Glasgow Carbon-Conscious Computing (GC3) lab

The GC3 lab is a computer systems laboratory in the Glasgow Systems Section and the School of Computing Science at the University of Glasgow. Its research falls into the School's Low-Carbon and Sustainable Computing theme.

Interested in joining as a PhD student? We are looking for motivated students to work with us on sustainable computer systems.

GC_3 Lab Logo
starting point:

An increasing number of data-driven and AI-enabled applications process large amounts of data and make use of machine learning. These range from scientific data analysis in research organizations and large-scale data analytics in industry to applications built around the distributed sensors and devices of the Internet of Things.
We believe there is immense potential in using data-driven methods and machine learning across domains and disciplines. However, without careful consideration and new management techniques, this development will further increase computing's substantial and growing environmental footprint.

research agenda:

The central aim of our research is to reduce the carbon footprint of resource-intensive distributed systems running on today's diverse computing infrastructures. To this end, we work on methods and tools that enable a more resource-efficient and sustainable use of modern distributed computing infrastructures (e.g. edge and cloud resources) for scalable data-intensive applications (e.g. machine learning, big data analytics, stream processing), while also taking performance and dependability requirements into account.

Our guiding principles are:

  1. compute when and where green energy is available (which can, for instance, translate to carbon-aware scheduling, scaling, and resource allocation) as the carbon intensity of grids and renewable energy availability often varies
  2. allocate resources for high resource utilization and highly utilize allocated resources (which can, for example, translate to "right-sizing", server consolidation, co-location of jobs with complementary resource demands, and bottleneck mitigation) as any energy is wasted with mostly idling resources
  3. save computation and communication through distributed and dynamic architectures (which can, for instance, translate to edge computing, adaptively offloading tasks and scaling out to more nodes only when necessary, effective caching, or distributed learning) as it is sometimes possible to do the same with less in complex software systems

Much of the lab's work builds on our broader research on adaptive resource management for data-intensive systems, developed over several years and through close collaboration with TU Berlin, HU Berlin, and other partners. On this basis, we currently focus on investigating and making use of:

  • profiling and predicting application and infrastructure performance as well as power consumption
  • forecasting computational loads, carbon emissions, and the availability of renewable energy
  • carbon-optimized resource allocation, dynamic scheduling and scaling, and automatic system tuning

Another focus is on techniques and tools that support monitoring, testing, and benchmarking of the performance and footprint of data-intensive distributed systems.

current lab members:

Group photo (April 2026):

GC_3 lab group photo taken in April 2026 at the University of Glasgow

Left to right: James, Mohammed, Matthew, Kathleen, Lauritz, Max, Tobey, Vasilis, Magnus, and Youssef.

Previous photos: Isle of Bute away day (August 2025)

past lab members:
  • L4/L5 project students: Andres La Riva Perez, Danial Tariq, Isabella Gard, James Sharma, John Wilson, Karl Hartmann, Richard Arthurs, Rishabh Mathur
  • summer interns: Domonkos Revesz, Frederik Glitzner, Hubert Dymarkowski, Niovi Lampiri
  • visitors: Giulio Attenni (April-September 2025; PhD student from Sapienza Rome), Philipp Wiesner (April-August 2022; PhD student from TU Berlin)
selected results:

Several of these works are featured in my talk in our Low-Carbon and Sustainable Computing seminar series and the recording of the talk is available online.

academic collaborations:
industry collaborations:
research infrastructure:

For our research, we have access to CPU and GPU clusters as well as a variety of individual devices (e.g. different server architectures, IoT systems, energy meters) in Glasgow. In addition, we are regularly able to access other compute infrastructure (e.g. public/private cloud services) through grants and collaborations.