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  Dr Dionysios Athanasopoulos  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Over the last few years, Infrastructure as a Service (IaaS) has become widely available with cloud providers such as Amazon, Microsoft and Google. These IaaS providers allow us to deploy services on a pay-as-you-go basis. However, the use of the machines of IaaS providers can be expensive for a long period of time and for a big number of end-users. Moreover, the latency of the communication between the end-users and the cloud machines can be high when the cloud machines are not located close to the end-users’ devices and when the cloud machines are frequently used. To reduce the usage cost and the communication of cloud machines, the Fog infrastructure that includes devices at the network edge has been recently proposed. While there are approaches in the literature that combine cloud and fog machines, these approaches do not address the challenge of how the dynamic switching between the Fog and the Cloud can be achieved at runtime without suspending the execution of the deployed Web services/APIs on the machines.

Project Description:

To address the above challenge, predictions of the cost, the performance, and the latency of the services deployed on the Cloud should be made in order to proactively decide whether the services should be executed on the Fog or the Cloud. The cloud providers currently provide platforms for the reactive elastic orchestration of service containers on the Fog or the Cloud exclusively (e.g., KubeEdge, Kubernetes). In other words, the cloud/fog platforms provide the mechanisms that react to spikes on the cost, the performance, the latency of services.

However, these platforms do not implement proactive elasticity between the Fog and the Cloud. We use the term “proactive elasticity” to refer to deployments that should be made before the cost, the performance, and/or the latency of services get worse. The proactive elastic decisions about where the services should be deployed, should lead to the proactive switching of the deployed services at the runtime of apps from the Cloud to the Fog and vice versa.

The proactive decisions can be made if accurate predictions of the cost, the performance, and the latency of services can be performed. These predictions should be made based on historical data of the cost, the performance, and the latency of services. In other words, the empirical cost, empirical performance, and empirical latency of services should be learnt.

Overall, the project addresses the challenge of extending the existing Fog/Cloud elasticity and orchestration mechanisms to provide proactive empirical elasticity between the Fog and the cloud.

Project Key Words:

Fog and cloud computing, elasticity, service deployment, Kubernetes and KubeEdge.

Start Date: 01/10/22

Application Closing date: 28/02/22

For further information about eligibility criteria please refer to the DfE

Postgraduate Studentship Terms and Conditions 2021-22 at

Applicants should apply electronically through the Queen’s online application portal at:

Academic Requirements

A minimum 2.1 honours degree or equivalent in Computer Science, Software Engineering or relevant degree is required. 

Funding Notes:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.

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