Fast and accurate edge resource scaling for 5G/6G networks with distributed deep neural networks

Abstract

Network slicing has been proposed as a paradigm for 5G+ networks. The operators slice physical resources from the edge, all the way to datacenter, and are responsible to micromanage the allocation of these resources among tenants bound by predefined Service Level Agreements (SLAs). A key task, for which recent works have advocated the use of Deep Neural Networks (DNNs), is tracking the tenant demand and scaling its resources. Nevertheless, for edge resources (e.g. RAN), a question arises whether operators can: (a) scale edge resources fast enough (often in the order of ms) and (b) afford to transmit huge amounts of data towards a cloud where such a DNN-based algorithm might operate. We propose a Distributed-DNN architecture for a class of such problems: a small subset of the DNN layers at the edge attempt to act as fast, standalone resource allocator; this is coupled with a Bayesian mechanism to intelligently offload a subset of (harder) decisions to additional DNN layers running at a remote cloud. Using the publicly available Milano dataset, we investigate how such a DDNN should be jointly trained, as well as operated, to efficiently address (a) and (b), resolving up to 60% of allocation decisions locally with little or no penalty on the allocation cost.