Introduction to AWS (Amazon Web Services) Ground Station

Dr John Hildebrandt1

1Amazon Web Services, Barton, Australia

 

AWS Ground Station is a fully managed service that lets you control satellite communications, downlink and process satellite data, and scale your satellite operations quickly, easily and cost-effectively without having to worry about building or managing your own ground station infrastructure. Ground stations are at the core of global satellite networks, which are facilities that provide communications between the ground and the satellites by using antennas to receive data and control systems to send radio signals to command and control the satellite. Today, you must either build your own ground stations and antennas, or obtain long-term leases with ground station providers, often in multiple countries to provide enough opportunities to contact the satellites as they orbit the globe. Once all this data is downloaded, you need servers, storage, and networking in close proximity to the antennas to process, store, and transport the data from the satellites.

AWS Ground Station eliminates these problems by delivering a global Ground Station as a Service. We provide direct access to AWS services and the AWS Global Infrastructure including our low-latency global fiber network right where your data is downloaded into our AWS Ground Station. This enables you to control satellite communications, ingest and process your satellite data, and integrate that data with your applications and services running in the AWS Cloud. For example, you can use Amazon S3 to store downloaded data, Amazon SageMaker for building custom machine learning applications, and Amazon EC2 to command and download data from satellites.


Biography:

John has over 30 years’ experience in the Computer Science, Enterprise Information Technology and Research areas. His experience includes the areas of solutions architecture, technology strategy, application architecture, distributed systems, image databases, and image analysis.

John has extensive Public Sector, Defence and Intelligence experience over 30 years.John worked in the Defence Science and Technology Organisation (DSTO) as a Senior Research Scientist leading teams undertaking cutting edge Image Analysis and Distributed Systems research.

Before joining AWS he had senior roles at Microsoft and IBM providing Architectural advice to Federal Government. John is currently employed as a Solutions Architect with Amazon Web Services (AWS) in Canberra. He was the first employee for the growing ANZ Public Sector team and has been active in growing the team.

John assists Public Sector customers envisage Cloud solutions. John has trained hundreds of customers the AWS platform and has presented at Australian and International Summits.

Speak of the (Geosciences) DeVL – she’s extending!

Dr Lesley Wyborn1, Dr Carsten Friedrich2, Dr  Ben Evans1, Dr Nigel Rees1, Professor Graham  Heinson3, Dennis Conway3, Dr Michelle Salmon4, Dr Meghan Miller4, Julia Martin5, Dr Jens Klump6, Dr Mingfang Wu7, Mr Ryan Fraser6, Dr Tim Rawling8

1NCI, ANU, Canberra, Australia
2Data 61, Canberra, Australia
3The University of Adelaide, Adelaide, Australia
4Research School of Earth Sciences, ANU, Canberra, Australia
5Australian Research Data Commons, Canberra, Australia
6Mineral Resources, CSIRO, Perth, Australia
7ARDC, Melbourne, Australia
8AuScope, Melbourne, Australia

 

Since 2017 the Australian Research Data Commons (ARDC) has co-funded the Geosciences Data-enhanced Virtual Laboratory (DeVL) project in collaboration with AuScope, National Computational Infrastructure (NCI), CSIRO, the Research School of Earth Sciences (RSES) of the ANU, The University of Adelaide, and Curtin University. The project is a first step in realising the AuScope Virtual Research Environment (AVRE) as part of a strategic goal to develop a data assimilation and geoscientific discovery and analytics platform for the Australian continent. The Geosciences DeVL has four work packages: Magnetotellurics (MT), Passive Seismic (PS), International Geo Sample Number (IGSN), and the AVRE platform and portals.

The University of Adelaide MT collection is being curated through collaboration with NCI, the Geological Survey of South Australia, and The University of Adelaide. Datasets, including time-series and processed data, are now discoverable and accessible through NCI’s catalogue and data services, and available for download and further HPC processing and data analysis through AVRE.

The PS work package is progressively releasing the RSES PS collection through the AusPass portal (supported by funding from other sources).

A collaboration between ARDC, CSIRO, and Curtin University has developed an IGSN minting service to allocate globally unique identifiers for academic geochemistry samples: this service is now being extended to researchers from other disciplines.

The AVRE component, led by CSIRO, is focused on improving access to geophysics data through a common AuScope portal infrastructure, and with ARDC, on improving the description, discovery, and execution of software, workflows and scientific solutions relevant to Australian geoscience.


