Cloud Case Study Precis


This page concisely describes public cloud use for research computing and data science at the University of Washington.

We track close to 100 research projects using the public cloud for computing across issues of access, time, compute power, storage, data management, cost and other issues. The summary below is organized by domain. Our work in consulting and advocating for use of the public cloud is integrated with the mission and operation of the UW eScience Institute.

CC*IIE Remarks

What are the impacts of the CC*IIE (NSF) sponsored projects? How has it contributed?


Much of the impact of this work is speculative. The general topic is public cloud computing applied to research computing, broken down into smaller segments of work. These efforts were motivated as contributions to specific projects; and we comment on their impact on those projects. However the work was also carried out with the idea of solving broader recurring challenges. For example we developed a push notification of cloud spending on AWS to catch unauthorized account use and runaway resource allocation by student researchers; but this was in view of the fact that any research groupd taking advantage of the AWS cloud will benefit from the same mechanism. At-a-glance cost summaries that arrive by email are easy to monitor in contrast with every day logging in to a cost tracking console and configuring a set of queries. The former process takes a matter of seconds, the latter requires 15 minutes.

A note on scale: The group responsible for the cloud adaptation work described here as supported by NSF is tasked with supporting the entirety of University of Washington research including medicine. This large-scale proposition is currently manageable because cloud computing is a relatively new technology that requires a certain investment of time to master. If we are on the verge of a phase transition to large-scale cloud adoption then we are in the calm before the storm. We want to take advantage of the comparative lull to create as many pathways and patterns to successful cloud adoption in preparation for the increase in migration volume. This NSF-sponsored project has been a windfall enabling us to bring in students and staff towards this end.

Guiding concepts for this report

  • Discussion of data resources
  • Acquisition of data skills
  • Emergence of career paths (data scientists)
  • New disciplines

Cloud Computing Innovation and Cyber-Infrastructure Elements for Research Topics

  • Data security
  • Data sharing
  • Computing at scale
  • Institutional IT support for cloud adoption/migration
  • Internet of Things in relation to the public cloud
  • Student access to cloud platforms for research computing
  • Connectivity for high-speed data transfer from the lab to the cloud

What is the impact on the development of the principal discipline(s) of the project?

To set the stage for our remarks on impact we describe a research transition to the cloud. Cloud computing represents a break with traditional research computing on ‘owned resources’; and as such – because of the perceived cost of change – it requires considerable motivation to overcome social inertia. This motivation often originates from a desire to stop purchasing and maintaining computers. However the cloud has so many additional benefits that the inquisitive research team often finds there is more benefit from what the cloud actually offers than there is in leaving behind purchase orders and operating sytem patches. We call this perceptual shift ‘cloud socialization’. Lastly it is important to note that a major impetus towards cloud socialization is the advent of cloud-hosted services. GoogleDrive is a typical example but in the research the most impactful development has been the Git paradigm for open source code control, for example as embodied by GitHub repositories.

We began discussion with a postdoc working within a well-funded molecular engineering laboratory. This provided an opportunity to explore cloud computing scale as a means of solving two related problems. First protein folding (the central research task) requires a huge number of computations to resolve peptide configuration in a search space of 10 trillion possible configurations. Second, a research lab operating its own computers will experience intermittent resource demand with a job queue. Our solution was to work with AWS representatives to harness a cluster technology called Batch which enabled the postdoc to run a 2-week computation in 53 hours at a cost of $3500. This cost is approximately 3x the corresponding cost on owned hardware. In strict terms this means that the cloud is not cost competitive with owned hardware unless one or more of the following considerations comes into play:

  • The researcher tasks owned compute resources at <= a 1/3 duty cycle
  • The research scientist values their own time spent waiting for processing runs to start / complete
  • There is more computation to be done than can be supported on owned resources
  • The university hides or subsidizes cost of operations, for example power and HVAC
  • The cost of cloud computing continues to drop, consistent with the current multi-year trend

Our engagement here had the impact of producing a library of ten million peptide structures in a reference dataset that will be made publicly available. However the principle impact of this work has been the documentation of the process within the cloudmaven resource corpus. By capturing cloud adoption success stories such as this one we provide a means for addressing the scale of research computing cloud migration as the social phase transition accelerates over the next decade. The methods described here will – in the near term for this laboratory – provide an alternative to standard computing practice that eliminates queue time and shortens the time to complete processing runs. (The AWS Batch process secured 164 high-powered computers for the 53-hour duration of the compute task.)

