Cloud Computing
and Distributed Systems
Uncertainty-Aware Workflow Scheduling, Statistical Risk Control, and Cloud Resource Optimisation
Joseph Adeleke • PhD student in Computer Science
Towards more predictable cloud workflows.
My research interests lie at the intersection of Cloud Computing, Distributed Systems, Statistical Learning, and Optimisation. I focus on developing reliable, cost-efficient methods for scheduling complex computational workflows in cloud environments.
I am particularly interested in deadline-constrained workflow scheduling under uncertain execution conditions, uncertainty quantification, distribution-free statistical risk control, and resource allocation strategies that balance cost, performance, and reliability.
My research direction combines mathematical modelling, statistical methods, and experimental systems evaluation to improve the predictability and dependability of cloud computing infrastructure.
Three connected perspectives.
My interests connect workflow scheduling, statistical reasoning, and dependable cloud infrastructure.
Systems & scheduling
- Cloud Computing and Distributed Systems
- Scientific Workflow Scheduling and DAG-Based Task Scheduling
- Deadline-Constrained and Cost-Efficient Scheduling
- Cloud Resource Allocation and Optimisation
Uncertainty & statistical risk
- Uncertainty Quantification and Performance Modelling
- Statistical Risk Control and Conformal Prediction
- Scheduling under Runtime Uncertainty and Model Misspecification
Reliability & resource intelligence
- Reliable and Trustworthy Cloud Computing
- Admission Control, Queueing Systems, and Service Reliability
- Applied Machine Learning for Cloud Resource Management
Interested in dependable cloud systems?
I welcome conversations about these research interests and potential research collaborations.
Discuss a research ideaCloud Computing | Distributed Systems | Workflow Scheduling | Uncertainty Quantification | Statistical Risk Control | Resource Optimisation