Network Planning in the Medical Context
Description
In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.
In this thesis, the task is to develop and evaluate an approach to dimension networking resources such as networking devices and processing units depending on the envisioned medical applications to be executed. This work is in cooperation with our partners at MITI (Hospital „Rechts der Isar“).
The result will be an approach to dimension and plan networks for future medical applications.
Prerequisites
· Motivation
· Ideally some experience in optimization problems
· Basic networking knowledge
· Basic programming skills
Supervisor:
Optimizing the Availability of Medical Applications
Description
In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.
In this thesis, the task is to develop and evaluate an approach to optimize the availability of medical applications, i.e., modular application functions (MAFs), when executed in the network. For that, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The optimizing approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications while considering the needed availability.
The result will be an evaluated placement approach for applications in the medical environment considering the availability.
Prerequisites
· Motivation
· Ideally some experience in solving optimization problems
· Basic networking knowledge
· Basic programming skills
Supervisor:
Minimizing the Power Consumption of Medical Applications
Description
In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.
In this thesis, the task is to develop and evaluate an approach to minimize the power consumptions of medical applications, i.e., modular application functions (MAFs), when executed in the network. For that, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The optimizing approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications while considering the power consumption.
The result will be an evaluated power minimizing approach for applications in the medical environment.
Prerequisites
· Motivation
· Ideally some experience in solving optimization problems
· Basic networking knowledge
· Basic programming skills
Supervisor:
In-Network Placement of Medical Applications
Description
In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.
In this thesis, the task is to place medical applications, i.e., modular application functions (MAFs), in the networking considering various parameters similar to [1]. For that, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The optimizing approach then leads to a specified distribution of the processing and networking resources, considering various important parameters.
The result will be an evaluated placement approach for applications in the medical environment.
[1] A. Hentati, A. Ebrahimzadeh, R. H. Glitho, F. Belqasmi and R. Mizouni, "Remote Robotic Surgery: Joint Placement and Scheduling of VNF-FGs," 2022 18th International Conference on Network and Service Management (CNSM), Thessaloniki, Greece, 2022, pp. 205-211, doi: 10.23919/CNSM55787.2022.9964591.
Prerequisites
· Motivation
· Ideally some experience in solving optimization problems
· Basic networking knowledge
· Basic programming skills
Supervisor:
Processing Priorization of MAF Chains in the Medical Context
Description
In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance. However, these processing resources are usually shared among many applications, which potentially leads to worse performance in terms of execution time, throughput, etc. . This is especially critical for applications such as autonomous driving, telemedicine or smart operations. Hence, the processing of more critical applications must be prioritized.
In this thesis, the task is to develop and evaluate a priorization approach for chains of modular medical applications, i.e., modular application functions (MAFs). Hereby, this work extends an already existing work, focusing on the placement of only single MAFs with prioritization. In this work, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The priorization approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications.
The result will be an evaluated priorization approach for applications in the medical environment.
Prerequisites
· Motivation
· Ideally some experience in solving optimization problems
· Basic networking knowledge
· Basic programming skills
Supervisor:
Detailed Requirement Analysis for the Reliability and Availability of Medical Network Communication
Description
6G soll als neuer und zukünftiger Mobilfunkstandard den Menschen in den Fokus rücken. Neben sehr geringen Latenzen und extrem großen Datenübertragungsraten wird das Netzwerk zuverlässiger, sicherer und dynamischer. Diese Eigenschaften sind besonders in der Medizin und der Medizingerätetechnik gefragt, um intelligente Datenübertragung zwischen Geräten und Menschen zu ermöglichen. Im Rahmen des Verbundprojektes „6G-life“ sollen zwei medizinische Demonstratoren entwickelt werden, die diese Netzwerkeigenschaften untersuchen. Ein Demonstrator ist ein robotisches Telediagnostiksystem zur Remote Untersuchung von Patienten. Ein zweiter Demonstrator beschäftigt sich mit der raumadaptiven Erfassung von Vitalparametern.
Ziel der Forschungspraxis ist die Ermittlung von medizintechnischen Anforderungen an die Netzwerkkommunikation hinsichtlich der Ausfallsicherheit. Dies beinhaltet eine Recherche gängiger Normen und Richtlinien, die bei der medizinischen Datenübertragung berücksichtigt werden müssen, sowie Maßnahmen, die zur Sicherstellung zuverlässiger Kommunikation getroffen werden können. Darüber hinaus sind Interviews mit Medizinern wünschenswert, um die Nutzerseite zu berücksichtigen.
