Cloud-data centres resource allocation under bursty conditions. Cloud-data centres resource allocation under bursty conditions. The project aims to design, implement, and integrate solutions to manage resources in cloud data centres (CDCs), especially when operating under bursty workload conditions. CDCs are expected to assure performance whilst optimising resource usage at a minimum cost, but efficiently providing resources with specific performance requirements can be difficult. This project i ....Cloud-data centres resource allocation under bursty conditions. Cloud-data centres resource allocation under bursty conditions. The project aims to design, implement, and integrate solutions to manage resources in cloud data centres (CDCs), especially when operating under bursty workload conditions. CDCs are expected to assure performance whilst optimising resource usage at a minimum cost, but efficiently providing resources with specific performance requirements can be difficult. This project intends to develop scalable solutions with industry approved software plug-ins. This is expected to affect both trustworthy information and communications technology (ICT) infrastructure (delivering more resilient CDCs) and economic sustainability (reducing CDC usage cost for both users and providers) of today’s computerised society.Read moreRead less
Contention-Aware Scheduling in Cloud Data Centres. This project aims to design, implement, and integrate solutions to improve resource use of private cloud data centres (CDCs). CDCs are expected to guarantee performance while optimising resource usage at a minimum cost. This incurs technical challenges that must be tackled to efficiently provision on-demand resources with specific performance requirements. The project intends to push the applicability of both current techniques and the ones to b ....Contention-Aware Scheduling in Cloud Data Centres. This project aims to design, implement, and integrate solutions to improve resource use of private cloud data centres (CDCs). CDCs are expected to guarantee performance while optimising resource usage at a minimum cost. This incurs technical challenges that must be tackled to efficiently provision on-demand resources with specific performance requirements. The project intends to push the applicability of both current techniques and the ones to be designed in this project to industry-scale CDCs, and identify metrics and variables to holistically control service-level agreements of hosted applications. Scalable solutions with industry approved software plug-ins are the major outcomes of this project. The outcomes of this project will have a substantial impact on both environmental (lowering energy consumption to lead to greener infrastructure) and economic sustainability (reducing cloud usage cost for both users and providers) of today’s much computerised society.Read moreRead less
Energy-Efficient Computing: Expanding the Role of Scheduling in Cloud Data Centres. Cloud data centres have become increasingly large-scale to meet ever increasing computing and storage capacity. The requirement of uninterrupted service availability has also contributed to such expansion. However, this relentless pursuit of high performance and high availability has led to serious resource over-provisioning and, in turn, low performance to energy consumption ratios. The impact of this poor resou ....Energy-Efficient Computing: Expanding the Role of Scheduling in Cloud Data Centres. Cloud data centres have become increasingly large-scale to meet ever increasing computing and storage capacity. The requirement of uninterrupted service availability has also contributed to such expansion. However, this relentless pursuit of high performance and high availability has led to serious resource over-provisioning and, in turn, low performance to energy consumption ratios. The impact of this poor resource management goes beyond the issue of cloud data centre efficiency, including excessive carbon footprint. This project aims to develop new energy-aware scheduling and resource allocation algorithms to provide energy-efficient solutions. These solutions exploit both workload and system diversity in cloud data centres.Read moreRead less
Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data ar ....Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data are needed. This project will design, implement and evaluate visualisation methods for massive multivariate network data sets. This research is expected to be used by Australian software development, biotechnology and security companies to exploit their data.Read moreRead less
Visual interaction methods for clustered graphs. This project aims to improve human understanding of huge network data sets, such as those arising in social networks, biological networks, and very large software structures. The project will enable analysts to explore and interact with such data sets, leading to better understanding.
Identifying technological trajectories using machine learning algorithms. This project aims to improve our understanding of why scientific knowledge progresses in certain directions and what causes it to grow faster or slower across fields. The project will create new neural-network machine-learning algorithms to scan patent and scientific article texts (specifications and claims) for natural language concepts. The results will potentially be used by patent offices to improve their own database ....Identifying technological trajectories using machine learning algorithms. This project aims to improve our understanding of why scientific knowledge progresses in certain directions and what causes it to grow faster or slower across fields. The project will create new neural-network machine-learning algorithms to scan patent and scientific article texts (specifications and claims) for natural language concepts. The results will potentially be used by patent offices to improve their own database search, by business analytics companies to reveal new technologies and potential collaborators, and by academic economists to understand how knowledge travels and accumulates.
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Visual analytics for high volume multi attribute financial data streams. While our ability to accumulate data (such as financial data) is increasing, our capability to analyse them is still inadequate despite technological improvements. The new Visual Analytics methods will allow processing of the massive and time-varying data so that the time-critical decisions can be made with minimum effort.
Approximate algorithms and architectures for area efficient system design. This project aims to develop simpler but reliable image recognition systems that can run on low-cost, small-scale platforms, for use in driver monitoring system (DMS) applications. Cheaper reliable DMS will lead to wider availability of this technology to end users and improve safety of motor vehicles. This project will develop approximate algorithmic and circuit techniques, provide training for research students and buil ....Approximate algorithms and architectures for area efficient system design. This project aims to develop simpler but reliable image recognition systems that can run on low-cost, small-scale platforms, for use in driver monitoring system (DMS) applications. Cheaper reliable DMS will lead to wider availability of this technology to end users and improve safety of motor vehicles. This project will develop approximate algorithmic and circuit techniques, provide training for research students and build capability in the area of approximate computing. It is also expected to lead to commercial products, licences and revenue, which will enable new job creation.
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