Overview
Machine learning (ML) and optimisation techniques are increasingly used to help solve decision-making problems that would be difficult or time-consuming to address manually. One such problem is the configuration of cloud infrastructure, where many deployment parameters can affect several competing objectives at the same time. This project investigates the use of multi-objective optimisation to automatically explore different cloud infrastructure configurations defined through Infrastructure-as-Code templates. Our aim will be to build a fully automated system that identifies a range of Pareto-optimal configurations that represent different trade-offs between the objectives being considered. Such a system can help reduce the time and cost required to create efficient cloud deployments while providing a better understanding of the available configuration choices.
FAQs
- What will I learn in this Project?
You will learn about multi-objective optimisation. You will learn how to define and create cloud resources using Infrastructure-as-Code approach. You will gain experience working with Amazon Web Services (AWS), and potentially other cloud providers. You will learn about Bayesian optimization and how to apply it to practical tasks.
- What is the objective of the project?
The goal of the project is to implement a procedure of building a 2D Pareto front between two chosen metrics for a chosen compute workload deployed to the cloud. We suggest using BoTorch for multi-objective optimization but other tools can also be considered. Once the implementation is in place, experiments on different service parameters and output metrics will be carried out. The final step of the project is to analyse the discovered trade-offs.
- How does this fit into the bigger picture?
This project is part of a wider research programme Interfaces. One of the goals of this programme is to build methods for interpretable automated decision loops in software systems. You will have a chance to interact with the wider team and a successful project will form part of the portfolio of Interfaces demonstrations. We will also encourage publication of the project’s result. This idea continues the line of work done by our research team of applying multi-objective Bayesian optimisation to study trade-offs involved in building ML models: 2D utility/privacy, 3D utility/privacy/fairness, 2D utility/energy.
