Networks
Research themes
Load Balancing and Robust Dimensioning in Heterogeneous Networks
| Summary | Large-scale service systems, such as cloud networks, data centers and skill-based business operations, are required to handle highly heterogeneous and complex tasks, and thus increasingly involve servers that are strongly tailored to perform specific types of jobs. Such affinity relations and compatibility constraints are not captured in classical paradigms for load balancing and dimensioning, and raise fundamental methodological challenges. In addition, uncertainty and variation in the total service demand as well the distribution of load across various job types create a need for adaptive learning algorithms to maintain high levels of efficiency and performance. The goal of this PhD project is to explore the impact of heterogeneity and affinity relations on the achievable performance of load balancing algorithms, design adaptive learning algorithms to deal with uncertain and time-varying load patterns, and develop robust dimensioning approaches to cover a wide range of load distributions in the presence of compatibility constraints. |
| Supervisors | Sem Borst (TU/e) |
| PhD Student |
Diego Goldsztajn |
| Location |
Eindhoven University of Technology (TU/e) |