摘要
为有效支撑网络和计算深度融合的发展需求,新型的算力网络架构应运而生。在此背景下,如何实现算网资源的智能感知以及计算任务的高效调度,是当前网络需要解决的关键问题。为此,分析了面向算网融合的新型网络场景,设计了计算任务与算力节点的调度模型,提出了一种基于深度强化学习的资源调度算法。所提算法通过感知用户设备、算网资源可用容量和链路状态等关键信息,能够智能地做出系统成本最小的调度决策。最后,通过仿真实验验证了所提算法在节约系统成本方面的有效性。
To effectively support the development needs of the deep integration of networking and computing,a novel computing and network convergence architecture has emerged.In this context,how to realize the intelligent perception of computing and networking resources and the efficient scheduling of computational tasks are key problems.To this end,the new network scenario for computing and networking convergence were analyzed,a scheduling model for computing tasks and nodes was designed,and a deep reinforcement learning-based resource scheduling algorithm was proposed.The proposed algorithm was able to intelligently make scheduling decisions that minimize the system cost by sensing key information such as user devices,available capacity of computing and networking resources,and link status.Finally,the effectiveness of the proposed algorithm in saving system cost was verified by simulation experiments.
作者
张维庭
孙呈蕙
王洪超
代嘉宁
ZHANG Weiting;SUN Chenghui;WANG Hongchao;DAI Jianing(National Engineering Research Center of Advanced Network Technologies,Beijing Jiaotong University,Beijing 100044,China)
出处
《电信科学》
2023年第9期12-20,共9页
Telecommunications Science
基金
国家自然科学基金资助项目(No.62201029)
中国博士后科学基金资助项目(No.2022M710007,No.BX20220029)。
关键词
算力网络
资源调度
深度强化学习
computing and network convergence
resource scheduling
deep reinforcement learning
