PhD project in Digital Twins for Empirical Elasticity between the Edge and Cloud

Location: United Kingdom
Application Deadline: 28 February 2025
Published: 1 week ago

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Overview

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 Edge that includes devices at the network edge has been recently proposed. While there are approaches in the literature that combine Cloud and Edge machines, these approaches do not address the challenge of how the dynamic switching between the Edge and the Cloud can be achieved at runtime without suspending the execution of the deployed Web services/APIs on the machines.

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 at the Edge or the Cloud. The Cloud providers currently provide platforms for the reactive elastic orchestration of service containers on the Edge or the Cloud exclusively (e.g., KubeEdge, Kubernetes). In other words, the Cloud/Edge 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 Edge 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 Edge 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.

The project addresses the challenge of extending the existing Edge/Cloud elasticity and orchestration mechanisms to provide proactive empirical elasticity between the Edge and the Cloud. We will propose an approach that uses Digital Twins (DTs). DTs are representations of the entities and the operations/functionality of a system that facilitate the development, analysis, monitoring, and management of the system.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:

The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

Entrance requirements

Graduate
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.

International Students

For information on international qualification equivalents, please check the specific information for your country.

English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).

International students wishing to apply to Queen’s University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

If you need to improve your English language skills before you enter this degree programme, INTO Queen’s University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.

How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

Find a supervisor

If you’re interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.

To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.

You might be asked to provide a short outline of your proposal to help us identify potential supervisors.

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