Application fee €100 one-time
More information 


PhD candidate position in
Self-Adaptive QoS-aware container autoscaling using machine learning techniques

Open application for a doctoral candidate in Informatics Engineering
The doctoral candidate position is available in Vilnius Gediminas Technical University Faculty of Fundamental Sciences Department of Information Systems. Applicants interested in other research topics related to Informatics Engineering are also welcome to apply.

Research topic description
A wide adaption of containerization technology led to more agile application development, optimization of resource usage, and faster resource provisioning. As applications grow and become more complex, all these instances need to be dynamically scaled up on time in the corresponding amount. Autoscaling service is used for this purpose employing different horizontal and vertical scaling algorithms.
A wide variety of container autoscaling solutions mainly address resource versus cost optimization problems. Also, the Quality of Service (QoS) versus optimization of resource utilization is investigated to minimize the risk of Service Level Agreement (SLA) violations.
Problems arise when a particular Service level objective SLO value based on application performance or user experience must be achieved. The problem can become even worse when the Kubernetes cluster runs on a shared infrastructure that is a public cloud, where the “noisy neighbour” problem arises. Also, autoscaling algorithms do not consider the dynamics of the load, and as a result, resource provisioning is delayed. A slow reaction or latency in the provisioning of required resources causes a QoS drop. To overcome the latency problem an approach of using Machine Learning techniques to predict SLA violations as well as prevent them by means of optimization must be performed.
The research aims to propose and investigate a self-adaptive autoscaling solution and framework for SLA-sensitive applications that will help with service level objectives (SLO) recovery in cases of service level degradation related to insufficient or delayed resource provisioning. Also, a platform that implements the proposed autoscaling algorithms must be developed and experimental research must be performed to evaluate and compare the obtained results with the container autoscaling algorithms proposed by other authors.

The selected candidate will work on the PhD thesis under the supervision of Prof. dr. Dalius Mažeika The successful applicant will have to attend scientific conferences, meetings and internships at other universities.

• Required background: Master’s degree in Computer Science
• Expected skills and knowledge:
• Programming experience in one the technology: .NET, Java, Python, JavaScript
• Experience in cloud computing and Kubernetes
• Knowledge of Machine learning and Deep learning

It is a prerequisite you can be present at and accessible to the institution daily.

For more information
Shortlisted candidates will be invited for an interview. The position may not be opened if no qualified candidate is found. Additional information regarding the post may be obtained from Prof. dr. Dalius Mažeika, e-mail:

Programme structure

The PhD programme consists of:
· Independent research under supervision;
· Courses for PhD students (approximately 30 ECTS credits);
· Participation in research networks, including placements at other, primarily foreign, research institutions;
· Teaching or another form of knowledge dissemination, which is related to the PhD topic when possible;
· The completion of a PhD thesis.

Apply now! Spring semester 2023/24
Application period has ended
Studies commence
1 Dec 2023

Application deadlines apply to citizens of: United States

Apply now! Spring semester 2023/24
Application period has ended
Studies commence
1 Dec 2023

Application deadlines apply to citizens of: United States