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 ...
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.
