Intelligent Routing Orchestration for Ultra-Low Latency Transport Networks

Qingmin Meng,Xiaoming Wang,Haiyan Guo,Jingcheng Wei

doi:10.1109/access.2020.3008721

Abstract

Autonomous driving scenarios face the need for millisecond real-time response, which has led to the study of mobile networks with high speed and ultra-low latency. Software-defined networking (SDN) is recognized as a key technology for next-generation networks because it contains advanced functions such as centralized control, software-based traffic analysis, and forwarding rules for dynamic updates. In this paper, an SDN with flexible architecture is considered and a transport component is proposed. The component based on mesh topology is an example of joint route prediction and forwarding. First, different from existing transport protocols, the component can adopt a software-defined stream access control strategy that includes an extended forwarding mechanism (retransmission) to improve the short-term response performance. Second, we evaluate the impact of route prediction on transport network performance by using offline training and prediction. The key challenge here is that a suitable model needs to be trained from a limited training sample dataset, which will dynamically update the forwarding rules based on current and historical facts (network data). By introducing a parallel neural network classifier, an intelligent route arrangement is implemented in this work. Experimental results over different traffic patterns verify the advantages of the design. Not only does it enhance the flexibility of SDN, but it also significantly reduces the signaling overhead of the transport network without reducing the network throughput.

Highlights

Since 2018 autonomous driving technology has developed rapidly in China and the United States
We propose a routing orchestration method embedded in machine learning, which can enhance the agility of traffic engineering [5]
By using the output from the predictors in the Software-defined networking (SDN) central controller, a series of actions on various nodes within the transport component is periodically triggered. This series of actions ensures that the delay in the transport network is within a controllable range. Aiming for this goal, we summarize what we want to achieve into a comprehensive traffic control problem, which involves routing and flow rules in the control plane (CP)

Summary

INTRODUCTION

Since 2018 autonomous driving technology has developed rapidly in China and the United States. Machine learning facilitates the SDN central controller to track seemingly random trends, and make effective decisions to complete intelligent configuration management and service orchestration in a network programmable process, which is the subject of this work. Path orchestration with small time granularity facilitates support for ultra-low latency service scenarios but requires a large overhead of controlled information and high computational complexity for online analysis [6]. The central controller uses a parallel deep neural network classifier to predict the path sequence in the time window, to effectively achieve fast inference and reduce the control information cost.

RELATED LITERATURE

CLASSIFIER COMPLEXITY AND MODELS

SIMULATION OF TRANSPORT NETWORKS

CONCLUSION