Home > Research > Researchers > Peter Garraghan

Current Postgraduate Research Students

Peter Garraghan supervises 7 postgraduate research students. If these students have produced research profiles, these are listed below:

Student research profiles

Show all »

View graph of relations

Dr Peter Garraghan

Lecturer in Distributed Systems

Peter Garraghan

InfoLab21

LA1 4WA

Lancaster

Tel: +44 1524 510306

Research overview

Distributed systems, Cloud computing, energy & sustainability, Machine Learning systems, systems security, resource scheduling, dependability.

(October 2020): Commencing June 2021 onwards, I will have multiple open positions in the following areas:

  • PhD students: Sustainable ML systems, energy-adaptive network infrastructure, secure ML infrastructure.
  • Postdoctoral researchers: Massive-scale distributed systems, ICT sustainability & energy-efficiency, cluster schedulers, ML-driven systems.

For serious enquires, kindly contact me via email to explore further.

PhD supervision

I am happy to explore and supervise topics within distributed systems, cloud computing, energy, resource scheduling, security, and dependability research. If you have your own ideas for a research project you would like to pursue, feel free to contact me to discuss further.

Web Links

Profile

Peter Garraghan is a Lecturer (Assistant Professor) in Computer Science and has published over 50 peer-reviewed articles within the field of distributed systems. His research expertise is empirically studying and improving the performance, resilience, and sustainability of massive-scale distributed systems (Cloud computing, Machine Learning systems, Datacentres) in the face of societal and environmental change.

Peter has industrial experience building large-scale production distributed systems, and has worked and collaborated internationally with the likes of Alibaba Group, Microsoft, BT, STFC, CONACYT, and the UK datacenter and IoT industry.

Research Interests

  • Distributed Systems, Cloud computing, Machine Learning systems
  • Energy-efficient & sustainable computing at scale
  • CPU & GPU cluster resource scheduling
  • Fault tolerance & recovery
  • Systems security

Qualifications

Ph.D. in Computer Science (University of Leeds, UK)

View all (44) »