算网资源智能适配与融合调度方法

doi:10.11959/j.issn.1000-0801.2023174

Abstract

Abstract:

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.

Key words: computing and network convergence, resource scheduling, deep reinforcement learning, The National Natural Science Foundation of China, China Postdoctoral Science Foundation

CLC Number:

TP393

Weiting ZHANG, Chenghui SUN, Hongchao WANG, Jianing DAI. Intelligent adaptation and integrated scheduling method for computing and networking resources[J]. Telecommunications Science, 2023, 39(9): 12-20.

Figures/Tables 7

参数表示	具体描述	取值
$K_{bs}$	基站数量	5
$K_{nodes}$	算力节点数量	5
N	中心算力控制器数量	1
$w_{i}$	计算任务输入数据大小/KB	{300, 500, 700, 900, 1 100}
$c_{i}$	任务所需要的计算资源/cycle	1×10⁹
C	算力节点的计算能力/GHz	{4, 8, 12, 16, 20}
$C_{local}$	本地设备的计算能力/GHz	{0.5, 0.6, 0.7, 0.8, 0.9, 1.0}
B	上行链路带宽/MHz	15
$S / N_{0}$	上行链路信噪比/dB	{15, 20, 25, 30, 35}
$p_{local}^{Tr}$	本地设备的发射功率/mW	50
$p_{local}^{wait}$	本地设备的静态功率/mW	10
$λ^{T} 、 λ^{E}$	用户偏好权重	0.5、0.5

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