Enhancing the performance of mobile networks using Kubernetes : Load balancing traffic by utilizing workload estimation

Abstract: As global mobile network usage increases rapidly and users demand lower latency, the importance of stable 5G networks is more critical than ever. One way to orchestrate mobile network backends is by using Kubernetes. Kubernetes allows for automatic restarts and scaling of containers and provides an easy way to route incoming connections to applications running in containers. By routing the incoming connections using different load-balancing algorithms, it is possible to reduce latency through more efficient usage of worker nodes.  This thesis aims to identify ways to use load balancing inside a Kubernetes cluster to increase throughput and reduce latency in a mobile network system. We perform a literature study on possible ways to implement load balancing in Kubernetes and possible algorithms to use in the load balancing. Using the study results, we model a simplified mobile network system in a Kubernetes cluster and implement a load balancer at the Service level. By running simulations on this model, we compare three algorithms existing in Kubernetes as well as a dynamic algorithm using estimated workloads in terms of latency and throughput. The existing algorithms that are compared include Round Robin, Least Connections, and Random. The results show a potential to reduce latency by up to 31% compared to the native Random algorithm when utilizing a dynamic load balancer at the Service level.