2 research outputs found
An End-to-End Performance Analysis for Service Chaining in a Virtualized Network
Future mobile networks supporting Internet of Things are expected to provide
both high throughput and low latency to user-specific services. One way to
overcome this challenge is to adopt Network Function Virtualization (NFV) and
Multi-access Edge Computing (MEC). Besides latency constraints, these services
may have strict function chaining requirements. The distribution of network
functions over different hosts and more flexible routing caused by service
function chaining raise new challenges for end-to-end performance analysis. In
this paper, as a first step, we analyze an end-to-end communications system
that consists of both MEC servers and a server at the core network hosting
different types of virtual network functions. We develop a queueing model for
the performance analysis of the system consisting of both processing and
transmission flows. We propose a method in order to derive analytical
expressions of the performance metrics of interest. Then, we show how to apply
the similar method to an extended larger system and derive a stochastic model
for such systems. We observe that the simulation and analytical results
coincide. By evaluating the system under different scenarios, we provide
insights for the decision making on traffic flow control and its impact on
critical performance metrics.Comment: 30 pages. arXiv admin note: substantial text overlap with
arXiv:1811.0233
A Graph Neural Network-based Digital Twin for Network Slicing Management
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordNetwork slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualised infrastructure and stringent Quality-of-Service (QoS) requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviours and predict the time-varying performance. In this paper, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel Graph Neural Network model that can learn insights directly from slice-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.Engineering and Physical Sciences Research Council (EPSRC