Biography:

Lesley Wyborn is an Adjunct Fellow at the National Computational Infrastructure and RSES at ANU and works part time for the Australian Research Data Commons. She previously had 42 years’ experience in scientific research (geochemistry and mineral systems research)  and in geoscientific data management in Geoscience Australia from 1972 to 2014. In geoinformatics her main interests are developing international standards that support the integration of Earth science datasets into transdisciplinary research projects and in developing seamless high performance data sets that can be used in high performance computing environments. She is currently Chair of the Australian Academy of Science ‘National Data in Science Committee’ and is on the American Geophysical Union Data Management Board. She was awarded the Australian Government Public Service Medal in 2014, the 2015 Geological Society of America Career Achievement Award in Geoinformatics and the 2019 US Earth Science Information Partners Martha Maiden Award.

The AuScope Virtual Research Environment (AVRE): A Platform for Open, Service-Oriented Science to ‘Understand the Earth’

Ryan  Fraser2, Dr Tim Rawling3, Dr Lesley Wyborn1, Dr Carsten Friedrich4, Dr Ben Evans1, Associate Professor Meghan Miller5, Professor  Brent McInnes6, Professor Louis  Moresi5, Dr Carsten Laukamp2, Nicholas Brown7

1NCI, ANU, Canberra, Australia
2Mineral Resources, Perth, Australia
3AuScope Limited, Melbourne, Australia
4Data 61, Canberra, Australia
5Research School of Earth Sciences, ANU, Canberra, Australia
6Curtin University, Bentley, Australia
7Geoscience Australia, Canberra, Australia

 

Since 2006, NCRIS projects (AuScope, NCI, ANDS, Nectar, RDS), and Government Agencies (GA, State/Territory Geological Surveys) have collaborated on building a suite of data portals, tools, software and virtual laboratories to support a diverse community of Earth scientists operating on a range of computational facilities including HPC, cloud, on-premise servers and desktops.

The 2016 National Research Infrastructure Roadmap noted that, to secure global leadership for the Earth Sciences over the next decade, Australia must now “enhance integration of existing data and mathematical modelling across large geographical areas to establish the next generation of ‘inward looking telescopes’ to better understand the evolution Earth’s crust and the resources contained within it”.

in 2017 the AuScope Virtual Research Environment (AVRE) was launched to support this new ambition, through enabling FAIR access to valuable academic research data collections and software, and improving coordination of existing national infrastructures. The aspiration was to realise a service-oriented science platform that will empower data assimilation and modelling across three networks: geophysics, geochemistry and geology.

AVRE cannot operate in isolation within the Australian Earth science community and will seek to ensure it can link to, and interoperate with data and services from other national NCRIS  research infrastructure initiatives (ARDC, NCI, TERN, IMOS, etc.). AVRE will also undertake a coordinated approach to optimising international partnerships and strategic collaborations in equivalent eResearch infrastructures globally. The end goal is to ensure Australian Earth science data and analytics can play a leadership role in next-generation transdisciplinary research to more comprehensively ‘Understand the Earth’.


Biography:

Ryan Fraser is now Program Leader for the AuScope Virtual Research Environments and has a long history with the former AuScope Grid program. He also lead the development of many collaborative eResearch projects with NeCTAR, ANDS and RDS, including the Virtual Geophysics Laboratory and the AuScope Scientific Software Solutions Centre.

Within CSIRO he is a skilled Portfolio Manager, with over 15 years of experience working in R&D, commercialisation of products and delivery to both government and industry using agile methodologies. Ryan has lead large, complex technology projects, managing sizeable software and interdisciplinary teams. His key focus is developing and fostering highly collaborative teams to deliver programs of work and continually grow capability to embark on future work

Ryan possesses specialist knowledge in decision science, data analytics, spatial information infrastructures, Disaster Management and Emergency Response, Cloud Computing, Data Management, and Interoperability and have extensive experience in managing and successfully delivering programs.

How Ultra-high dimensional machine learning and serverless cloud architecture transforms life science research

Dr Denis Bauer1

1Csiro, North Ryde, Australia

 

Genomic data is outpacing traditional Big Data disciplines, producing more information than Astronomy, twitter, and YouTube combined. As such, Genomic research has leapfrogged to the forefront of Big Data and Cloud solutions using machine learning to generate insights from these unprecedented volumes of data. This talk showcases how researchers at CSIRO find disease genes responsible for ALS using VariantSpark, which is a custom machine learning implementation capable of dealing with ‘wide’ or ultra-high-dimensional data (80 million columns). Using this powerful technology in a novel cloud-based architecture powers a decision support framework for clinicians to find actionable genomic insights at a speed required for point-of-care applications. The talk also introduces GT-Scan, which we think of as the “search engine for the genome”. This web-service enables researchers to identify the optimal spot in the 3 billion letter-long genome to make alterations (CRISPR) that one day help cure or prevent diseases. GT-Scan was one of the first fully serverless applications, a space pitted to grow to a $8 Billion market as it allows complex architectures to instantaneous scale with high-demand while maintaining a zero cost downtime. The talk concludes by touching on how to evolve cloud architecture more efficiently through an hypothesis-driven approach to DevOps and how to keep data and functions secure in an serverless environment.


