Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs

TL;DR

This paper introduces LOFV, a learning-based framework for dynamic functional split and resource allocation in vRANs, significantly reducing management costs through reinforcement learning.

Contribution

It presents a novel zero-touch orchestration framework that jointly optimizes functional splits and resource allocation using model-free reinforcement learning, based on real network measurements.

Findings

LOFV reduces management costs by up to 69% compared to static policies.

The framework effectively captures non-linear demand-resource relationships.

Numerical evaluations demonstrate significant cost savings.

Abstract

One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challenges. In this paper, a learning-based zero-touch vRAN orchestration framework (LOFV) is proposed to jointly select the functional splits and allocate the virtualized resources to minimize the long-term management cost. First, testbed measurements of the behaviour between the users' demand and the virtualized resource utilization are collected using a centralized RAN system. The collected data reveals that there are non-linear and non-monotonic relationships between demand and resource utilization. Then, a comprehensive cost model is proposed that takes resource overprovisioning, declined demand, instantiation and reconfiguration into account. Moreover, the proposed cost model also captures different routing and computing costs for each split. Motivated by our measurement insights and cost model, LOFV is developed using a model-free reinforcement learning paradigm. The proposed solution is constructed from a combination of deep Q-learning and a regression-based neural network that maps the network state and users' demand into split and resource control decisions. Our numerical evaluations show that LOFV can offer cost savings by up to 69\% of the optimal static policy and 45\% of the optimal fully dynamic policy.