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 assosciated 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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Join Our Team

We are always looking for motivated students and researchers to join our lab.

1 Position Available

PhD Position: Self-Organizing Edge Infrastructure for Next-Generation Internet

Deadline: December 1, 2025

TU Delft's SPEAR (Systems and Protocols for Edge-Enabled Internet) lab seeks a doctoral researcher to develop autonomous systems that orchestrate resources across edge, cloud, and satellite networks. You'll focus on building systems that self-organize, self-optimize, and self-heal across heterogeneous environments, working with network transport protocols and conducting Internet-scale performance validation. Contributions to open-source projects like Kubernetes and Oakestra are expected.

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Are You a Student at TU Delft?

Check out our open thesis topics for Bachelor's and Master's students.

13 Topics Available
Master's

Building 5G/6G-compliant edge

Design and implement an orchestration framework for deploying 5G/6G core network functions in edge computing environments with real lab testbed validation.

EdgeSystemsNetworkingPerformance Measurements+1 more
Master's

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.

EdgeSystems
Master's

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.

EdgeSystemsNetworkingOrchestration+1 more