Biography:

Dr Denis Bauer is an internationally recognised expert in machine learning, specifically in processing big genomic data to help unlock the secrets in human DNA. Her achievements include developing an open-source, artificial intelligence-based cloud-service that accelerates disease research and contributes to national and international initiatives for genomic medicine funded with over $500M.

As CSIRO’s transformational bioinformatics leader, Denis is frequently invited as a keynote at international medical and IT conferences including Amazon Web Services Summit 2018, International conference on Frontotemporal Dementia ‘18, Alibaba Infinity Singapore ’18 and Open Data Science Conference India ’18. Her transformational achievements have been featured in the international press, e.g. GenomeWeb, ZDNet, Computer World, CIO Magazine, the AWS Jeff Barr blog, and was listed as ComputerWeekly’s Top 10 IT stories of 2017.

Denis holds a BSc from Germany and PhD in Bioinformatics from the University of Queensland, and has completed postdoctoral research in both biological machine learning and high-throughput genetics. She has 33 peer-reviewed publications (14 as first or senior author), with over 1000 citations and an H-index 14.

Denis advocates for gender equality in IT, and is active on CSIRO’s Inclusion and Diversity committee.

Containers in High Performance Computing

Mr Mark Gray1, Dr Marco De La Pierre1, Dr Ben Menadue2

1Pawsey Supercomputing Centre, Kensington, Australia, 2NCI, Canberra, Australia

 

This hands-on workshop teaches containers in the context of high performance computing, in both cloud and supercomputing environments.  Staff from Australia’s two tier-1 supercomputing centres, NCI and the Pawsey Supercomputing Centre, will facilitate the workshop.

Containers allow applications to be bundled into a single, lightweight piece of software that provides everything needed to run them, including software dependencies, runtimes, system libraries, and configurations.  Containers enable moving software and workflows between systems with minimal effort.  The software can be distributed to collaborators with reliable results.

Both Pawsey and NCI support users running Docker containers within their cloud services.

NCI currently use containers as a way of providing newer operating system features not available in the base OS image of Raijin. This allows provision of more modern operating system runtimes, without causing compatibility issues for existing users.  Similarly, Pawsey supports using containers on Magnus and Zeus.


Biography:

Mr Gray brings a strong research and data management background to the Pawsey Supercomputing Centre.  He manages all aspects of the research cloud service at Pawsey (called Nimbus), which is utilised by researchers around the globe.  His activities include procurement, deployment, training, user expectation management – and leadership of the team who administer and operate the service.

Mr Gray represents the Pawsey Supercomputing Centre both nationally and internationally, with respect to the use of Nimbus.  He also provides project management skills for projects of key strategic importance to the Centre.

Marco completed a PhD in Materials Science, specialising in theoretical and computational chemistry. Joining Pawsey in 2018, he engages with researchers in the fields of computational materials science, computational chemistry and bioinformatics. Building on his experience in scientific software development, Marco has recently being focused on improving efficiency and throughput of bioinformatics pipelines through container technology and workflow management tools.

Ben Menadue has a PhD in computational physics, specifically Lattice Quantum Chromodynamics (QCD) .  Ben was a user of both NCI and Pawsey systems for his research before joining NCI..  Ben is a senior HPC systems specialist at NCI, with activities of managing systems through to supporting and training users

Research on AWS – Accelerating Time to Science Workshop

Dr John Hildebrandt1

1Amazon Web Services, Barton, Australia

 

There are many common technology challenges emerging in science and research, especially when doing research at scale. Some of these challenges include but are not limited to; capturing, storing and analyzing increasingly massive data sets.

Access to on-demand, highly reliable, secure and scalable services and platforms to perform these tasks means researchers can often dramatically reduce time to a research outcome, and drive exciting scientific discoveries.

AWS as a research platform reduces the cost of trying new ideas; provides massive scale storage, compute, database, HPC and other platform services immediately to researchers, which in turn fosters and encourages experimentation and innovation.

This workshop, is focused on enabling you to work with large scientific datasets in a meaningful way and will also include hands on lab time with a few common data processing tools in the sciences domain.

