Project Overview

Modern cloud services, such serverless computing, Function as a Service, Container as a Service, are characterized by their scalability, flexibility, and efficiency. However, they also exhibit significant performance variability due to their complex infrastructures and multi-tenant nature. This research aims to systematically analyze the causes and impacts of performance variability in cloud services, proposing solutions to enhance stability and predictability. This study will contribute to the optimization of cloud technologies, ultimately improving service delivery for enterprises and end-users.

Aims

The primary aim of this project is to thoroughly investigate and understand the factors contributing to performance variability in modern cloud services. By identifying these factors, the project seeks to develop predictive models and strategic interventions to enhance the stability and predictability of cloud service performance.

Objectives

This project will identify the primary sources of performance variability in modern cloud services and quantify the impact of various factors on service performance. A set of predictive models will be delivered that anticipate performance changes under different conditions. It will also propose architectural and operational improvements to mitigate performance variability.

Significance 

The significance of this research project on understanding performance variability in modern cloud services is multifaceted and critically relevant in today's technology-driven landscape. By aiming to dissect and mitigate the unpredictable performance of cloud platforms, this study addresses a core challenge that affects user experience, operational efficiency, and service reliability across numerous industries reliant on cloud technologies. Improved predictability and stability in cloud services can lead to substantial economic benefits by optimizing resource allocation and reducing the need for costly over-provisioning. Furthermore, by enhancing the reliability of cloud infrastructures, the project not only bolsters user trust but also encourages wider adoption of cloud solutions, particularly in sectors where dependable performance is paramount. Academically, the findings are expected to fill significant gaps in existing literature, providing a foundation for future research and advancing the understanding of cloud architectures. This work also has the potential for immediate practical impact, offering cloud service providers actionable insights and tools to improve service delivery, thus strengthening the overall digital ecosystem.

• Future Students

• Faculty of Science & Engineering
  • Science courses
  • Engineering courses

• Higher Degree by Research

• Australian Citizen
• Australian Permanent Resident
• New Zealand Citizen
• Permanent Humanitarian Visa
• International Student

• Merit Based

The annual scholarship package, covering both stipend and tuition fees, amounts to approximately $70,000 per year.

In 2024, the RTP stipend scholarship offers $35,000 per annum for a duration of up to three years. Exceptional progress and adherence to timelines may qualify students for a six-month completion scholarship.

Selection for these scholarships involves a competitive process, with shortlisted applicants notified of outcomes by November 2024.

1

All applicable HDR courses.

The ideal HDR applicant for this project should possess a strong foundational knowledge in computer science, particularly in cloud computing and data analytics. Proficiency in programming languages such as Python or Java and experience with database management and statistical analysis tools are essential. The candidate should demonstrate the ability to work with complex datasets and develop predictive models. Excellent problem-solving skills, a keen attention to detail, and the capacity for critical thinking are crucial. Effective communication skills, both written and verbal, and the ability to collaborate effectively in a team-oriented environment are also required. Prior research experience in a related field is highly desirable.

 

Please send your CV, academic transcripts and brief rationale why you want to join this research project via the HDR Expression of Interest form to the project lead researcher, listed below. 

You must be enrolled in a Higher Degree by Research Course at Curtin University by March 2025.

Project Lead: Dr Sheik Fattah