Project Supervision
I supervise Master and Bachelor projects in the Distributed Systems, Cloud and Edge Computing research areas. For research topics and students, see below.
Masters and Bachelor Thesis and Projects:
Check my profile for prospective thesis topics. I supervise Bachelors (BSc) and Masters (MSc) projects on Distributed Systems, Cloud and Edge Computing. Feel free to contact me if you would like to work on a challenging project under my supervision in the aforementioned research topics. Some of the recently completed (C) and ongoing (O) MSc thesis projects under me (Note: list only include students with me as their first supervisor):
Completed Master Projects:
- Topic: Mitigating the cold start issue in Serverless using Reinforcement learning (C), 2025; Student: Rosen Kosakov.
- Topic: Holistic Observability Framework and Software Library for Performance Diagnosis In Cloud (C), 2025; Student: Haoyue Chen.
- Topic: SLO-aware AI/ML Inference Serving in Cloud (C), 2024; Student: Rachel de Haan.
- Topic: A Systematic Framework for Deploying Large Language Models in the Cloud(C), 2024; Student: Domonkos Debreczeni.
- Topic: Efficient failure prediction in Cloud systems (C), 2024; Student: Ignacy Skrzeczek
- Topic: Consumer Group Autoscaling: Estimating Concumer Capacity with Dynamic Workloads in Topic-based Publish-Subscribe Middleware (C), 2024; Student: Mervin van der Horst.
- Topic: Benchmarking Hybrid Classical-Quantum Computing Middlewares (C), 2024; Student: Milo Voorhout.
- Topic: Proactive auto-scaling for performance variable cloud infrastructure (C), 2024; Student: Pieter Hollander.
- Topic: MetricFlow: Predicting Application KPIs using Continuous Stream-based Active Learning (C), 2023; Student: Tim Schonborn, MSc.
- Topic: CausalCloudScape: A novel approach to Causal Discovery of High Dimensional-Low level Cloud performance metrics (C), 2023; Student: Tobias Zenner, MSc.
- Topic: Fingerprinting Cloud performance using compute resource characterisation (C), 2023; Student: Max van den Heijkant, MSc.
- Topic: Benchamarking GAN-Based Synthetic Data Generation Techniques in Cloud (C), 2023; Student: Tessel VAN RoozenDAAL, MSc.
- Topic: Forecasting the Cloud: Using time-series neural networks to predict performance variability in Cloud (C), 2023; Student: Jacob Herbert, MSc.
- Topic: Prestige: A platform agnostic middleware for seamless and portable data migration in multi-cloud deployment (C), 2023; Student: Francois Tronche-Macaire
Ongoing Master Projects:
- Topic: SLO-aware IAC automation framework for dynamic Cloud deployment (O); Student: Koen Boetius.
- Topic: Container vs. VMs: Performance interference-aware decision-making at runtime (O); Student: Christoffer Gram
- Topic: Seamless migration of live virtual machines to containerized environment at runtime (O); Student: Sem Bode
- Topic: eBPF-based container downsizing in Cloud-native orchestration platforms (O); Student: Matthijs de Heus
- Topic: Evaluating Quantum-Classical Middlewares using Quantum Mini-Apps (O); Student: Roy Schenk
- Topic: Application-agnostic dependency mapping in networked distributed systems (O); Student: Gijs Blanken
Bachelor Projects:
- Topic: Reference Architecture for Reproducibility in E2C Experiment Using Human-in-the-Loop. Student: Luuk Lisdonk. (Completed, 2024)
Open Master Thesis Topics:
- Active Learning for Performance Diagnosis in Cloud and Edge Systems
- Serverless for AI and Data (2) Processing Workloads in Cloud-Edge Infrastructure
- ML Inference in Cloud and Edge
- Cold start issues of serverless models
- Workload scheduling and placement approaches for Edge-Cloud
- Computational offloading techniques for heterogeneous infrastructures
- Auto Scaling techniques for Edge and Cloud
- Performance Benchmarking, degradation detection and prediction in Cloud and Edge systems
- Causal analysis of performance metrics in Cloud and Edge systems
- Middleware of Integrated Cloud and Quantum systems
- LLMs for Cloud-Edge Orchestration, Performance Management and Diagnosis