Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN

Abstract

Efficient computational resource management in 5G Cloud Radio Access Network (C-RAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. The assumption of a fixed computational capacity at the baseband unit (BBU) pools may result in underutilized or oversubscribed resources, thus affecting the overall Quality of Service (QoS). As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). In this paper, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). To further improve, two new strategies are proposed and tested in a realistic scenario: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 98 % and 99.9 % compared to the DRM-AC, respectively.

Keywords

• Adaptive resource management
• C-RAN
• machine-learning