This workshop is designed to teach you how to:

  • Efficiently move scientific data into and out of the AWS cloud.
  • Use the deep security models provided by AWS to ensure security and privacy of your data.
  • Establish patterns for approaches for doing high performance compute on AWS, especially when processing large datasets.

This Workshop is intended for:

  • Researchers wishing to understand how AWS might enable them to perform their data processing tasks faster and more effectively
  • Scientific Computing users interested in how AWS helps solve HPC and HTC challenges

Please note that this workshop requires you to bring your own device and an AWS account.


Biography:

John has over 30 years’ experience in the Computer Science, Enterprise Information Technology and Research areas. His experience includes the areas of solutions architecture, technology strategy, application architecture, distributed systems, image databases, and image analysis.

John has extensive Public Sector, Defence and Intelligence experience over 30 years.

John worked in the Defence Science and Technology Organisation (DSTO) as a Senior Research Scientist leading teams undertaking cutting edge Image Analysis and Distributed Systems research.

Before joining AWS he had senior roles at Microsoft and IBM providing Architectural advice to Federal Government.John is currently employed as a Solutions Architect with Amazon Web Services (AWS) in Canberra. He was the first employee for the growing ANZ Public Sector team and has been active in growing the team.

John assists Public Sector customers envisage Cloud solutions. John has trained hundreds of customers on AWS and has presented at Australian and International Summits.

 

 

Introduction to running Auto Scaling Geoserver and PostgreSQL/PostGIS in the Cloud (AWS)

Dr John Hildebrandt1

1Amazon Web Services, Barton, Australia

 

This workshop will provide an introduction to running a Geospatial data server on AWS. We will leverage GeoServer; GeoServer is a Java based, open source server that allows users to view and edit geospatial data. It leverages open standards from the Open Geospatial Consortium (OGC). You will install GeoServer and load a dataset onto the server.

GeoServer can leverage a variety of data sources including direct support for the PostGIS product. PostGIS is a spatial database extender for PostgreSQL object-relational database. With AWS support for PostgreSQL and PostGIS in RDS we will explore connecting GeoServer to a PostGIS information source to illustrate a multitier scalable Infrastructure. Finally we will explore scaling out the GeoServer web tier using an AutoScaling Group (ASG) and an Elastic Load Balancer(ELB). Amazon EFS will be leveraged as a common file system for the Geoserver web tier. Attendees will need an AWS accounts for the lab.


Biography:

John has over 30 years’ experience in the Computer Science, Enterprise Information Technology and Research areas. His experience includes the areas of solutions architecture, technology strategy, application architecture, distributed systems, image databases, and image analysis.

John has extensive Public Sector, Defence and Intelligence experience over 30 years. John worked in the Defence Science and Technology Organisation (DSTO) as a Senior Research Scientist leading teams undertaking cutting edge Image Analysis and Distributed Systems research.

Before joining AWS he had senior roles at Microsoft and IBM providing Architectural advice to Federal Government.John is currently a Solutions Architect with Amazon Web Services (AWS) in Canberra. He was the first employee for the growing ANZ Public Sector team and has been active in growing the team.

John assists Public Sector customers envisage Cloud solutions. John has trained hundreds on  the AWS platform and presented at Australian and International Summits on AWS technologies.

Deep Regressor Chain: An Approach for Predicting Long Term Water Quality Change

Dr Yifan Zhang1, Mr Peter Fitch2, Dr Peter Thorburn1

1CSIRO, St Lucia, Australia,

2CSIRO, Canberra, Australia

 

Water quality is an important issue because of its effects on human health and aquatic ecosystems. An understanding of the long term trends in water quality is extremely important for scheduling water quality management activities.

Data-driven models have gained much attention for predicting nonlinear time series in hydrological modelling, yet predicting long term water quality change is still a big challenge. Firstly, most of these studies are restricted to predicting water quality in one short upcoming time step. In this approach, hourly/daily predicative models only predict water quality in the next one hour/day, and provide no information on the longer-term trends in water quality. Secondly, while some data-driven models can predict monthly or yearly water quality changes, they either use or resample the data with monthly or yearly time interval. Therefore, the ‘long term’ prediction still follows the one time step idea and has the same single prediction issue.

We proposed a deep learning based method that we call Deep Regressor Chain (DRC) to overcome the above issues. DRC connects multiple recurrent neural network (RNN) models in order. The 1st RNN uses N numbers of time series data and predicts at time step N+1. The 2nd RNN combines the previous RNN’s inputs and prediction together as the new input to predict at time step N+2. Followed by this hybrid strategy, DRC can predict long term water quality in N+m upcoming time steps at once by integrating all the previous predictions and no extra water quality data resampling work is needed.


