PEARC17 presentation on Cloud-enabling a Collaborative Research Platform: The GABBs Story

1. CLOUD-ENABLING A COLLABORATIVE
RESEARCH PLATFORM
THE GABBS STORY
Rajesh Kalyanam, Robert Campbell, Derrick Kearney, Leif Delgass, Larry
Biehl, Lan Zhao, Carolyn Ellis, Carol Song
Purdue University

2. I have historical
data on sub-
Saharan maize
yields
I’d love to use that
historical data in my
model predicting the
effect of climate
change on yields
I used the maize
yield data in my
thesis, how do I
cite it?
RESEARCH COLLABORATION LANDSCAPE

3. SCIENCE GATEWAYS
• Website for a scientific research community
• User, data, & content management
 Data sharing, access control
 Data processing, visualization, publication
 Wiki, static web pages, ticketing system
• Research tools
 Tool creation and web deployment
 Optional HPC resource availability
and more…

4. GEOSPATIAL DATA ANALYSIS BUILDING BLOCKS
Data Management
 Data sharing
 Geospatial
metadata
extraction
 Geospatial data
discovery (search)
 Data publication
Visualization
 Geospatial preview
 Diverse data format
overlays
 Visualization library
and control widgets
 Hardware and
software rendering
Computation
 Geospatial data
transformation
 Subset extraction
 Extended metadata
capture
 GIS-enabled
workflows
GABBs provides these domain-specific capabilities for
geospatial science gateways

5. GABBS SYSTEM ARCHITECTURE
• Based on HUBzero
cyberinfrastructure framework
• Containerized tools are web-
accessible via VNC
• Scalable, external
compute/render servers
 Support tool sessions
 Provide hardware and
software rendering
• represent
GABBs enhancements to
HUBzero
Blue blocks

6. WHY CLOUD ENABLE ?
Easily scalable
Increasingly adopted
by academia and
industry
Automatable,
repeatable solutions
even for lay-users
Offload physical
resource
maintenance,
provisioning to cloud
service provider
Software packages as
a byproduct, enabling
manual installation

7. MOVING GABBS TO THE CLOUD
GPU Render Server
• OSGEarth
rendering software
• Default base maps
Data Storage Server
• iRODS server software
• PostgreSQL database
• iRODS microservices
 Metadata
extraction
 Hub search
indexing
 Geospatial
preview
• GeoServer map server
HUBzero Web Host
• GABBs-HUBzero
integration software
• GABBs hub tools/libraries
and dependencies
 MultiSpec
 pyMapLib
 GeoBuilder
• iRODS FUSE mount
Hub server DNS/IP
iRODS server
DNS/IP

8. MOVING GABBS TO THE CLOUD
• Utilize Amazon Web Services (AWS)
 Vanilla HUBzero already available on AWS
• Provision other compute/storage servers
 iRODS + GeoServer
 Render server with appropriate hardware
• Setup installation/configuration of necessary
software and server interconnects

9. SOME AWS CONSIDERATIONS
• Each server (EC2 instance) requires a base machine
image (AMI)
• AMIs cannot contain hardcoded administrator
usernames, passwords
• Network security considerations
 Servers require unrestricted traffic between them
• Fully automated software installation at boot-up
rather than requiring user interaction

10. AWS CLOUDFORMATION
• Simplifies provisioning of AWS resources
• Automatable software installation &
configuration on boot-up
• User-specified parameters enable dynamic
configuration
• Resource reference enables DNS/IP queries
• Declarative template via JSON/YAML files

11. AWS CLOUDFORMATION TEMPLATE
Parameters: { gabbsUser: {
Default: gabbs,
Description: Username for iRODS connection,
Type: String,
MinLength: 1,
MaxLength: 41,
AllowedPattern : [a-zA-Z0-9]*
},
Resources: {
HUBZERO: {
Type: AWS::EC2::Instance,
Properties: {
ImageId: <ami-hubzero>,
InstanceType: <instance-type>,
SecurityGroups: [gabbsSG]}
},
UserData: {
#!/bin/bash
yum –y install irods-client
/tmp/update-irods-connection.sh
{Fn::GetAtt:[IRODS,PublicDnsName
]}
{Ref: gabbsUser}
…
}
Metadata: {
AWS::CloudFormation::Init: {
config: {
packages: {
rpm: {
repo: <gabbs-repo-url>}}}}
IRODS: {
Type: AWS::EC2::Instance,
Properties: {
ImageId: <ami-centos>
…
Reference user
parameter
Required
properties
Software
installation/configuration
shell script run on
boot-up
Automatic package
installation
via cloud-init
Reference
resource
property

12. CLOUDFORMATION ISSUES
INTERLEAVED DEPLOYMENT AND CONFIGURATION
HUBZERO: {
UserData: {
#!/bin/bash
/tmp/update-irods-connection.sh
{Fn::GetAtt:[IRODS,PublicDnsName]}
IRODS: {
UserData: {
#!/bin/bash
/tmp/update-hub-solr-connection.sh
{Fn::GetAtt:[HUBZERO,PublicDnsName]
}
• Resource reference forces deployment ordering
• UserData, cloud-init not separable from actual resource
provisioning
• Gives rise to cyclic dependency above
• Resource considered “created” before UserData is done
 HUBzero UserData may begin execution before iRODS server user setup is
done

13. CLOUDFORMATION ISSUES
INTERLEAVED DEPLOYMENT AND CONFIGURATION
• Solving cyclic dependency
 Decide on an ordering (iRODS before HUBzero)
 Configure HUBzero DNS in iRODS later via HUBzero
UserData
• Ensuring UserData completion before next resource
deployment
 Utilize AWS WaitCondition to force resource dependencies
 WaitCondition considered “created” only when signalled
 Signal WaitCondition at end of UserData script

14. CLOUDFORMATION ISSUES
SHARED PARAMETERS
HUBZERO: {
UserData: {
#!/bin/bash
/tmp/update-irods-connection.sh
{Fn::GetAtt:[IRODS,PublicDnsName]}
{"Ref": "gabbsUser"}
IRODS: {
UserData: {
#!/bin/bash
/tmp/add-irods-user.sh
{"Ref": "gabbsUser"}
• HUBzero needs to setup connection to iRODS server, after
iRODS has created a new service user
 Username required during deployment of both servers
• Cannot be hardcoded into AMIs

15. CLOUDFORMATION ISSUES
SHARED PARAMETERS
• Solving shared (dynamic) parameter requirement
 Use CloudFormation parameters
 User prompted to fill in value
 Cannot default to random value (only hardcoded defaults
supported)
 Necessitates explaining (ideally behind-the-scenes)
parameters

16. CONCLUSION & FUTURE WORK
• Current CloudFormation Template:
https://mygeohub.org/groups/gabbs/release
• Employ AutoScaling to scale up render servers and
tool execution servers on demand
• Provide configuration flexibility in CloudFormation
template
• Utilize configuration management tools like Chef,
Puppet to streamline server types and software
installation

17. ACKNOWLEDGEMENTS
This work was supported by the NSF Award ACI - 1261727
CIF21 DIBBs : Integrating Geospatial Capabilities into
HUBzero