Download - Globus Genomics: How Science-as-a-Service is Accelerating Discovery (BDT310) | AWS re:Invent 2013

Science as a Service

Ian Foster, The University of Chicago and Argonne National Laboratory

November 14, 2013

A time of disruptive change

Most labs have limited resources Heidorn: NSF grants in 2007

< $350,000 80% of awards 50% of grant $$

$1,000,000

$100,000

$10,000

$1,000

2000 4000 6000 8000

Automation is required to apply more sophisticated methods to far more data

Automation is required to apply more sophisticated methods to far more data

Outsourcing is needed to achieve economies of scale in the use of automated methods

Building a discovery cloud • Identify time-consuming activities amenable to

automation and outsourcing • Implement as high-quality, low-touch SaaS • Leverage IaaS for reliability,

economies of scale • Extract common elements as

research automation platform Bonus question: Sustainability

Software as a service

Platform as a service

Infrastructure as a service

We aspire (initially) to create a great user experience for

research data management

What would a “dropbox for science” look like?

• Collect • Move • Sync • Share • Analyze

• Annotate • Publish • Search • Backup • Archive

BIG DATA

Registry Staging Store

Ingest Store

Analysis Store

Community Store

Archive Mirror

Ingest Store

Analysis Store

Community Store

Archive Mirror

Registry

Quota exceeded

!

Expired credentials

!

Network failed. Retry.

!

Permission denied

!

It should be trivial to Collect, Move, Sync, Share, Analyze, Annotate, Publish, Search, Backup, & Archive BIG DATA … but in reality it’s often very challenging



BIG DATA



BIG DATA

• Move • Sync • Share Capabilities delivered using

Software-as-Service (SaaS) model

Data Source

Data Destination

User initiates transfer request

1

Globus Online moves/syncs files

2

Globus Online notifies user

3

Data Source

User A selects file(s) to share; selects user/group, sets share permissions

1

Globus Online tracks shared files; no need to move files to cloud storage!

2

User B logs in to Globus Online and accesses

shared file

3

Extreme ease of use • InCommon, Oauth, OpenID, X.509, … • Credential management • Group definition and management • Transfer management and optimization • Reliability via transfer retries • Web interface, REST API, command line • One-click “Globus Connect” install • 5-minute Globus Connect Multi User install

Early adoption is encouraging

Early adoption is encouraging

>12,000 registered users; >150 daily >27 PB moved; >1B files

10x (or better) performance vs. scp 99.9% availability

Entirely hosted on Amazon

Amazon web services used • Amazon EC2 for hosting Globus services • Elastic Load Balancing to use multiple

Availability Zones for reliability and uptime • Amazon S3 to store historical state • Amazon RDS PostgreSQL for active state

K. Heitmann (Argonne) moves 22 TB of cosmology data LANL ANL at 5 Gb/s

B. Winjum (UCLA) moves 900K-file plasma physics datasets UCLA NERSC

Dan Kozak (Caltech) replicates 1 PB LIGO astronomy data for resilience

2

Credit: Kerstin Kleese-van Dam

Erin Miller (PNNL) collects data at Advanced Photon Source, renders at PNNL, and views at ANL



BIG DATA

• Move • Sync • Share Capabilities delivered using

Software-as-Service (SaaS) model



BIG DATA

Globus Online already does a lot

Globus Toolkit

Sharing Service Transfer Service

Globus Nexus (Identity, Group, Profile)

Glo

bus

Onl

ine

API

s

Glo

bus

Con

nect

The identity challenge in science • Research communities often need to

– Assign identities to their users – Manage user profiles – Organize users into groups for authorization

• Obstacles to high-quality implementations – Complexity of associated security protocols – Creation of identity silos – Multiple credentials for users – Reliability, availability, scalability, security

Nexus provides four key capabilities • Identity provisioning

– Create, manage Globus identities

• Identity hub – Link with other identities; use

to authenticate to services

• Group hub – User-managed groups; groups can

be used for authorization

• Profile management – User-managed attributes;

can use in group admission

I

I I I

I

I a b

I U

V G

Key points: 1) Outsource

identity, group, profile management

2) REST API for flexible integration

3) Intuitive, customizable Web interfaces

Branded sites

Open Science Grid University of Chicago XSEDE

DOE kBase Indiana University University of Exeter

Globus Online NERSC NIH BIRN

A platform for integration

Data management SaaS (Globus) + Next-gen sequence analysis pipelines (Galaxy) +

Cloud IaaS (Amazon) = Flexible, scalable, easy-to-use genomics analysis for

all biologists

globus genomics

Globus Toolkit



Glo

bus

Onl

ine

API

s

Glo

bus

Con

nect

We are adding capabilities

Globus Toolkit


Dataset Services


Glo

bus

Onl

ine

API

s

Glo

bus

Con

nect

We are adding capabilities

We are adding capabilities • Ingest and publication

– Imagine a DropBox that not only replicates, but also extracts metadata, catalogs, converts

