Welcome to SPEAR Lab
The SPEAR Lab (Systems and Protocols for Edge-Enabled Internet) at TU Delft conducts cutting-edge research in edge computing, next-generation network protocols, and Internet-wide measurements. We build practical systems and conduct measurements that shape the future of Internet infrastructure.
SPEAR Lab is associated with the Networked Systems Group at Software Technology department of the Faculty of Electrical Engineering Mathematics, and Computer Science (EEMCS) at TU Delft.
Highlights
Research Areas
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Edge Computing
Physical deployment, specifications and effective utilization of compute resources at the network edge
Next-Generation Network Protocols
Protocol design and optimization for emerging network environments including LEO satellites, multipath transport, and edge computing
Satellite Networks
LEO satellite constellation measurement, characterization, and protocol optimization for global connectivity
EdgeAI & ML Model Management
Systems challenges in deploying and managing machine learning workloads across heterogeneous edge infrastructure
Next-Generation Cellular Networks
Integration of edge computing with cellular infrastructure and architectural evolution for beyond-5G networks
Join Our Team
We are always looking for motivated students and researchers to join our lab.
No open PostDoc/PhD positions at the moment, but we regularly have opportunities for students through thesis projects and research internships.
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Are You a Student at TU Delft?
Check out our open thesis topics for Bachelor's and Master's students.
Circumventing Internet Connectivity through Starlink
Measure and characterize where Starlink connectivity appears outside official availability regions.
Designing and supporting "fluid computing"
Design and implement a system for automatic code partitioning that transforms monolithic applications into distributed pico-services optimized for fluid computing environments.
Efficient AI pipeline management in distributed edge infrastructures
Develop an intelligent AI pipeline orchestration system integrated with Oakestra's hierarchical architecture to optimize deployment and execution of machine learning workloads across distributed edge infrastructures.