Abstract
The Internet of Things (IoT) universe will continue to expand with the advent of the sixth
generation of mobile networks (6G), which is expected to support applications and services with higher
data rates, ultra-reliability, and lower latency compared to the fifth generation of mobile networks (5G).
These new demanding 6G applications will introduce heavy load and strict performance requirements on the
network. Network Function Virtualization (NFV) is a promising approach to handling these challenging requirements,
but it also poses significant Resource Allocation (RA) challenges. Especially since 6G network
services will be highly complicated and comparatively short-lived, network operators will be compelled to
deploy these services in a flexible, on-demand, and agile manner. To address the aforementioned issues,
microservice approaches are being investigated, in which the services are decomposed and loosely coupled,
resulting in increased deployment flexibility and modularity. This study investigates a new RA approach
for microservices-based NFV for efficient deployment and decomposition of Virtual Network Function
(VNF) onto substrate networks. The decomposition of VNFs involves additional overheads, which have a
detrimental impact on network resources; hence, finding the right balance of when and how much decomposition
to allow is critical. Thus, we develop a criterion for determining the potential/candidate VNFs for
decomposition and also the granularity of such decomposition. The joint problem of decomposition and
efficient embedding of microservices is challenging to model and solve using exact mathematical models.
Therefore, we implemented a Reinforcement Learning (RL) model using Double Deep Q-Learning, which
revealed an almost 50% more normalized Service Acceptance Rate (SAR) for the microservice approach
over the monolithic deployment of VNFs.
generation of mobile networks (6G), which is expected to support applications and services with higher
data rates, ultra-reliability, and lower latency compared to the fifth generation of mobile networks (5G).
These new demanding 6G applications will introduce heavy load and strict performance requirements on the
network. Network Function Virtualization (NFV) is a promising approach to handling these challenging requirements,
but it also poses significant Resource Allocation (RA) challenges. Especially since 6G network
services will be highly complicated and comparatively short-lived, network operators will be compelled to
deploy these services in a flexible, on-demand, and agile manner. To address the aforementioned issues,
microservice approaches are being investigated, in which the services are decomposed and loosely coupled,
resulting in increased deployment flexibility and modularity. This study investigates a new RA approach
for microservices-based NFV for efficient deployment and decomposition of Virtual Network Function
(VNF) onto substrate networks. The decomposition of VNFs involves additional overheads, which have a
detrimental impact on network resources; hence, finding the right balance of when and how much decomposition
to allow is critical. Thus, we develop a criterion for determining the potential/candidate VNFs for
decomposition and also the granularity of such decomposition. The joint problem of decomposition and
efficient embedding of microservices is challenging to model and solve using exact mathematical models.
Therefore, we implemented a Reinforcement Learning (RL) model using Double Deep Q-Learning, which
revealed an almost 50% more normalized Service Acceptance Rate (SAR) for the microservice approach
over the monolithic deployment of VNFs.
| Original language | English |
|---|---|
| Number of pages | 18 |
| Journal | IEEE Access |
| Early online date | 21 Oct 2022 |
| DOIs | |
| Publication status | E-pub ahead of print - 21 Oct 2022 |