An Adaptive CMT-SCTP Scheme: A Reinforcement Learning Approach

Abstract

With the continuous increase of end users and types of services, the scale of the network has shown explosive growth, which has brought tremendous pressure and challenges to network data transmission. How to achieve high-quality data transmission has become a core issue. Single-path transmission has been difficult to meet the above requirements. The concurrent multipath transfer extension for stream control transmission protocol (CMT-SCTP), which supports multipath and independent data streams, can solve this problem. However, the current transmission path assessment scheme has too large granularity to make full use of the resources of the transition zone. Most studies ignore the different requirements of different services, a single transmission strategy, and the lack of an intelligent dynamic adjustment mechanism. Therefore, we designed a QCMT(Q-learning based CMT-SCTP) scheduling method. This method considers the multi-dimensional characteristics of the path and the characteristic preferences of different services, periodically evaluates and trains the reinforcement learning model for service adaptation, and makes scheduling decisions dynamically. Experimental results show that dynamic scheduling based on path parameters and service preferences can reduce message delay and improve network throughput.