	10	$ {f}_{i,s} $	40 Gcycles/s
$ {\text{Cac}}_{s} $	1200 KB	$ {f}_{i,c} $	10 Pcycles/s
$ {a}_{i} $	[0.2, 5] Mbits	$ {B}_{i,j} $	500 MHZ
$ {c}_{i} $	[1000, 1500] cycles/bit	$ p_{i}^{\text{D}} $	0.5 W
$ {O}_{k} $	5	$ \gamma $	4
$ c $	3*10⁸ m/s	$ {N}_{{{0}_{i,j}}} $	–174 dBm/Hz

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