Im Rahmen der Arbeit sollen folgende Punkte behandelt werden:
- Recherche zu gängigen Richtlinien und Mechanismen, die in der medizintechnischen Netzwerkkommunikation zur Ausfallsicherheit eingesetzt werden.
- Interviews mit Klinikpersonal
- Evtl. Messung mit den vorhandenen Testbeds
Diese FP wird in Kooperation mit unseren Partnern der MITI Gruppe am Krankenhaus "Rechts der Isar" durchgeführt.
Prerequisites
- Motivation
- Interesse an Medizintechnik
- Deutsch (zwingend erforderlich zwecks der Interviews)
- Grundlegende Kenntnisse über Netzwerkkonzepte
Supervisor:
Intel's IPU: Starting from the beginning
Description
Intel develops Network Devices consisting of an FPGA and a general purpose processor. These are the so called IPUs. The goal of this Thesis/Position is to get such an IPU (Intel IPU F2000X) up and running and evaluates its potential. Here, the goal is to program a custom IPU application and evaluate metrics like latency, throughput, and many more under varying circumstances.
Prerequisites
- Basic Knowledge Linux Terminal
- Basic Knowledge C/C++
- Basic Knowledge of and about FPGAs
Supervisor:
DPU as Measurement Cards and Load Generators
Description
Datacenters experience higher and more demanding Network Loads and Traffic. Companies like Nvidia developed special networking hardware to fulfill these demands (The Nvidia Bluefield Line-Up). These cards promise high throughput and high precision. The features required to achieve such tasks can also be used to use Bluefield Cards as potential measurement cards or as load generators.
The goal of this Thesis/Position is to evaluate the performance and feasibility of this approach
For more information, please contact me directly (philip.diederich@tum.de)
Prerequisites
- Basic Knowledge Linux Terminal
- Basic Knowledge Python
- Basic Knowledge C/C++
Supervisor:
Advancing Real-time Network Simulations to Real World Behaviour
Description
Testing real-time application and networks is very timing sensitive. It is very hard to get this precision and accuracy in the real-world. However, the real-world itself also behaves different then simualtions. Our Simulator behaves like the theory dictates and allows us to get these precise timing, but needs to be tested and exteded to behave more like a real-network would
Requirements
- Knowledge of NS-3
- Knowledge of Python
- Knowledge of C/C++
Please contact me for more information (philip.diederich@tum.de)
Supervisor:
Working Student - Real-Time Network Controller for Research
Description
Chameleon is a real-time network controller that guarantees packet latencies for admitted flows. However, Chameleon is designed to work in high performance environments. For research and development, a different approach that offers more debugging and extension capablites would suit us better.
Goals:
- Create Real-time Network Controller
- Controller needs to be easy to debug
- Controller needs to be easy to extend
- Controller needs to have good logging and tracing
Requirements:
- Advanced Knowledge of C/C++
- Advanced Knowledge of Python
Please contact me for more information (philip.diederich@tum.de)
Amaury Van Bemten, Nemanja Ðeri?, Amir Varasteh, Stefan Schmid, Carmen Mas-Machuca, Andreas Blenk, and Wolfgang Kellerer. 2020. Chameleon: Predictable Latency and High Utilization with Queue-Aware and Adaptive Source Routing. In The 16th International Conference on emerging Networking EXperiments and Technologies (CoNEXT ’20), December 1–4, 2020, Barcelona, Spain. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3386367. 3432879
Supervisor:
Controlling Stochastic Network Flows for Real-time Networking
Description
Any data that is sent in a real-time network is monitored and accounted for. This allows us with the help of some mathematical frameworks to calculate upper bounds for the latency of the flow. These frameworks and controllers often consider hard real-time guarantees. This means that every packet arrives in time every time. With soft real-time guarantees, this is not the case. Here, we are allowed to have some leeway
In this thesis, we want to explore how we can model and admit network flows that have a stochastical nature.
Please contact me for more information (philip.diederich@tum.de)!!
Supervisor:
Working Student: Framework for Testing Realtime Networks
Description
Testing a Network Controller, custom real-time protocols, or verifying simulations with emulations requires a lot of computing effort. This is why we are developing a framework that helps you run parallel networking experiments. This framework also increases the reproducibility of any networking experiment.
The main Task of this position is to help develop the general-purpose framework for executing parallel networking experiments.