To be very specific about how this effort drives a new approach in science: From molecular engineering to genomics to the hydrology of high mountain Asia (all real projects we are involved in): Scientists tend to compute in bursts and the cloud is at cost parity with on-premise computers; the cloud is much much powerful than owned machines; the cloud is public and hence dissociated from any particular funding source or agency; and so the public cloud allows scientists to ask deeper and more computationally intensive questions without purchasing a single extension cord; at the cost of the time investment to learn the procedures and services; and at the cost of just the resources (computers) used. Cloud computing is a new solution for moving research forward, for coping with the data deluge problem, for not being constrained by available compute resources. To this end we are documenting solutions and building a trans-disciplinary community of practice that is leading the way into this new solution.

What is the impact on other disciplines?

This work included as one of its focus topics the security of data that are moved, managed and analyzed on the public cloud. The cloud solution involves (particular to AWS) a virtual private cloud construct that guarantees isolation of the data (i.e. unauthorized data access via internet is virtually impossible). Furthermore data encryption can be required and guaranteed (at rest and in motion) and access logging is facilitated to track parties that come into contact with the secured data. Together with best practices this ensemble of technologies essentially guarantees data security for a given project where the largest threat is (as one might expect) human error.

A signal impact of our data security work can be cited from hydrological science: Proprietary data (for example river stage) received from countries can be sequestered on private subnetworks and used to force or validate hydrological models with no risk of public dissemination of those data.

Extending beyond hydrology and the proprietary ‘national asset’ nature of water resource data: Our group has been in contact with research teams from other disciplines looking for a cloud-based data security template. These include researchers from clinical trial research, disease and epidemiology, laboratory medicine (for example annotation of genomic markers), genomics, demography, business, and data centers that have some degree of contact with sensitive data. Our progress in this area is helping to build the cyberinfrastructure of data security on the public cloud.

What is the impact on the development of human resources?

University IT staff are faced with a constantly shifting landscape of technology; which they are obliged to filter through, understand, and pass along to a somewhat indifferent (or intransigent) user community in the form of stable, reliable services. As a perceived edifice the public cloud can present an intimidating prospect to IT staff: Rather than one or two new ideas, a given cloud provider can surface 100 or more new technologies. To hazard a normative statement in response to this observation: It is incumbent on early cloud adopters – be they researchers, IT staff, graduate students, undergraduates, or administrators – to leave a well-documented path in the wake of their learning and solution-building projects. This path should emphasize ‘how it was done’ but as importantly ‘how a new person can learn the background – in depth – to do likewise.’

From this there follows a corollary: University administration must keep pace by providing for the training and hiring of IT staff in support of cloud computing. Research is a natural avenue for this growth because it is by its nature unbounded. The work carried out in this project produced a set of case studies with documentation; where the attempt has been to imply such a path, from cloud newcomer to cloud adept. The shift of training on the cloud will take diligence but we feel we have contributed some impetus to the process. Cloud working groups and workshops for community of practice will be the follow-on sign posts as the effort continues.

One further aspect of this sociological and technical shift deserves mention. The most flexible and adaptable demographic within the university community is (arguably) the student population. It was therefore a natural choice to hire several students in the course of this work to take on tractable problems and build solutions; for example push notifications of daily cloud expenditure. This approach proved to be extremely effective. Further: Through a student computing organization these students have the opportunity to transfer their enthusiasm and technical skills for cloud computing to others; and thence into research groups and through the community in general.

What is the impact on physical resources that form infrastructure?

Research teams are rapidly improving their table tennis skills, having emptied their server rooms and installed high-quality ping pong tables.

What is the impact on informational resources that form infrastructure?

The GitHub repository-based website is the principle informational resource proceeding from this work. The website captures basics of cloud computing, provides links to instructional resources online, and follows cloud case studies at the level of tutorials. These enable subsequent research teams to accelerate the pace at which they can build cloud-based research computing tools, workflows, websites, data repositories and other data-arc solutions.


  • Contact us regarding updates to this material
  • Focus here is topics; we try to preserve a degree of anonymity

Yun Zhao Guan: A cloud library – digital curation – in cooperation with Suzallo Library

General Systems and Tools

  • SQL Share: A system for managing, sharing and manipulating research data.
  • Myria: A distributed, shared-nothing Big Data management system and Cloud service from the University of Washington
  • IOT based on Arduino Yun leaf technology and cloud IOT endpoint services
  • Geohackweek and Neurohackweek: Hosting intensive workshops for learning and developing cloud-based tools and methods at UW

Student-driven research

The UW Student High Performance Computing Club has begun making cloud computing available to its members. This includes training and consulting on implementation as well as careful cost management and tracking. The following is a partial list of projects undertaken by students during the pilot phase of this program, spring 2017.