• Cataloging – Virtual views of data based on user-defined and/or automatically

extracted metadata

• Computation – Associate computational procedures, orchestrate application,

catalog results, record provenance

Next Gen Sequencing Analysis for Everyone – No IT Required

Ravi K Madduri, The University of Chicago and Argonne National Laboratory

November 14, 2013

One slide to get your attention

Outline • Globus Vision • Challenges in Sequencing Analysis

– Big Data Management – Analysis at Scale – Reproducibility

• Proposed Approach Using Globus Genomics • Example Collaborations • Q&A

Globus Vision Goal: Accelerate discovery and innovation worldwide by providing research IT as a service Leverage software-as-a-service to:

– provide millions of researchers with unprecedented access to powerful tools for managing Big Data

– reduce research IT costs dramatically via economies of scale

“Civilization advances by extending the number of important operations which we can perform without thinking of them” —Alfred North Whitehead , 1911

Challenges in Sequencing Analysis

Sequencing Centers

Sequencing Centers

Data Movement and Access Challenges

Manual Data Analysis

Public Data

Storage

Local Cluster/ Cloud Seq

Center

Research Lab

How do we analyze this Sequence Data

Picard

GATK

Fastq Ref Genome

Alignment

Variant Calling

• Manually move the data to the Compute node

(Re)Run Script

Install

Modify

• Install all the tools required for the Analysis • BWA, Picard, GATK, Filtering Scripts, etc. • Shell scripts to sequentially execute the tools

• Manually modify the scripts for any change • Error Prone, difficult to keep track, messy.. • Difficult to maintain and transfer the knowledge

• Data is distributed in different locations • Research labs need access to the data for analysis • Be able to share data with other researchers/collaborators

• Inefficient ways of data movement • Data needs to be available on the local and distributed compute

Resources • Local clusters, cloud, grid and transfer the knowledge

Globus Genomics

Sequencing Centers Sequencing Centers

Public Data

Storage

Local Cluster/ Cloud Seq

Center

Research Lab

Globus Provides a • High-performance • Fault-tolerant • Secure file transfer Service between all data-endpoints

Data Management Data Analysis

Galaxy Data Libraries

• Globus Integrated within Galaxy

• Web-based UI • Drag-Drop workflow

creations • Easily modify

Workflows with new tools

Globus Genomics on Amazon EC2

• Analytical tools are automatically run on the scalable compute resources when possible

Galaxy Based Workflow Management System

Globus Genomics

Globus Genomics Architecture

Figure 2: Globus Genomics Architecture

Globus Genomics Usage

Globus Genomics • Computational profiles for

various analysis tools • Resources can be provisioned

on-demand with Amazon Web Services cloud based infrastructure

• Glusterfs as a shared file system between head nodes and compute nodes

• Provisioned I/O on Amazon EBS

Coming soon! • Integration with Globus Catalog

– Better data discovery and metadata management

• Integration with Globus Sharing – Easy and secure method to share large datasets with collaborators

• Integration with Amazon Glacier for data archiving • Support for high throughput computational

modalities through Apache Mesos – MapReduce and MPI clusters

• Dynamic Storage Strategies using Amazon S3 or LVM-based shared file system

Provide more capability for more people at lower cost by building a “Discovery Cloud”

Delivering “Science as a service”

Our vision for a 21st century discovery infrastructure

Thank you to our sponsors

For more information • More information on Globus Genomics and to

sign up: www.globus.org/genomics • More information on Globus:

www.globusonline.org • Follow us on Twitter: @ianfoster, @madduri,

@globusgenomics, @globusonline

http://www.globus.org/genomics

http://www.globusonline.org

Thank you!

Please give us your feedback on this presentation

As a thank you, we will select prize winners daily for completed surveys!

BDT 310

Download - Globus Genomics: How Science-as-a-Service is Accelerating Discovery (BDT310) | AWS re:Invent 2013

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