智算网络中面向QoS感知的资源调度技术研究

赵明

doi:10.11959/j.issn.1000-0801.DXKX260062

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智算网络中面向QoS感知的资源调度技术研究

更新时间：2026-07-22

- 智算网络中面向QoS感知的资源调度技术研究
- Research on QoS-aware resource scheduling for AI computing networks
- 电信科学 2026年页码：1-16
- 作者机构：
  
  中信数字科技集团有限公司，北京 100004
- 作者简介：
  
  赵明（1980- ），男，中信数字科技集团公司高级工程师，主要研究方向为云计算、数据通信、网络信息与安全、人工智能、数据中心等。
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260062
  中图分类号： TN92
- 收稿：2026-01-23，
  
  修回：2026-02-27，
  
  录用：2026-02-28，
  
  网络首发：2026-07-22，
- 稿件说明：
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赵明.智算网络中面向QoS感知的资源调度技术研究[J].电信科学,

Zhao Ming.Research on QoS-aware resource scheduling for AI computing networks[J].Telecommunications Science,
赵明.智算网络中面向QoS感知的资源调度技术研究[J].电信科学, DOI：10.11959/j.issn.1000−0801.DXKX260062.

Zhao Ming.Research on QoS-aware resource scheduling for AI computing networks[J].Telecommunications Science, DOI：10.11959/j.issn.1000−0801.DXKX260062.

摘要

为保障大语言模型推理与多智能体协同等新兴智能业务对服务质量（quality of service，QoS）的需求，构建高效的资源调度机制已成为智算网络中的核心问题。面向QoS多目标优化下的资源调度场景，设计了一种融合差异化智能业务建模、异构算网资源感知与高通量远程直接内存访问（remote direct memory access，RDMA）传输优化的调度框架；在该框架下提出基于强化学习（reinforcement learning，RL）的智能调度算法，在考虑业务优先级、算网资源负载与数据传输特性的基础上，自适应生成调度策略以提升QoS；引入网络实测数据与智算中心真实组网参数构建仿真平台，对所提算法进行性能验证。实验结果表明，该方法在任务完成率、平均时延和资源利用率等关键QoS指标上较现有方案具有明显优势。

Abstract

To meet the quality of service (QoS) requirements of emerging intelligent services such as large language model inference and multi-agent collaboration

constructing an efficient resource scheduling mechanism has become a core issue in AI computing networks. For the resource scheduling scenario under QoS multi-objective optimization

a scheduling framework that integrates differentiated intelligent service modeling

heterogeneous computing-network resource awareness

and high-throughput remote direct memory access (RDMA) transmission optimization was proposed. Within this framework

a reinforcement learning (RL)-based intelligent scheduling algorithm was developed to adaptively generate scheduling policies by considering service priorities

computing-network resource loads

and data transmission characteristics

thereby improving QoS. Finally

a simulation platform was constructed using real-world network measurement data and actual topology parameters from an AI computing center to verify the performance of the proposed algorithm. Experimental results demonstrate that the proposed method achieves significant improvements over existing schemes in key QoS metrics such as task completion rate

average latency

and resource utilization.

关键词

Keywords

references

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