Learning-Based Proactive Resource Allocation for Delay-Sensitive Packet Transmission

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.