AIRIC: Orchestration of Virtualized Radio Access Networks with Noisy Neighbours

TL;DR

This paper introduces AIRIC, an AI-driven controller for virtualized Radio Access Networks that effectively manages resources amidst noisy neighbors, reducing overhead and outperforming existing benchmarks.

Contribution

The paper presents a novel hybrid neural network architecture for orchestrating vRAN resources, addressing noisy neighbor issues and dynamic network demands.

Findings

AIRIC achieves up to 30% resource savings.

AIRIC closely matches offline optimal performance.

Outperforms existing benchmarks in service guarantees.

Abstract

Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing overhead introduced by noisy neighbors problem when multiple virtualized base station instances share computing platforms. In this paper, first, we thoroughly dissect the multiple sources of computing overhead in a vRAN, quantifying their different contributions to the overall performance degradation. Second, we design an AI-driven Radio Intelligent Controller (AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid neural network architecture combining a relation network (RN) and a deep Q-Network (DQN) such that: (i) the demand of concurrent virtual base stations is satisfied considering the overhead posed by the noisy neighbors problem while the operating costs of the vRAN infrastructure is minimized; and (ii) dynamically changing contexts in terms of network demand, signal-to-noise ratio (SNR) and the number of base station instances are efficiently supported. Our results show that AIRIC performs very closely to an offline optimal oracle, attaining up to 30% resource savings, and substantially outperforms existing benchmarks in service guarantees.