Tasks:
- Continue developing the Framework for multi server / multi app usage
- Extend Web Capabilities of the Framework
- Automate Starting and Stopping
- Ease-of-use Improvements
- Test the functionality
Requirements:
- Knowledge of Python
- Basic Knowledge of Web-App Development (FastApi, React etc...)
- Basic Knowledge of System Architecture Development
Feel free to contact me per mail (philip.diederich@tum.de)
Supervisor:
Working Student Infrastructure Service Management
Description
We are seeking a highly motivated and detail-oriented Working Student to join our data center team. As a Working Student, you will assist in the daily operations of our data center, gaining hands-on experience in a fast-paced and dynamic environment.
Responsibilities:
Assist with regular data center tasks, such as.
- Rack and Stack equipment
- Cable Management and organization
- Perform basic troubleshooting and maintenance tasks
- Assist with inventory management
- Monitor data center systems and report any discrepancies or issues
- Create the basis for our Data Center Infrastructure Management
- Develop and maintain documentation of data center procedures and policies
- Perform other duties as required to support the data center operations
Requirements
- Availability to work 8 - 10 hours per week with flexible scheduling to accommodate academic commitments
- Basic knowledge of computer systems, networks, and data center operations
- Basic knowledge in Python
Supervisor:
Implementing and Evaluating 5G Roaming Scenarios in an Open Source Testbed
5G, Roaming, Core Network, Network Functions
Description
5G is the newest generation of mobile networks allowing for higher data-rates, lower latency and many new features like network slicing. Its central element is the 5G Core, which is a network of specialised Network Functions (NFs). One of these NFs is responsible for Roaming connections. Roaming allows subscribers to connect to the internet via other network operators’ networks if they have a roaming agreement. Between two Public Land Mobile Networks (PLMNs) there are two standardised Roaming modes: Local Break Out and Home Routed Roaming. For Local Break Out Roaming only the control plane of the home network is accessed from the visited network, while the user data is directly transmitted to the Data Network (DN). For Home Routed Roaming the user data is routed through the home network to the DN. The goal of this thesis is to implement both Roaming versions in an open source core network and compare them regarding chosen KPIs, e.g. latency or throughput. Open5GS would be the primary choice for the open source core network, as it supports Local Break Out Roaming already. Home Routed Roaming is not yet supported.
A major part of 5G Roaming is the Security Edge Protection Proxy (SEPP), a 5G NF designed to establish and maintain a secure control plane connection between two PLMNs. Implementing it, or extending the existing implementation of Open5GS, will be an important part of this work. The SEPP is connected to other NFs in the same PLMN via Service Based Interfaces (SBIs) and to other PLMN’s SEPPs via the N32 interface.
The biggest difference between the two Roaming versions lies in the data plane routing, so implementing the connection between two User Plane Functions (UPFs), the N9 interface, is necessary to connect two PLMNs. The newly introduced Inter PLMN User Plane Security (IPUPS) used for additional security on this connection is initially considered out-of-scope for this work, but may be added later on.
Objectives
1. Check Roaming functionalities of Open5GS
2. Implement missing Roaming functionalities into Open5GS
3. Run tests to investigate the differences between Home Routed and Local Break Out Roaming considering chosen KPIs
Prerequisites
• Basic understanding of 5G networks advantageous; especially of the 5G core network
- interest and motivation to learn the system are sufficient
• Programming knowledge in C useful (for Open5GS)
• Interest in Roaming functionalities
• Interest in security would be nice, but is not needed (not the main focus of the work
Contact
Oliver Zeidler (oliver.zeidler@tum.de)
Julian Sturm (Julian.Sturm@ZITiS.Bund.de)
Supervisor:
Experimental Evaluation of xApp-related Vulnerabilities in the O-RAN's RAN Intelligent Controller Implementation
O-RAN, Security, RAN Intelligent Controller
Description
In previous mobile network generations, Radio Access Networks (RAN) have been treated as a proprietary, closed network segment that is specific to every operator. To accelerate development and innovation, new initiatives such as the O-RAN ALLIANCE were born, aiming to split the RAN into different components and standardize the open interfaces that connect them.
