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Microservice chains: Placement / Scaling with moveable infrastructure
Thesis abroad
keywords ALGORITHMS, EXPERIMENTAL DESIGN, TESTBED
Reference persons CLAUDIO ETTORE CASETTI, CARLA FABIANA CHIASSERINI, PAOLO GIACCONE
External reference persons Prof. Dr. Holger Karl, Hasso Plattner Institute, Potzdam University (Berlin), Germany
Research Groups Telecommunication Networks Group
Thesis type EXPERIMENTAL AND MODELING
Description When deploying and running microservices (or closely related network function chains) to and in edge or core clouds, typical assumptions about these kinds of infrastructure prevail: it is dependable, does not fail, does not move. On that basis, many so-called orchestration algorithms have been designed; these algorithms decide, e.g., how many instances of a service to run, where each instance runs, and which instance serves which data flow.
This mindset, however, changes with new types of infrastructure: vehicles can be seen as a moving cloud, but only vehicles in the vicinity of a particular intersection can be of interest. Fleets of drones similarly can act as (very simple, very specialized) service providers; but they need to handover service execution once they run out of battery power and have to be replaced by another drone, for a few minutes. For such volatile, evolving infrastructures, there is very little in the literature about suitable orchestration concepts.
The goal of this thesis is hence to identify a suitable model for volatile infrastructure, to cast some typical orchestrations problems into that model and to design and to evaluate their performance. As this is a fairly open area, the topic is also fairly open and evolving the concept is clearly part of the thesis assignment.
See also https://hpi.de/karl/teaching/bachelor-and-master-theses.html
Required skills Familiar with cloud computing concepts and microservices / network function virtualization
Ideally also familiar with vehicle-to-anything
Good modeling skills
Some experience in one of: optimization problems, heuristic design, machine learning is useful
Deadline 02/08/2023
PROPONI LA TUA CANDIDATURA