Applications are invited for a fully funded PhD studentship at the School of Computing Science at the University of Glasgow. This covers tuition fees at the home level and a stipend at research council rates.
This PhD project will research how novel distributed computing paradigms (such as edge computing) and distributed energy generation (from sources such as solar) can be combined to enable low-carbon and sustainable computing.
As data analytics and machine learning applications are increasingly deployed throughout our cities, factories, and homes, the computing infrastructure for these applications is becoming more distributed and diverse. That is, the intelligent and cyber-physical systems of the Internet of Things will not be implemented with centralized cloud resources alone. Such resources are simply too far away from sensors and devices, leading to high latencies, bandwidth bottlenecks, and unnecessary energy consumption. Additionally, there are often privacy and safety requirements mandating distributed architectures. Therefore, new distributed computing paradigms – such Edge Computing – aim to provide computing and storage closer to data sources and users.
Meanwhile, the IT industry is starting to recognize its increasing environmental footprint as more and more organizations are setting emission targets on the way to net zero. One approach towards more sustainable and cost-effective computing is equipping edge and cloud infrastructure with on-site renewable energy sources such as solar or wind. In addition, the emissions associated with consuming energy from public grids also fluctuate, depending on when and where the energy is consumed. This allows for reducing the emissions of computing by scheduling applications based on the expected carbon intensity of the available energy sources. This emerging area is known as Carbon-Aware Computing.
Effectively leveraging differences in the carbon intensity of energy systems for computing applications is far from trivial, though, and hence, the topic of this PhD project. It requires integrating state-of-the-art energy forecasts as well as estimates of the performance and power consumption of applications and compute resources. Furthermore, it should take possible errors in energy forecasts (e.g. due to unforeseen spikes in demand) and performance estimates (e.g. due to hardware/software failures or interference) into consideration. Thus, carefully planning the placement and scheduling of applications is not enough. Instead, applications running across edge and cloud resources will need to be monitored and adjusted continuously. For example, if an edge site is supplied with energy generated by solar panels, cloudy weather can easily make it necessary to offload applications to large centralised data centres, as these are often much more efficient in using energy for computation, so less grid energy could be used. However, the benefits of offloading need to outweigh the costs of moving a job and its data.
We expect this PhD project to investigate (1) performance and power models, (2) grid carbon intensity and renewable energy forecast, and (3) dynamic resource allocation and task offloading for carbon-aware edge computing.
This PhD project will be supervised by Dr Lauritz Thamsen (https://lauritzthamsen.org/) and Dr Yehia Elkhatib (https://yelkhatib.github.io/), who are academics of the Glasgow System Section (GLASS) within the School of Computing Science at the University of Glasgow. The PhD research will fall into the School’s newest research theme, Low-Carbon and Sustainable Computing, and we plan to leverage a close collaboration with TU Berlin in Germany on the topic of Adaptive Resource Management.
Candidates will be expected to hold at least a 2:1 BSc degree in Computer Science or a closely related discipline.
Suitable candidates will have a strong interest in computer systems, systems programming, and cloud computing as well as a working knowledge of data analysis, machine learning, and optimization methods.
How to Apply: Please refer to the following website for details on how to apply: