Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach

Abstract

Network slicing is designed to support a variety of emerging applications with diverse performance and flexibility requirements, by dividing the physical network into multiple logical networks. These applications along with a massive number of mobile phones produce large amounts of data, bringing tremendous challenges for network slicing performance. From another perspective, this huge amount of data also offers a new opportunity for the management of network slicing resources. Leveraging the knowledge and insights retrieved from the data, we develop a novel Machine Learning-based scheme for dynamic resource scheduling for networks slicing, aiming to achieve automatic and efficient resource optimisation and End-to-End (E2E) service reliability. However, it is difficult to obtain the user-related data, which is crucial to understand the user behaviour and requests, due to the privacy issue. Therefore, Deep Reinforcement Learning (DRL) is leveraged to extract knowledge from experience by interacting with the network and enable dynamic adjustment of the resources allocated to various slices in order to maximise the resource utilisation while guaranteeing the Quality-of-Service (QoS). The experiment results demonstrate that the proposed resource scheduling scheme can dynamically allocate resources for multiple slices and meet the corresponding QoS requirements.