In this article, a learning-based proactive resource sharing scheme is proposed for the next-generation core communication networks, where the available forwarding resources at a switch are proactively allocated to the traffic flows in order to maximize the efficiency of resource utilization with delay satisfaction. The resource sharing scheme consists of two joint modules, estimation of resource demands and allocation of available resources. For service provisioning, resource demand of each traffic flow is estimated based on the predicted packet arrival rate. Considering the distinct features of each traffic flow, a linear regression algorithm is developed for resource demand estimation, utilizing the mapping relation between traffic flow status and required resources, upon which a network switch makes decision on allocating available resources for delay satisfaction and efficient resource utilization. To learn the implicit relation between the allocated resources and delay, a multi-armed bandit learning-based resource allocation scheme is proposed, which enables fast resource allocation adjustment to traffic arrival dynamics. The proposed algorithm is proved to be asymptotically approaching the optimal strategy, with polynomial time complexity. Extensive simulation results are presented to demonstrate the effectiveness of the proposed resource sharing scheme in terms of delay satisfaction, traffic adaptiveness, and resource allocation gain.

中文翻译：

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延迟敏感包传输的基于学习的主动资源分配

在本文中，针对下一代核心通信网络提出了一种基于学习的主动资源共享方案，其中将交换机可用的转发资源主动分配给业务流，以最大限度地提高资源利用效率并满足延迟. 资源共享方案由两个联合模块组成，资源需求估计和可用资源分配。对于服务提供，每个业务流的资源需求是基于预测的数据包到达率来估计的。考虑到每个交通流的不同特点，利用交通流状态与所需资源的映射关系，提出了一种线性回归算法进行资源需求估计，网络交换机据此决定分配可用资源以实现延迟满足和有效资源利用。为了了解分配的资源与延迟之间的隐含关系，提出了一种基于多臂老虎机学习的资源分配方案，该方案能够根据交通到达动态快速调整资源分配。证明该算法渐近逼近最优策略，时间复杂度为多项式。大量的仿真结果展示了所提出的资源共享方案在延迟满足、交通适应性和资源分配增益方面的有效性。提出了一种基于多臂老虎机学习的资源分配方案，可以根据交通到达动态快速调整资源分配。证明该算法渐近逼近最优策略，时间复杂度为多项式。大量的仿真结果展示了所提出的资源共享方案在延迟满足、交通适应性和资源分配增益方面的有效性。提出了一种基于多臂老虎机学习的资源分配方案，可以根据交通到达动态快速调整资源分配。证明该算法渐近逼近最优策略，时间复杂度为多项式。大量的仿真结果展示了所提出的资源共享方案在延迟满足、交通适应性和资源分配增益方面的有效性。