Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:PS4
Session:
Number:PS4-08
Adaptive Ensemble Learning-Based Network Resource Workload Prediction for VNF Lifecycle Management
Khizar Abbas, Jae-Hyoung Yoo, James Won-Ki Hong,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
Nowadays, Machine Learning (ML) approaches gain a lot of intention for automating and managing Software-Defined Networking, and Network Function Virtualization (SDNNFV) enabled networks. These networks are highly dynamic and flexible due to centralized control and scaleable Virtual Network Functions (VNFs). But the automatic management of the VNF lifecycle in a data center is still challenging; it includes several tasks such as VNF resource usage prediction, placement, consolidation, autoscaling, live migration, etc. So, accurately predicting VNF resource usage can be used for the several tasks mentioned above for performing VNF lifecycle management. It can also help Mobile Network Operators (MNOs) to ensure QoS by reducing Service-Level-Agreement (SLAs) violations. This article introduces an efficient mechanism that uses Adaptive Ensemble Learning to predict resource usage of virtual network functions. This mechanism has three modules: Machine-Learning Predictors (MLPs), Predictor Selector (PS), and Predictor Combiner (PC). The MLPs module contains several ML models for performing prediction. The PS module has a pretrained Random Forest model that is used to choose the best predictors from the MLPs. The PC module combines the selected predictors using an ensemble learning mechanism to generate the final prediction. In tests on three datasets, our method achieved a high R2 = 0.96 for predicting CPU utilization and R2 = 0.97 for predicting memory utilization.