Motivation:

Deutsche Telekom is operating and constantly developing and improving its own cloud to operate internet and telephony services. The Kubernetes 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 in order to optimize costs, energy consumption, and carbon emissions. Hence the concept of Full Stack Energy Management is established. The focus is to 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 don’t apply to this multi-layer redundancy any longer. Moreover, there is no mathematical model that calculates the service availability for such a case.

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 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, 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 model needs to be set up to consider all of these parameters into “one equation”.

Expected Outcome:

A simulation and mathematical model should be developed that considers software and hardware redundancy across multiple sites and SW layers in order to calculate the network-wide service availability. 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.

Prerequisites

5G networks brought significant performance improvements for different service types like augmented reality, virtual reality, online gaming, live video streaming, robotic surgeries, etc., by providing higher throughput, lower latency, higher reliability as well as the possibility to successfully serve a large number of users. However, these improvements do not come without any costs. The main consequence of satisfying the stringent traffic requirements of the aforementioned applications is excessive energy consumption.

Therefore, making the cellular networks sustainable, i.e., constraining their power consumption, is of utmost importance in the next generation of cellular networks, i.e., 6G. This goal is of interest mostly to cellular network operators. Of course, while achieving network sustainability, the satisfaction of all traffic requirements, which is of interest to cellular users, must be ensured at all times. While these are opposing goals, a certain balance has to be achieved.

In this thesis, the focus is on the type of services known as eMBB (enhanced mobile broadband). These are services that are characterized as latency-tolerant to a certain extent, but sensitive to the throughput and its stability. Live video streaming is a use case falling into this category. For these applications, on the one side, higher data rates imply higher energy consumption. On the other side, the users can be satisfied with slightly lower throughput as long as the provided data rate is constant, which corresponds to the flexibility that the network operator can exploit. Hence, the question that needs to be answered in this thesis is what is the optimal trade-off between the data rate and the energy consumption in a cellular network with eMBB users? To answer this question, the entire communication process will be encompassed, i.e., from the transmitting user through the base station and core network to the receiving end. The student will need to formulate an optimization problem to address the related problem, which they will then solve through exact optimization solvers, but also through proposing simpler algorithms (heuristics) that reduce the solution time while not considerably deteriorating the system performance.

Prerequisites