Resource Allocation for High-Volume Streaming Data in Data Centers. Almost all chip vendors are producing new hardware accelerators by combining several units into a single main-board, and therefore making the execution of parallel and distributed run-time primitives not efficient/scalable. This project aims to develop innovative ways to building incremental and iterative computations over massive data sets in a cluster of heterogeneous systems. This will provide a significant reduction of perfo ....Resource Allocation for High-Volume Streaming Data in Data Centers. Almost all chip vendors are producing new hardware accelerators by combining several units into a single main-board, and therefore making the execution of parallel and distributed run-time primitives not efficient/scalable. This project aims to develop innovative ways to building incremental and iterative computations over massive data sets in a cluster of heterogeneous systems. This will provide a significant reduction of performance bottlenecks when running heavily distributed data-driven applications. Expected outcomes will include resource management algorithms that optimise performance at large scale. The project will benefit many areas, including running stateful iterative stream-based data-analysis applications in data centres. Read moreRead less
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
Adaptive context caching for fast concurrent access in Internet of Things. Context-awareness in Internet of Things (IoT) applications has profound impact on smartness, relevance, adaptability, dependability, performance and flexibility of such applications. This project will address the significant knowledge gap by investigating, proposing and validating a novel adaptive context caching scheme for fast near real-time access in multiple concurrent context queries coming from multiple and diverse ....Adaptive context caching for fast concurrent access in Internet of Things. Context-awareness in Internet of Things (IoT) applications has profound impact on smartness, relevance, adaptability, dependability, performance and flexibility of such applications. This project will address the significant knowledge gap by investigating, proposing and validating a novel adaptive context caching scheme for fast near real-time access in multiple concurrent context queries coming from multiple and diverse IoT applications. The outcome will be a critical component of the IoT context management platform called Context-as-a-Service which is currently under development. The expected benefits will be far ranging and applicable to many domains including intelligent transportation, industrial internet and smart cities..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.
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