Current Projects

The aim of this Collaborative Research Project is to to develop a comprehensive solution for distributed self-management based on autonomous agents and distributed constraint satisfaction/optimisation (DisCSP/DisCOP) techniques. While the research is carried out within the context of autonomous cyber defence, it has broader applicability to IoT and Cloud-based enterprise scenarios.

This collaborative R&D Project supported by the industry partner Zimbani and ARC Linkage Program aims at developing novel solutions based on predictive analytics and AI for adaptive resource cost optimization, to enable enterprises cut their cloud costs while ensuring quality-assured provision of their applications.

Ensuring cost-effective end-to-end QoS in a multilayer, multi-service, IoT data processing pipeline is a non-trivial challenge. The uncertainties surrounding the 3Vs of streaming data – variety, velocity and volume – impose dynamic QoS-driven resource requirements on each component (or service) of the pipeline and make adaptive resource management a complex task. Our overall research objective is to develop appropriate resource scaling strategies that dynamically adjust the resources allocated to each component in the pipeline so as to ensure end-to-end QoS fulfillment while optimizing the associated costs.

Most organisations moving their legacy systems to the cloud base their decisions on the naïve assumption that public cloud always provides cost savings, without sufficiently assessing the underlying application architecture, and the technical and financial constraints that it imposes on the chosen cloud platform. This can lead to undesirable consequences including project delays, budget overruns, below-par performance, application instability and creation of technical debt. This project aims to develop a structured yet flexible decision framework comprising models, guidelines, tools and calculators to help enterprises choose between public, private and hybrid cloud for the deployment of their applications.