Carbon-Conscious Resource Scheduling for AI Workloads in the Cloud–Edge Continuum

About the Project

AI workloads are rapidly increasing in scale and complexity, powering applications such as intelligent transportation, smart cities, healthcare monitoring, and industrial automation. These workloads are computationally demanding, often distributed across the Cloud–Edge continuum, where heterogeneous resources must balance performance, latency, energy consumption, and environmental impact. A key challenge is how to schedule and manage such workloads in a way that is both efficient and carbon-conscious.

Traditional scheduling approaches focus primarily on performance, cost, and availability, often neglecting the carbon footprint of distributed infrastructures. However, the dynamic nature of energy sources (e.g., varying availability of renewables), the heterogeneity of hardware across cloud and edge layers, and diverse workload requirements create a complex optimisation problem. Addressing this requires novel scheduling mechanisms that can dynamically adapt to sustainability constraints while still meeting system-level performance objectives.

This project investigates carbon-aware scheduling strategies for AI workloads in the Cloud–Edge continuum. It aims to integrate awareness of energy sources, carbon intensity, and workload characteristics into scheduling decisions, enabling infrastructures to adapt intelligently to sustainability goals. Research will focus on balancing performance, energy efficiency, and carbon reduction, while ensuring scalability, resilience, and adaptability of the scheduling mechanisms.

The key objectives are: 1) To investigate and benchmark existing carbon-aware scheduling techniques in distributed systems and identify their limitations in the Cloud–Edge continuum; 2) To formalise the problem of carbon-conscious AI workload scheduling across heterogeneous, geographically distributed resources; 3) To design, prototype, and evaluate novel scheduling strategies that account for energy availability, carbon intensity, and workload characteristics, using simulation and real-world testbeds where feasible.

Academic qualifications

Have, or expect to achieve by the time of start of the studentship a first-class honours degree, or a distinction at master level, ideally in Computer Science or equivalent with a good fundamental knowledge of Computer programming, Software engineering, Cloud computing, Machine learning.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.
• Experience in fundamental software engineering
• Competent in one (or some) programming languages
• Knowledge of Cloud, IoT, Machine learning, and Microservices architecture
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:

• Practical experience in research or industry will be considered an advantage.

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use the advertised title as project title

For informal enquiries about this PhD project, please contact A.Ullah@napier.ac.uk