Wide Area Network Intelligence with Application to Multimedia Service
This work addresses network intelligence for multimedia services in wide area networks, but it appears incremental as it builds on existing machine learning approaches with specific architectural modifications.
The authors tackled the problem of improving multimedia service quality in wide area networks by proposing a machine learning-based system with dual-hemisphere models, achieving superior accuracy, latency, and communication compared to deep feed-forward neural networks in data centers, with scalable improvements as terminal machines increase.
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.