Fundamentally, O-RAN leverages the concept of Software Defined RAN (SD-RAN) by decoupling the RAN data plane from the control plane and introducing several new RAN-controlling components. One of the central components is the near real-time RAN Intelligent Controller (nearRT-RIC), which manages the RAN (network slices, handovers, etc). The nearRT-RIC is designed to allow both the use of traditional, rule-based policies and Machine Learning or data-driven ones to optimize the RAN operation. The logic of these policies is encapsulated in applications called xApps that run on the nearRT-RIC platform and can read and modify different parameters of the RAN.
While providing opportunities for efficient resource management, the nearRT-RIC is also a prospective target for attackers, because of its control power over the RAN. Specifically, an attack vector is a malicious xApp that can interfere with other legitimate xApps running on the nearRT-RIC.
NearRT-RIC implementations are still in their infancy and suffer from bugs and security vulnerabilities. These vulnerabilities are also prevalent in open-source implementations such as O-RAN Software Community's (OSC) RIC [1], where malicious xApps may disrupt the nearRT-RIC operation. The H Release of the OSC nearRT-RIC suffers from two major vulnerabilities that can compromise the operation of the RIC and crash it [2]. Additionally, a crafted packet sent by an xApp can crash memcpy and implicitly the whole OSC nearRT-RIC [3]. Such vulnerabilities significantly hinder the wide-scale adoption and deployment of O-RAN.
Objectives
The goal of this student thesis is to reproduce the attacks discussed in [2] and [3] for the newer OSC nearRT-RIC I Release. Additionally, after reproducing the existing attacks and understanding the OSC RIC Platform, the student is expected to explore new attack attempts with the same goal of disrupting OSC nearRT-RIC. Special focus will be put on the critical components of the system, such as the Subscription Manager and Subscription Procedures, Routing Message Router, other xApps, and O1/A1/E2 Terminations.
---
[1] “O-RAN SC Projects,” https://docs.o-ran-sc.org/en/latest/projects.html#near-realtime-ran-intelligent-controller-ric, accessed: 2024-04-19.
[2] Hung, C.F., Chen, Y.R., Tseng, C.H., & Cheng, S.M. (2024). Security Threats to xApps Access Control and E2 Interface in O-RAN. IEEE Open Journal of the Communications Society, 5, 1197-1203.
[3] "Opening Critical Infrastructure: The Current State of Open RAN Security,” https://www.trendmicro.com/en us/research/23/l/the-current-state-of-open-ran-security.html, accessed: 2024-04-19.
Prerequisites
- Experience with Docker and Kubernetes
- Linux Knowledge
- C/C++ Knowledge is a plus
Contact
- Razvan-Mihai Ursu (razvan.ursu@tum.de)
- Dominik Brunke (Dominik.Brunke@ZITiS.bund.de)
Supervisor:
Working Student for Analysis, Modeling and Simulation of Communication Networks SS2024
Description
The primary responsibilities of a working student include assisting tutors in correcting programming assignments and answering questions in Moodle. Working time is 6-7 hours per week in the period from May to July.
Prerequisites
- Python knowledge
Contact
polina.kutsevol@tum.de
Supervisor:
Student Assistent for Wireless Sensor Networks Lab Summer Semester 2024
Description
The Wireless Sensor Networks lab offers the opportunity to develop software solutions for the wireless sensor networking system, targeting innovative applications. For the next semester, a position is available to assist the participants in learning the programming environment and during the project development phase. The lab is planned to be held on-site every Tuesday 15:00 to 17:00.
Prerequisites
- Solid knowledge in Wireless Communication: PHY, MAC, and network layers.
- Solid programming skills: C/C++.
- Linux knowledge.
- Experience with embedded systems and microcontroller programming knowledge is preferable.
Contact
yash.deshpande@tum.de
alexander.wietfeld@tum.de
Supervisor:
Demo Implementation: Network Planning For The Future Railway Communications
Demo, GUI, Web
This works consists on the implementation of a demo for the work on Network Planning For The Future Railway Communications
Description
This works consists on the implementation of a demo for the work on Network Planning For The Future Railway Communications.
The idea is to program a web GUI, where the users can plan the network and examine its performance under dynamic scenarios.
An example of the expected outcome can be found here.
Please send your CV and transcript of records.
Prerequisites
Basic knowledge on the following:
- Linux
- Python
- Web programming (GUI)
- GIT
Contact
Supervisor:
Development of a GUI for Monitoring and Debugging a Digital Twin of QKD Networks
GUI
Quantum key distribution (QKD) is a promising technology for providing secure communication also in the presence of powerful quantum computers. Due to its time-dependent behavior and multi-layer architecture, analysis of routing policies and network performance parameters can be done by emulation. Our implemented network emulator based on container and network function virtualization allows network performance parameters analysis and routing policy optimization.
