In the ongoing digital transformation, telecommunications companies are shifting from Virtual Network Functions (VNFs) to Cloud-Native Network Functions (CNFs) to meet the demand for agile, scalable, and resilient services. Deutsche Telekom is at the forefront of this transition, moving its network services onto a self-hosted bare-metal cloud infrastructure using Kubernetes as the core platform for container orchestration.

Kubernetes, widely recognized for its robust orchestration capabilities, is the foundation of Deutsche Telekom's cloud-native strategy. However, as network services are usually complex software solutions, deploying and provisioning CNFs pose several orchestration challenges that may require additional tooling. Various tools on the market are designed to manage these orchestration complexities, but the necessity and efficiency of such tools in a Kubernetes-based environment remain an open question.

This thesis seeks to answer the following question: "Is an additional orchestration tool necessary for managing CNF deployments in Kubernetes, or can a custom Kubernetes operator effectively address these orchestration needs?". The purpose of this master's thesis is to evaluate whether a dedicated orchestration tool is needed when deploying and managing CNFs in a Kubernetes setup, where Kubernetes already acts as an orchestrator. This thesis will also explore the design and development of a Kubernetes operator as a potential alternative to using an external orchestration tool.

For more details, please check the PDF with the thesis description

Prerequisites

Motivation:

Deutsche Telekom is operating and constantly developing and improving it’s own cloud to operate internet and telephony services (Telco applications). The Kubernetes-based cloud and the Telco applications are combined to form a TaaP - Telco as a Platform. The TaaP are thousands of servers and hundreds of applications. The energy efficiency of the TaaP is a key success criterion for optimizing costs, energy consumption, and carbon emissions. Hence, a concept of Full Stack Energy Management is established. The focus is to jointly optimize hardware, software, and services towards energy efficiency without affecting service availability and robustness. 

Problem & Challenge:

In the Telco industry, so far, HW redundancy has been the baseline for service robustness and resilience. The introduction of virtualization and containerization concepts resulted in an additional redundancy level above the hardware. Classical redundancy models no longer apply to this multi-layer Software redundancy. Moreover, so far, there is no mathematical model that captures the service availability for these new architectures. For example, a Telco-Service can be provided via 3 data centers with 50 servers each, forming 1 cluster hosting 500 Kubernetes pods. There is a mix of data center redundancy, hardware redundancy in the data centers, and Kubernetes worker node and pod redundancy.

Specific Problem Formulation:

On a TaaP there are multiple layers of redundancy in Hardware and Software. On the one hand, there are multiple site deployments, where each site has multiple hundreds of servers. On the other hand, on each site, each server has multiple redundant hardware parts, like the power supply. Moreover, a Kubernetes Cluster, which is homed on one site, hosts multiple microservices, each with a different redundancy concept like active/passive, n+1, n+m, etc. This setup of mixed HW and SW redundancy causes inefficiency and is not easy to calculate or simulate in terms of overall service availability, network, site, redundancy, and energy consumption. 

Solution Approach:

There are multiple different parameters in HW and SW that impact the service availability and energy consumption. Firstly, a comprehensive list of these parameters is required, including modeling of dependencies. Secondly, a mathematical model needs to be set up that considers all of these parameters in "one equation". Thirdly, a graphical simulation should be set up to demonstrate the dependencies and results.

Expected Outcome:

A simulation and mathematical model should be developed that considers software and hardware redundancy across multiple sites and SW layers to calculate the network-wide service availability. This is key in order to further optimize the HW and SW footprint and improve sustainability. Moreover, the model should allow the optimization of the following parameters: least required HW based on predefined service availability, least energy consumption, and best redundancy.

Working on the live network or setting up a lab deployment is out of scope of this thesis. The focus of this thesis are the modeling and simulation of the deployment.

Prerequisites