Applying Machine Learning to End-to-end Slice SLA Decomposition

Abstract

5G is set to revolutionize the network service industry with unprecedented use-cases in industrial automation, augmented reality, virtual reality and many other domains. Network slicing is a key enabler to realize this concept, and comes with various SLA requirements in terms of latency, throughput, and reliability. Network slicing is typically performed in an end-to-end (e2e) manner across multiple domains, for example, in mobile networks, a slice can span access, transport and core networks. Thus, if an SLA requirement is specified for e2e services, we need to ensure that the total SLA budget is appropriately proportioned to each participating domain in an adaptive manner. Such an SLA decomposition can be extremely useful for network service operators as they can plan accordingly for actual deployment. In this paper we design and implement an SLA decomposition planner for network slicing using supervised machine learning algorithms. Traditional optimization based approaches cannot deal with the dynamic nature of such services. We design machine learning models for SLA decomposition, based on random forest, gradient boosting and neural network. We then evaluate each class of algorithms in terms of accuracy, sample complexity, and model explainability. Our experiments reveal that, in terms of these three requirements, the gradient boosting and neural network algorithms for SLA decomposition out-perform random forest algorithms, given emulated data sets.