Description
We search for a student to build a GUI, simplifying analysis and interaction with the network emulator. The emulator is based on Containernet and includes QKD-specific network function virtualization. Currently, distributed routing is supported but will be extended by centralized routing. Monitoring data from active QKD-links are fed in to mirror realistic circumstances.
- Build a front-end displaying performance and operational data
- Build a GUI for dynamically changing secret key rates
Prerequisites
- Programming skills in Python
- Experience in front-end web development
- Interest in security and practical concepts of guaranteed security
Contact
Mario Wenning mario.wenning@tum.de
Supervisor:
Sustainable Core Networks in 5G with Performance Guarantees
5G, 5G Edge, UPF, Optimization, Heuristic
Description
With the advent of 5G cellular networks, more stringent types of traffic, pertaining to applications like augmented reality, virtual reality, and online gaming, are being served nowadays. However, this comes with an increased energy consumption on both the user’s and network side, challenging this way the sustainability of cellular networks. Furthermore, the in-network computing aspect exacerbates things even further in that direction.
Hence, it is very important to provide end-to-end sustainability, i.e., minimize the energy consumption in the network while maintaining performance guarantees, such as the maximum latency each flow should experience. This can be done, for example, depending on the traffic load in the network, and in order to keep the energy usage at low levels, the operator can decide to shut off certain network components, like User Plane Functions (UPFs) or edge clouds, and reassign the tasks to other entities.
In this thesis, the focus will be on the core network. The aforementioned decisions will come up as solutions to optimization problems. To that end, the student will formulate optimization problems and solve them either analytically or using an optimization solver (e.g., Gurobi). The other part would be conducting realistic simulations and showing the improvements with our approach.
Prerequisites
- Basic understanding of 5G Core Networks and Mobile Edge Computing (MEC).
- Experience with mathematical formulation of optimization problems.
- Programming experience with Python and Gurobi.
Supervisor:
Distributed Deep Learning for Video Analytics
Distributed Deep Learning, Distributed Computing, Video Analytics, Edge Computing, Edge AI
Description
In recent years, deep learning-based algorithms have demonstrated superior accuracy in video analysis tasks, and scaling up such models; i.e., designing and training larger models with more parameters, can improve their accuracy even more.
On the other hand, due to strict latency requirements as well as privacy concerns, there is a tendency towards deploying video analysis tasks close to data sources; i.e., at the edge. However, compared to dedicated cloud infrastructures, edge devices (e.g., smartphones and IoT devices) as well as edge clouds are constrained in terms of compute, memory and storage resources, which consequently leads to a trade-off between response time and accuracy.
Considering video analysis tasks such as image classification and object detection as the application at the heart of this project, the goal is to evaluate different deep learning model distribution techniques for a scenario of interest.
Supervisor:
Edge AI in Adversarial Environment: A Simplistic Byzantine Scenario
Distributed Deep Learning, Distributed Computing, Byzantine Attack, Adversarial Inference
Description
This project considers an environment consisting of several low performance machines which are connected together across a network.
Edge AI has drawn the attention of both academia and industry as a way to bring intelligence to edge devices to enhance data privacy as well as latency.
Prior works investigated on improving accuracy-latency trade-off of Edge AI by distributing a model into multiple available and idle machines. Building on top of those works, this project adds one more dimension: a scenario where $f$ out of $n$ contributing nodes are adversary.
Therefore, for each data sample an adversary (1) may not provide an output (can also be considered as a faulty node.) or (2) may provide an arbitrary (i.e., randomly generated) output.
The goal is to evaluate robustness of different parallelism techniques in terms of achievable accuracy in presence of malicious contributors and/or faulty nodes.
Note that contrary to the mainstream existing literature, this project mainly focuses on the inference (i.e., serving) phase of deep learning algorithms, and although robustness of the training phase can be considered as well, it has a much lower priority.
Supervisor:
On the Efficiency of Deep Learning Parallelism Schemes
Distributed Deep Learning, Parallel Computing, Inference, AI Serving
Description
Deep Learning models are becoming increasingly larger so that most of the state-of-the-art model architectures are either too big to be deployed on a single machine or cause performance issues such as undesired delays.
This is not only true for the largest models being deployed in high performance cloud infrastructures but also for smaller and more efficient models that are designed to have fewer parameters (and hence, lower accuracy) to be deployed on edge devices.
