William Voorsluys

An autonomic cloud environment for hosting ECG data analysis services

Advances in sensor technology, personal mobile devices, wireless broadband communications, and Cloud computing are enabling real-time collection and dissemination of personal health data to patients and health-care professionals anytime and from anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful in terms of processing capabilities and information management and play a major role in peoples daily lives. This technological advancement has led us to design a real-time health monitoring and analysis system that is Scalable and Economical for people who require frequent monitoring of their health. In this paper, we focus on the design aspects of an autonomic Cloud environment that collects peoples health data and disseminates them to a Cloud-based information repository and facilitates analysis on the data using software services hosted in the Cloud. To evaluate the software design we have developed a prototype system that we use as an experimental testbed on a specific use case, namely, the collection of electrocardiogram (ECG) data obtained at real-time from volunteers to perform basic ECG beat analysis.

Reliable Provisioning of Spot Instances for Compute-intensive Applications

Cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault-tolerance techniques, namely check pointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.

The Australia urban research gateway

The

A data-driven urban research environment for Australia

20m Australian Urban Research Infrastructure Network (AURIN) project (www.aurin.org.au) began in July 2010. AURIN has been tasked with developing a secure, Web‐based virtual environment (e‐Infrastructure) offering seamless, secure access to diverse, distributed and extremely heterogeneous data sets from numerous agencies with an extensive portfolio of targeted analytical and visualization tools. This is being provisioned for Australia‐wide urban and built environment researchers – itself a highly heterogeneous collection of research communities with diverse demands, through a unified urban research gateway. This paper describes these demands and how the e‐Infrastructure and gateway is being designed and implemented to accommodate this diversity of requirements, both from the user/researcher perspective and from the data provider perspective. The scaling of the infrastructure is presented and the way in which it copes with the spectrum of big data challenges (volume, veracity, variability and velocity) and associated big data analytics. The utility of the e‐Infrastructure is also demonstrated through a range of scenarios illustrating and reflecting the interdisciplinary urban research now possible. Copyright

A scalable Cloud-based system for data-intensive spatial analysis

The Australian Urban Research Infrastructure Network (AURIN) project (www.aurin.org.au) is tasked with developing an e-Infrastructure to support urban and built environment research across Australia. As identified in [1], this e-Infrastructure must provide seamless access to highly distributed and heterogeneous data sets from multiple organisations with accompanying analytical and visualization capabilities. The project is tasked with delivering a secure, web-based unifying environment offering a one-stop-shop for Australia-wide urban and built environment research. This paper describes the architectural design and implementation of the AURIN data-driven e-Infrastructure, where data is not just a passive entity that is accessed and used as a consequence of research demand, but is instead, directly shaping the computational access, processing and intelligent utilization possibilities. This is demonstrated in a situational context.

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Advances in Cloud computing technology and the availability of affordable and easy to use Cloud services are enabling a multitude of scientific applications to use these resources as primary or secondary computing infrastructure. The urban and built environment research domain is one area that can benefit greatly from Cloud computing. The global population growth and increase in the size and population of cities raise many challenges for governments, planners and researchers alike. The Australian Urban Research Infrastructure Network (AURIN—http://www.aurin.org.au) project has been tasked with developing an advanced platform (e-Infrastructure) across Australia to tackle these challenges. The platform leverages large-scale Cloud resources to provide federated data access to, at present over 1100 data sets from major and often definitive government and industry data-rich organisations, and for scalable data processing and visualisation. The original AURIN tools were developed using the object modelling system (OMS) and supported integrated workflows to define and enact/re-enact scientific processes. More recently the work has evolved to focus more on delivery of a workbench offering a rich range of tools delivered through an extensible workflow environment. In this paper, we provide the background to AURIN including the scientific drivers that are shaping the work and the realisation of the Cloud-based AURIN environment. We focus in particular on the workflow environment and show how it seamlessly utilizes the Cloud for urban research processes focused especially on data-intensive spatial analysis. We illustrate the utilisation of this workflow environment across a range of case studies reflecting urban research activities.

Privacy-Preserving Data Linkage through Blind Geo-spatial Data Aggregation

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.

Brain Image Registration Analysis Workflow for fMRI Studies on Global Grids

The Australian Urban Research Infrastructure Network (AURIN -- www.aurin.org.au) project is a federally funded initiative across Australia that is focused on development and delivery of an advanced Cloud-based platform for research into the many challenges facing the major urban settlements of Australia. AURIN provides direct programmatic (federated) access to a wide range of urban and built environment data (over 1000 data sets) from at present 35 major and typically definitive data providers. Most of the data sets made available to AURIN are aggregated at source by the providers to particular geospatial regions, e.g. Local Government Authority, Statistical Areas, Census Districts amongst a multitude of other levels. Increasingly however researchers require access to less aggregate data sets -- often requiring data right down to the individual (unit) level. In this context, AURIN has developed blind, privacy preserving data linkage solutions exploiting the geospatial nature of urban data through collaboration with the Department of Health in Victoria. This paper describes these solutions and how they have addressed the many sensitivities in delivering secure access solutions over the Cloud.

Gridbus Work?ow Management System on Clouds and Global Grids

Scientific applications like neuroscience data analysis are usually compute and data-intensive. With the use of globally distributed resources and suitable middlewares, we can achieve much shorter execution time, distribute compute and storage load, and add greater flexibility to the execution of these scientific applications than we could ever achieve in a single compute resource.In this paper, we present the processing of Image Registration (IR) for Functional Magnetic Resonance Imaging(fMRI) studies on global Grids. We characterize the application, list its requirements and transform it to a workflow. We then execute the application on Grid’5000 platform and present extensive performance results. We show that the IR application can have 1) significantly improved makespan, 2) distribution of compute and storage load among resources used, and 3) flexibility when executing multiple times on global Grids.

Designing Adaptable Spatial Cyberinfrastructure for Urban eResearch

The Gridbus workflow management system (GWMS) is designed to execute scientific applications, expressed in the form of workflows, onto Grid and Cloud resources. With the help of this system, we demonstrate a computational and data-intensive Image Registration (IR) workflow for functional Magnetic Resonance Imaging (fMRI) applications on Clouds and global Grids. We also present a demonstration of the Aneka System. Aneka is a .NET based Cloud software platform that provides: (i) a configurable service container hosting pluggable services for discovering, scheduling various types of workloads and (ii) a flexible and extensible framework supporting various programming models. We use distributed Grid and Cloud resources from Australia, Austria, France, Japan, and USA for the executions of image registration workflow, and resources from Melbourne University for executing map-reduce applications in Aneka cloud environment.