Bottom-Up Swarm Computing for Workload Placement in Edge-cloud Continuum
Eligibility: UK/International (including EU) graduates with the required entry requirements
Duration: Full-Time – between three and three and a half years fixed term
Application deadline: 15 January 2025
Interview date: Will be confirmed to shortlisted candidates
Start date: May 2025
For further details contact: Abdorasoul Ghasemi
Introduction
This PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The PhD Student is anticipated to spend at least six months of the total period of the programme at Deakin University, with the remainder of the programme based at Coventry University. The supervision team will be drawn from the two universities.
This project will start at Coventry UK.
Edge-cloud continuum computing is an essential enabler for numerous high-demand applications, such as smart grids, smart cities, intelligent transportation systems, and e-health. Current cloud web service-based solutions cannot adequately meet the frequent data queries and delay-sensitive responses due to the unpredictable latency of the inter-networking between the end application and cloud or data centres. Edge computing facilitates data processing close to the end application, deploying edge micro data centres (EMDCs) distributed resources complementing the services already provided by cloud computing. This technology also offers other advantages regarding scalability, resilience, and privacy. However, resource allocation and orchestration are more challenging as resources are distributed across various geographic EMDCs, necessitating a self-organised scheme. The heart of this emerging computing scheme is its scheduling algorithm, which assigns the incoming workloads to a proper node in the continuum, considering its required resources, e.g., CPU and RAM demand, its needed quality of service (QoS), e.g., response time, and the desired system objective, e.g., load balancing or reliability.
Project details
Recently, there has been an investment in developing swarm intelligence-based algorithms for intelligent bottom-up workload placement. Swarm Intelligence algorithms are nature-inspired algorithms that end up with desired collective behaviour by relying on distributed and simple local rules in decision-making for each involved agent in the systems. This project aims to develop a swarm computing model considering the continuum as a multiagent system consisting of resource and demand agents. he procedure of workload placement is performed by local decisions that involved agents make without relying on a centralised point.
The selected candidate will be expected to:
- Conduct a literature review on swarm intelligence and swarm computing and mathematics behind collective intelligence
- Develop a simulator to model the edge-cloud continuum as a multiagent system
- Analyse the workload placement as a swarm computing problem and develop mathematical modelling
- Contribute to the future of distributed computing by providing insights into the design of swarm computing algorithms
Funding
Tuition fees and stipend
Benefits
The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.
This is an exciting opportunity to study a PhD as part of a cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The PhD Student will graduate with two PhDs, one from Deakin University and one from Coventry University, each of which recognises that the programme was carried out as part of a jointly supervised doctoral programme.
Candidate specification
Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.
- Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
- A Bachelor's degree in a relevant field requiring at least four years of full-time study, and which normally includes a research component which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80%.
OR
- a Masters degree, with a significant research component, in a relevant subject area, with overall mark at minimum Distinction.
- In addition, the mark for the Masters thesis (or equivalent) must be a minimum of 80%.
- Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports.
- Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).
The potential to engage in innovative research and to complete the PhD within a prescribed period of study.
Additional Requirements
Please consider the requirements below when preparing your Expression of Interest (EoI). Please spend half the EoI outlining why your experience makes you a good fit for the project and the other half identifying and discussing some relevant literature.
The selected candidate should/be:
- Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics.
- Knowledgeable in optimisation or machine learning techniques (had successful courses or projects)
- Proficient in programming (preferably in Python).
- Ideally familiar with mathematical modelling/cloud computing
How to apply
To find out more about the about the technical details of the project, please contact: Abdorasoul Ghasemi.
In the first instance, please submit your expression of interest (EOI) via the button below with a supporting statement detailing your suitability with evidence of the following. Please spend half the EOL outlining why your experience makes you a good fit for the project and the other half identifying and discussing some relevant literature.
- Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics.
- Knowledgeable in optimisation or machine learning techniques (had successful courses or projects)
- Proficient in programming (preferably in Python).
- Ideally familiar with mathematical modelling/cloud computing