Biography:

Yi-Fan Zhang is a Postdoctoral fellow in Agriculture & Food, CSIRO. He received a PhD in data science from Queensland University of Technology in 2016. His work focuses on deep learning for agriculture decision making and management, with an emphasis on water quality time series modelling and forecasting.

The AuScope Geoscience DEVL: a multipurpose Virtual Research Environment that caters for multiple use cases, a range of scales and diverse skill sets

Dr Lesley Wyborn1, Ryan  Fraser2, Dr Tim Rawling3, Dr Carsten Friedrich4, Dr Ben Evans1

1National Computational Infrastructure, Action, Australia,

2CSIRO Mineral Resources, Kensington, Australia,

3AuScope, Melboune, Australia,

4Data 61, Canberra, Australia

 

For over a decade, AuScope has been delivering physical, software and data research infrastructure to the Australian Solid Earth research community across the geophysics, geochemistry and geodesy domains.  Initially, access to data ‘libraries’ and software tools was provided through websites and portals. In 2010, an experimental Virtual Geophysics Laboratory (VGL) was created to provide integrated access to both data and software to enable researchers to achieve ‘online’ workflows that facilitated processing, lowered the barriers to entry and increased uptake of these resources.

Demands soon followed for specific laboratories to be built for geohazards,  geochemistry, mineral exploration and more: succeeding virtual laboratories were built using the generic Portal Core and VL Core software. However, finding the ‘sweet spot’ that resulted in maximum usage for a given amount of effort, proved difficult. For example, some users were wanting more effort to be put into user interfaces, whilst others were wanting specific, more complex processing workflows to be added.

To better coordinate the existing Australian Solid Earth Geoscience eResearch infrastructures the AuScope Virtual Research Environment (AVRE) is now being created to enable users with varying skills to specifically target their needs and access a range of online data and software resources to either create their own workflows in their own environment or utilise pre-existing workflows on a variety of computational infrastructures.

Funding from the Australian National Data Service (ANDS), National eResearch Collaboration Tools and Resources (NECTAR) and Research Data Services (RDS) will be utilised with co-contributions from AuScope to develop this new platform.


Biography:

Lesley Wyborn is an Adjunct Fellow at both the National Computational Infrastructure (NCI) Facility and the Research School of Earth Sciences at the Australian National University (ANU). She had over 40 years’ experience in scientific research and in transparent management of geoscientific data in Geoscience Australia. Her scientific research interests are in Mineral Systems analysis and in granite geochemistry whilst her current informatics interests are on global integration of transdisciplinary data sets, enabling in situ analytics in virtual research environments, and in generating High Performance Data sets.  She is currently Chair of the Australian Academy of Science ‘Data for Science Committee’ and is on the American Geophysical Union (AGU) Data Management Board. In 2014 she was awarded the Australian Government Public Service Medal for her long-term contributions to the management of Australian Public Sector Geoscience Data and in 2015, the Geological Society of America Career Achievement Award in Geoinformatics.

ASKAP’s End-to-end Science Pipelines – Producing Science-ready Data Products with the High-Performance ASKAPsoft Processing Pipelines

Dr Matthew Whiting1

1Csiro, Epping, Australia

 

The Australian Square Kilometre Array Pathfinder (ASKAP) is an innovative wide-field, high-data-rate radio-synthesis telescope, that requires high-performance processing pipelines for calibration and imaging. This poster describes these pipelines, their content and implementation at the Pawsey Supercomputing Centre. These pipelines have been demonstrated with commissioning and Early Science observations, and are regularly used by science team members.


Biography:

Matthew Whiting is acting Group Leader for ATNF Science, part of CSIRO Astronomy & Space Science, leading a group responsible for research and science operations within the Australia Telescope National Facility. He has a PhD in astrophysics from University of Melbourne, and a career of working in astronomy and astronomical computing. Matthew leads the development of the processing pipelines for CSIRO’s ASKAP telescope, and is a member of the small software team responsible for the high-performance calibration & imaging software for ASKAP. He is also the sole developer of the widely-used Duchamp source-finding software, designed for 3D astronomical spectral-line surveys. He works closely with the Pawsey Supercomputing Centre, and is a member of Astronomy Australia Limited’s Astronomy e-Research Advisory Committee.

ABOUT AeRO

AeRO is the industry association focused on eResearch in Australasia. We play a critical coordination role for our members, who are actively transforming research via Information Technology. Organisations join AeRO to advance their own capabilities and services, to collaborate and to network with peers. AeRO believes researchers and the sector significantly benefit from greater communication, coordination and sharing among the increasingly different and evolving service providers.

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