Edge-Accelerated Deep Learning. Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in eff ....Edge-Accelerated Deep Learning. Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in effective deployment and efficient execution of DL applications in a distributed edge-computing environment. Several innovative edge-computing methods will be developed for DL training, inference and implementation to achieve high performance with low latency and enhanced privacy.Read moreRead less
A Unified Framework for Resource Management in Edge-Cloud Data Centres. Edge Computing (EC) is an emerging paradigm with a great promise for advancing Information and Communications Technologies. This project aims to investigate and provide solutions for the realization of a seemingly integrated Edge Data Centres (EDCs) with cloud environments. Using theoretical and system development approaches, the project expects to generate new knowledge for managing the resources of an EDC ecosystem. Outcom ....A Unified Framework for Resource Management in Edge-Cloud Data Centres. Edge Computing (EC) is an emerging paradigm with a great promise for advancing Information and Communications Technologies. This project aims to investigate and provide solutions for the realization of a seemingly integrated Edge Data Centres (EDCs) with cloud environments. Using theoretical and system development approaches, the project expects to generate new knowledge for managing the resources of an EDC ecosystem. Outcome of this project includes practical solutions through building novel mathematical frameworks and resource management objectives accompanied by system implementations. These outcomes will benefit both scientific and industrial communities, and mark Australian scientists as pioneers in this emerging area of research.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100263
Funder
Australian Research Council
Funding Amount
$425,775.00
Summary
Adaptive Resource Management for Sustainable Edge Computing Systems. This project aims to develop adaptive resource management solutions in edge computing systems for efficient management of the use of limited computing resources and varying renewable energy resources without compromising the stringent needs of emerging Internet of Things applications. These resources will be jointly managed on the diverse, dispersed, often independently owned and operated edge devices with a set of prediction, ....Adaptive Resource Management for Sustainable Edge Computing Systems. This project aims to develop adaptive resource management solutions in edge computing systems for efficient management of the use of limited computing resources and varying renewable energy resources without compromising the stringent needs of emerging Internet of Things applications. These resources will be jointly managed on the diverse, dispersed, often independently owned and operated edge devices with a set of prediction, scheduling and energy saving techniques. The expected outcome is to realise a sustainable edge computing system to reduce both operational cost and negative environmental impact of the system. This project will elevate Australia to be a dominant player in sustainable computing and lead future development trends.
Read moreRead less