That said, this project considers the second environment where there are multiple resource constrained machines connected through a network.
Continuing the research towards distributing deep learning models into multiple machines, the objective is to generate more efficient variants/submodels compared to existing deep learning parallelism algorithms.
Note that this project mainly focuses on the inference (i.e., serving) phase of deep learning algorithms, and although efficiency of the training phase can be considered as well, it has a much lower priority.
Supervisor:
Optimizing Communication Efficiency of Deep Learning Parallelism Techniques in the Inference Phase
Distributed Deep Learning, Parallel Computing, Inference, Communication Efficiency
Description
Deep Learning models are becoming increasingly larger so that most of the state-of-the-art model architectures are either too big to be deployed on a single machine or cause performance issues such as undesired delays.
This is not only true for the largest models being deployed in high performance cloud infrastructures but also for smaller and more efficient models that are designed to have fewer parameters (and hence, lower accuracy) to be deployed on edge devices.
That said, this project considers the second environment where there are multiple resource constrained machines connected through a network.
When distributing deep learning models across multiple compute nodes, trying to realize parallelism, certain algorithms (e.g., Model Parallelism) are not able to achieve the desired performance in terms of latency, mainly due to (1) communication cost of intermediate tensors; and (2) inter-operator blocking.
This project consists of multiple sub-projects each can be taken separately.
In the context of Model Parallelism, two potential modifications can be considered:
- Pipeline parallelism by delaying the inference of the first few data samples assuming a live stream of input data.
- Finding certain points in deep learning architectures or modifying the architecture itself so that for each data sample, it becomes possible to filter out some sub-parts of the model, and therefore reducing the transmitted data, and still achieve comparable accuracy.
Class and Variant Parallelism improve inter-node communication significantly. However, the input data needs to be shared between contributing nodes. The goal is to propose a technique to transmit less data, and to find a good trade-off between computation and communication.
Note that this project mainly focuses on the inference (i.e., serving) phase of deep learning algorithms, and although efficiency of the training phase can be considered as well, it has a much lower priority.
Supervisor:
Load Generation for Benchmarking Kubernetes Autoscaler
Horizontal Pod Autoscaler (HPA), Kubernetes (K8s), Benchmarking
Description
Kubernetes (K8s) has become the de facto standard for orchestrating containerized applications. K8s is an open-source framework which among many features, provides automated scaling and management of services.
Considering a microservice-based architecture, where each application is composed of multiple independent services (usually each service provides a single functionality), K8s' Horizontal Pod Autoscaler (HPA) can be leveraged to dynamically change the number of instances (also known as Pods) based on workload and incoming request pattern.
The main focus of this project is to benchmark the HPA behavior of a Kubernetes cluster running a microservice-based application having multiple services chained together. That means, there is a dependency between multiple services, and by sending a request to a certain service, other services might be called once or multiple times.
This project aims to generate incoming request load patterns that lead to an increase in either the operational cost of the Kubernetes cluster or response time of the requests. This potentially helps to identify corner cases of the algorithm and/or weak spots of the system; hence called adversarial benchmarking.
The applications can be selected from commonly used benchmarks such as DeathStarBench*. The objective is to investigate on the dependencies between services and how different sequences of incoming request patterns can affect each service as well as the whole system.
* https://github.com/delimitrou/DeathStarBench/blob/master/hotelReservation/README.md
Supervisor:
Demo implementation: Multi-domain redundant network routing
multi-domain, SDN
This works consists on the implementation of a multi-domain SDN network.
Description
Software-Defined Networking (SDN) is a network paradigm where control and data planes are decoupled. The control plane consists on a controller, which manages network functionality and can be deployed in one or multiple servers. The data plane consists on forwarding entities which are instructed by the controller on how to forward traffic.
A network can be divided in multiple domains in order to ease its management or limit ownership. In multi-domain SDN, each domain has a controller which is responsible for the management. Controllers in different domains cooperate which each other aiming at providing multi-domain end-to-end connectivity.
In this work, the student will receive an abstract topology representing the multi-domain network. This information has to be used to build a virtual network, that can be used in the testing of different algorithms. The implementation should include a GUI, in order to visualize the topology and interact with the different elements in the network.
Please send your CV and transcript of records.
Prerequisites
Basic knowledge on the following:
- Linux
- Networking/SDN
- Python
- Object-Oriented Programming
- Web programming (GUI)