Wide Area Network Intelligence with Application to Multimedia Service

TL;DR

This paper introduces a machine learning-based system for wide area network intelligence that enhances multimedia service delivery by improving accuracy, latency, and communication efficiency, scalable with more terminal machines.

Contribution

It proposes a novel dual-hemisphere neural model for WAN intelligence, combining pre-training and terminal response to optimize multimedia service performance.

Findings

Outperforms deep neural networks in accuracy, latency, and communication.

Scales effectively with more terminal machines.

Longer training time is a trade-off for improved performance.

Abstract

Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.