  • Epigenome imputation across a nucleotide-protein-cell tensor (Status: Successful completion)
  • Design of a high-reliability micropump for cooling high-heat semiconductors (In progress)
  • Novel peptide characterization of marine organic matter: insights into carbon cycling (In progress)
  • Characterizing the progression of three pathologies in ER electronic medical records (In progress)
  • Quora question pair intent comparison (In progress)
  • Novel peptide characterization of marine organic matter: insights into carbon cycling (In progress)
  • Schedular development and benchmarking for containerized bioinformatics workflows (UW Tacoma; in progress)
  • Empirical Studies of Docker Orchestration Tools for The Analyses of Big Biomedical Data (UW Tacoma; in progress)
  • Predictive models to optimize cloud computing using genomics data (UW Tacoma; in progress)
  • A Dynamic Scaling Engine in the Cloud (CSE; in progress)
  • LaraDB Experiments for the DARPA Graph Challenge (CSE; in progress)
  • Learning multiple outcomes with predictive coding (CSE; in progress)


  • Laboratory Medicine: Genome analysis and annotation (clinical oncology & co)
  • Clinical data availability for research
  • Data access and tool access for MRI- and EEG-based research
  • Gut biome metagenomics (Children’s Hospital)
  • Patterns in unexpected in-hospital mortality
  • Studies on Post-hospital-admission sepsis (blood infection)
  • Deep learning for patient behavior prediction: EEG data in relation to A/V transcripts of patient behavior
    • See above under Student research
  • Canine longitudinal aging studies
  • Biostatistics
  • Light-sheet microscopy for fast-turnaround biopsy analysis
  • Neuroimaging: Functional MRI
  • Neuroimaging: Visual cortex studies

Genomics and Biochemistry (not included in Medical above)

  • Epigenome imputation: See above under Student Research
  • Genetic architecture of autism
  • Metagenomics of methane-consuming microbial communities
  • Enzyme inhibition molecular structure
  • Peptide scaffolding enumeration and design: Large-scale computing using the Rosetta protein folding toolkit

Hydrology and Geochemistry

  • GDS: Geometabolomics Data System, a community library and reproducible workflow environment for molecular spectral analysis applied to naturally occurring Dissolved Organic Matter (DOM).
  • HiMAT (NASA): Atmosphere-land coupled analysis of the hydrological state and future of high mountain Asia
    • Hydrological studies and human impacts drawing from in situ, remote sensing, model, re-analysis and assimilation data and methods.
  • Dynamic Infomation Framework (DIF) (World Bank): Scientific hydrological expertise transferred into public information
    • In resource management and public safety domains the incorporation of scientific modeling is not well developed.
    • This program provides localized information building from a reproducible model of free and open access

Ocean science

  • LiveOcean: Ocean modeling forecast
  • Marine microbial ecology
  • Mesoscale eddie structure and correlation to marine life

Computer Science

  • Analysis of code fault detection: Student project
  • IOT: A design pattern and tutorial for using cloud-based support of Internet of Things implementations (NSF: Campus Cyberinfrastructure)
  • Data security on the cloud: A generic data system with automated and human protocols for working on sensitive data including elements of compliance with oversight regulations (NSF: Campus Cyberinfrastructure)
  • Scale on the cloud: See under Molecular Engineering and Science the protein folding case study (NSF: Campus Cyberinfrastructure)
  • Collaboration on the cloud: See case studies herein on GeoServer/THREDDS, on LIDAR, on Dynamic Information Frameworks and on HiMAT; thematically lightweight geospatial data system with the underlying theme of ‘access to data through pre-built frameworks, data APIs and minimal (non-redundant) software engineering. (NSF: Campus Cyberinfrastructure)

Mechanical and Civil Engineering

  • Computational fluid dynamics of hydrogen and methane combustion


  • Identifying stellar composition through spectral model superposition in nearby galaxies
  • Large Scale Synoptic Telescope (LSST) toolchain development


  • Implementation of GeoServer and a THREDDS server on the public cloud
  • Various data archival projects: Using the cloud for many 9s of reliability

Stubs and pending

  • IOT
  • Power consumption