Causal Online Learning of Safe Regions in Cloud Radio Access Networks

TL;DR

This paper introduces a causal online learning method for safely identifying operational regions in cloud RANs, ensuring network safety while efficiently expanding knowledge with minimal operational costs.

Contribution

It proposes COL, a novel causal online learning approach combining passive inference and active intervention to learn safe network configurations efficiently.

Findings

COL learns safe regions quickly with low operational cost.

It is up to 10x more sample-efficient than existing methods.

The method guarantees safety with high probability and converges to the full safe region.

Abstract

Cloud radio access networks (RANs) enable cost-effective management of mobile networks by dynamically scaling their capacity on demand. However, deploying adaptive controllers to implement such dynamic scaling in operational networks is challenging due to the risk of breaching service agreements and operational constraints. To mitigate this challenge, we present a novel method for learning the safe operating region of the RAN, i.e., the set of resource allocations and network configurations for which its specification is fulfilled. The method, which we call (C)ausal (O)nline (L)earning, operates in two online phases: an inference phase and an intervention phase. In the first phase, we passively observe the RAN to infer an initial safe region via causal inference and Gaussian process regression. In the second phase, we gradually expand this region through interventional Bayesian learning. We prove that COL ensures that the learned region is safe with a specified probability and that it converges to the full safe region under standard conditions. We experimentally validate COL on a 5G testbed. The results show that COL quickly learns the safe region while incurring low operational cost and being up to 10x more sample-efficient than current state-of-the-art methods for safe learning.