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

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 micro-manage 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.