Lin Wang, Ph.D.

Assistant Professor
Department of Computer Science
Vrije Universiteit Amsterdam (VU Amsterdam)

Phone: +31 20 59 87707
Address: De Boelelaan 1105, 1081HV Amsterdam, The Netherlands

Google Scholar Profile


My research is focused on networked systems, where I design and optimize computer systems for supporting emerging (mobile) appliations such as augmented reality. Topics include edge computing, in-network processing, novel network protocols, and cloud computing in general.

I am also affiliated as an Athene Young Investigator at the Department of Computer Science at TU Darmstadt. During Jul 2016 - Nov 2018, I headed the Smart Urban Networks group in the Telecooperation (TK) Lab at TU Darmstadt. Before that, I was a Postdoctoral Research Associate at SnT Luxembourg. I obtained my Ph.D. in Computer Science with distinction from the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS). During Sep 2012 - Jan 2014, I visited at IMDEA Networks Institute in Madrid.


SmartEdge: Concepts and Methods for Edge Computing

DFG Joint Sino-German Research Grant, 2018 - 2020, €350K

The project outcome will help developers abstract from technical issues of edge computing by means of the unifying model, and it will enable the sophisticated automatic handling of these issues in a runtime-adaptive manner with its novel algorithms, protocols and mechanisms. We deem it seminal for fostering large-scale ‘smart’ IoT applications.

Microservice Autoscaling and Caching for Edge Computing

DFG Collaborative Research Center MAKI C7, 2018 - 2020, €210K

As part of CRC MAKI, this subproject will take an alternative approach by exploring network adaptivity from a service-centric view. In particular, we investigate the fundamental challenges in advancing edge computing based on the software engineering paradigm called microservice architecture, in which a single application is developed as a suit of small services rather than a monolithic whole. Unlike the related research in MAKI, microservice-based edge computing is not restricted to any particular application scenarios. Therefore, the results generated by this subproject will help close the gap of enabling network adaptivity generally at the software stack level. While bringing more flexibility compared to the monolithic architecture, the modularity of microservice, on the other hand, introduces more dynamics, both spatially and temporally. Our overarching goal is to tackle these challenges and provide a formal basis for such a new edge computing architecture solution with necessary theoretical guarantees.