计算机科学
利用
资源管理(计算)
资源配置
网络拥塞
强化学习
延迟(音频)
分布式计算
跑
计算机网络
机器学习
电信
计算机安全
网络数据包
作者
Xiaoxi Zhang,Jinhang Zuo,Zhe Huang,Zhi Zhou,Xu Chen,Carlee Joe‐Wong
标识
DOI:10.1109/jsac.2023.3339174
摘要
The open radio access network (O-RAN) architecture provides enhanced opportunities for integrating machine learning in 5G/6G resource management by decomposing RAN functionalities. Yet, generic learning mechanisms either do not fully exploit the disaggregated non-real-time and near-real-time RAN controllers or ignore the potential elasticity of application demands, another degree of freedom in managing RAN resources. We introduce a two-timescale framework aimed at optimizing users’ long-term total QoS. Rather than reactive resource allocation, our approach proactively modifies multi-resource user demands using congestion indicators, prior to enforcing any allocation rules. Addressing the issue of insufficient user feedback on individual resource utilities, we employ a bandit-feedback version of the combinatorial multi-armed bandit framework to deduce resource-specific signals. Also, to compensate for insufficient and infrequent feedback, we’ve developed an algorithm that gleans side information from live network traffic to refine predictions on user resource sensitivities. This streamlines the algorithm’s optimality convergence and leverages the two-tier O-RAN controller structure. We validate our algorithms’ efficacy through analysis and 5G usage experiments, revealing our proposed method improves application utility by 13-60%, throughput by 8-19%, and reduces latency by 10-18%.
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