Research projects in Information Technology

This project aims to develop resource management techniques including scheduling and scaling algorithms for container-based virtual clusters in cloud data centres powered with renewable energy sources. Algorithms are designed to optimise renewable energy use while meeting the Quality of Service requirements of applications running on the cluster. Specifically, the project aims to:

1. Define an architectural framework and principles for renewable energy-aware management of containers in cloud data centres;

Interest is growing in powering data centres by energy generated from renewable sources to reduce high operational cost and carbon footprint. In 2017, Google achieved a major milestone of purchasing 100% renewable energy to match its data centres annual electricity consumption. However, efficient utilisation of renewable energy is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply.

This project aims to build an automated bird repellent system to detect and identify pest birds and repel them from the farming area by frightening them. An Edge computing platform is embedded in a network of devices to bring application execution close to the data source and overcome challenges with high volumes of data and the need for automated, near real-time system responsiveness. The arrangement of electricity for devices in farming areas is also challenging. Thus, we intend to make our system fully off-grid by the deployment of solar-powered devices.

A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues.

This project aims to design effective and intelligent search techniques for large scale social network data. The project expects to advance existing social network search systems in three unique aspects: utilizing the geographical locations of queries and social network data to provide more relevant results; acknowledging and handling inherent uncertainties in the data; and exploiting knowledge graphs to produce intelligent search results. Expected outcomes of this project include a next-generation social network search system and enhanced international collaborations.