March 23-24, 2017

Doing It Right

SYMPOSIUM

The IBM Reference Architecture for

Healthcare and Life Sciences

Janis Landry-LaneIBM Systems Group

World Wide Program Directorjanisll@us.ibm.com

Big Data Symposium – March 24, 2017

The Era of Genomics Represents “BIG DATA”

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A New Era of Precision Healthcare

Green, ED et al (2011). Charting a course for genomic medicine from base pairs to bedside. Nature 470: 204-213

Key Client Interests

Clinical GenomicsWhat does my patient’s genomic information tell me about the treatment I should select?

Completion of the Human Genome Project in 2003 led to an expansion of research on the contributions of genomics in disease diagnosis, treatment, and prevention

Early DiscoveryWhat biological or environmental factors are causing disease? Can we design diagnostics and drugs to improve patient outcomes?

University Research / Pharmaceutical R&D Hospital Systems

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Key Technical IT Challenges

Big Data Symposium – March 24, 2017

Data Silos

Complex Workload

Big Data

…

Evolving Frameworks & Databases

……

International Collaboration

4

…

Enterprise Data Management

Disk TapeFlash POWERx86

Workload Orchestration

Industry Applications

IT Administration

VM

Data Repositories and Databases

…

Applications & Frameworks

Compute & Storage Servers

Optimize utilization of compute resources across the enterprise

Improve data access and optimize storage utilization across the enterprise

Software-DefinedInfrastructure

On / Off premises – Hybrid Cloud

Reference Architecture for Healthcare & Life Science Analytics

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IBM Spectrum Computing

POSIX

Disk TapeFlash POWERx86

Image AnalysisGenomic Analysis Cognitive AnalyticsClinical Informatics

IT Administration

IBM Spectrum StorageInformation Lifecycle Management High-Performance I/O Data Sharing

Workflow Admin Workload MonitoringResource Allocation Cluster ProvisioningMetadata Collection

Metadata Collection

EMR

EDW / UDMH

LIMS CPOE

Omics DW VNA Knowledge Base

NGS Ref Databases Imaging / PACS RIS Literature Ontologies

- - - -

HDFS POSIObject

VM

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- ---… … …-

On / Off premises – Hybrid Cloud

Enterprise Data Management

Workload Orchestration

Big Data Repository

Sample Workloads

Workload Management

Enterprise Data

Management

Compute & Storage Servers

Reference Architecture for Healthcare & Life Science Analytics

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On-premise infrastructure

Spectrum Scale (GPFS)

Secure VPN tunnel Cloud Resident Cluster

Spectrum Scale (GPFS)

…

IBM Aspera FASP

Spectrum Scale AFM AFMIBM Elastic Storage

…

Cloud infrastructure

IBM Elastic Storage

On-Premise Cluster Spectrum LSFWorkloads

IBM designs a hybrid cloud architecture that supports seamless communication of workflows across on-premise and cloud environments

A Hybrid Cloud Architecture

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IBM Products Offer Flexibility for Customers

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Platform Symphony Scheduler

App1 App2

Spectrum Conductor for Spark

Job 1 Job 2

Hadoop MapReduce Apps

Spectrum LSF Scheduler

Job 1 Job 2

MPI/BatchApplications

Spectrum Conductor for Spark

App1 App2

IBM Platform Computing - Resource Orchestration and Monitoring

Single File System for POSIX, NFS, HDFS access for efficient data Sharing

IBM Spectrum Scale File System / Data Store ConnectorsPOSIX NFS HDFS Object

Storage rich servers TapeFlash Disk

Single platform for workload management forautomated resource sharing

Platform Process ManagerWorkflows/Pipelines

Spark ApplicationsPlatform SymphonyApplications

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IBM provided: Spectrum Scale for performance, data management, and resiliencySpectrum Archive to support movement of 1.2 PB per month to tapeA robust scheduler that supports multi-thread processing stream to

accelerate compute and can efficiently process unpredictable data streams

An overall lowest cost of managing 10’s of PB of online data and an archive with site diversity

Mission: Deliver analysis to support personalized treatment to individual cancer patients where the Standard of Care has failedRequirements: a data architecture that provides Management, Resiliency, Scalability, Economics, and Long Term Retention

Case Study #1: Major Genomics Provider in NY:

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New Project Demands• Add 2 Spectrum Archive• Add 2 Spectrum Scale• Add TS4500 Exp frame• Add 8 TS1150• Add V7000 +SSD for

Metadata in Cluster• 3PB Peak

Throughput/mo

The Timeline: Major Genomics Center in NY:

010002000300040005000600070008000

Month 1 Month 4 Month 11 Month 21 Month 24

Site Diversity

Ingest RateTB/Month

Initial Assessment• 3 Spectrum

Archive• 3 Spectrum

Scale• TS4500/V7000• 6 TS1150• Inc Air Gap

Security (one tape copy)

Revised Assessment• Add 6 TS1150• Add V7000 disk

Organic Growth• No

change

Mission Critical (Planned)• System critical production operation• Site diversity• Second site is at a related institution, with high bandwidth connectivity (Internet 2)

Variety of All Data Sources Not Known & Growth

Unpredictable

Volume of Data Known & Growth Predictable

Value of Data Known

(TB)Infrastructure ScalesQuickly at Low $ Add

Infrastructure ScalesIncrementally for

Planned Growth

DR Extension

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Case Study #2: Alberta Children’s Hospital Research Inst.

Mission: Provide Precision Medicine for a variety of childhood diseases, including ”Care for Rare” consortium. Make available a robust platform for new discovery and model organisms. Requirements: A cost-effective, scalable platform for supporting the “breaking down of silos”

IBM provided: Spectrum Scale for performance, data management, and resiliency with an archive Compute enhancements to support projects with the ability to aggregate data into a data model for advanced analytics A Global namespace that provided the ability to “break down silos” and share amongst many research groups A robust scheduler that supports both existing as well as additional compute paradigms An overall architecture with the ability to add incrementally.

https://www.ibm.com/news/ca/en/2016/06/15/u599716r24585k82.html

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Mission: To achieve the most effective solution for genomic workloads without re-architecting the industry-standard software, we performed a rigorous analysis of usage statistics, benchmarks and available technologies to design a system for maximum throughput. IBM provided:

Spectrum Scale for performance, data management, and resiliency. IBM Flash tier supported by Spectrum Scale for optimization of workflow performance with this tier of SCRATCH to support small file sizes A robust scheduler that could handle 700,000 jobs in the queue Data management to move data from the Flash tier to disk as soon as the workflow completes A Global namespace to provide support for a large and growing faculty/staff user baseAn overall architecture with the ability to add incrementally.

https://hpc.mssm.edu/files/SC15-BODE-paper.pdf

Case Study #3: Mount Sinai School of Medicine

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Lessons Learned:

Customer Success is derived from: Managing explosive growth. Managing application performance– especially with clinical diagnostics and patients for whom the standard of care has failed A robust Data management/metadata search system that allows scientists to retrieve past data is a must as they will ultimately update their research with new algorithms or the data required for reproducible results. A Global namespace that breaks down silos, and provides a single copy of the data. A robust scheduler that supports both existing as well as additional compute paradigms is essential to enable sharing of a single IT environment with a myriad of applications

APPLICATIONS, they come and go, but ARCHITECTURE ENDURES……………

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