SlideShare a Scribd company logo
1 of 32
Running Mixed Workloads on
Kubernetes at IHME
Dr Tyrone Grandison, IHME
Jason Smith, Univa
Your Speakers
Jason Smith
Principal Solutions Architect, Navops
Tyrone Grandison
Chief Information Officer, IHME
Flow
•Introducing the Institute for Health Metrics and
Evaluation (IHME)
•Introducing Univa
•The IHME Environment
•Univa and IHME
Introducing the Institute for Health Metrics and
Evaluation (IHME)
Institute for Health Metrics and Evaluation
• Identity: UW-affiliated, population health-focused research institute.
• Mission: improve the health of the world by
collecting
synthesizing
providing
the world’s best population health data.
• Product: high-quality population health data.
• Other Products: training, visualizations, special analyses.
• Customers: researchers, advocates, policy makers, media, academics.5
IHME Process
6
OutputsAnalyses
Policy
Media
Science
Data
source
Data
source
Data
source
Inputs
High-Quality Population Health Data
• Global Burden of Disease: a systematic, scientific effort to quantify the comparative
magnitude of health loss due to diseases, injuries, and risk factors by age, sex, and
geography over time.
• Global Health Data Exchange: the world’s most comprehensive catalog of public
health data sources.
• Geospatial Analysis: measure all components of the GBD from 1990 to present at the
1 km X 1 km level.
• Forecasting, Scenarios, and Cost-Effectiveness: Develop probabilistic baseline
forecasts of population health, including microsimulations exploring a broad range of
what-if scenarios.
• Special analyses: geographic- or subject-specific projects.
7
Example: Global Burden of Disease 2016
• Billions of points of data
• More than 30.3 TB of data
• More than 3,000 points of metadata
• More than 150,000 data sources
• 335 diseases and injuries
• 1,974 sequelae of disease
• 84 risk factors of disease
• 2,613 cause-risk pairs
• 269 covariates
• 323 locations
• 23 age groups
• 3 sexes
• 26 years
• 36 measures
• 3 metrics
Example: Global Burden of Disease 2016
•GBD Publications
•GBD Reports
•GBD Visualizations and Tools
oMortality Visualization
oCauses of Death Visualization
oEpi Visualization
oGBD Compare
oGBD Data Input Sources Tool
oGBD Results Tool
9
Impacts of Data – Policy
• Collaborators: World Bank, WHO, MDG
Health Alliance, etc.
• Governments: UK, Mexico, China, Saudi
Arabia, Indonesia, Norway, Georgia,
India, Rwanda, etc.
• Examples:
o Public Health England
o China GBD Collaborative Research
Center
o State-level India disease burden
o Data requests daily from more than 72
countries
Introducing Univa
Who is Univa?
Univa is the leading innovator of workload orchestration and
container optimization solutions
• Global reach – based in Chicago with offices in Canada and Germany
• Fast growing enterprise software company
• Support some of the largest clusters in global Fortune 500 companies
Univa Customers
Data Services Energy Gov’t Financial Life Sciences
Manufacturing /
Technology
Navops for Kubernetes
Virtual Multi-
tenancy
Mixed Workloads Manage Cloud
Resources
Application
Workflows
Run Mesos
Frameworks
Share clusters
across teams
and
applications
Run
containerized
and non-
containerized
workloads on
shared
resources
Prioritize
workloads to
efficiently use
on-premises
and cloud
resources
Sequence
workflows to
address job
dependencies
Run
frameworks
seamlessly on a
Kubernetes
cluster
The IHME Environment
IHME Technology Team
Mission:
To enable, empower and engage our partners in improving
public health globally through data and innovative technologies.​
Details:
Sixty-one People across
Infrastructure/DevOps, Data Management, Visualization, Data
Science, Engineering, Workforce Technology Enablement.
IHME Technology Users
• Researchers
o Differing technology backgrounds
o Need to run sophisticated statistical models
o Need to have customized tech stack
• IHME Support Functions (Finance & Planning Operations, Human
Resources & Training, Global Engagement, Executive Support Team)
o Document Management
o Collaboration Management
o Customer Relationship Management
Environment Overview
• HPC nodes: 550
o Intel and AMD
o dev and prod
• Virtual machines: 381
o VMware vSphere
• Containers: 300
o Docker
• Usable Storage: 5.8 PB
o Qumulo clusters
• Tape Storage: 9.2 PB 18
An Intel HPC
Node
56 compute cores
512 GB of memory
800 GB of solid state storage
Hardware
• HPC Cluster
o Primary Modeling:
─ 500x Heterogeneous x86 nodes for ~25k cores, 150TB Memory
o Machine Learning:
─ 4x Nvidia CUDA on Kepler
• Storage Tiers
o Primary ingress & archival (StornextFS)
o VMWare for public facing DB & Web (LSI & Netapp Arrays)
o HPC transform & scratch (Qumulo)
• Fabrics
o 10/40G Ethernet
o Infiniband & Fiberchannel
19
Software
• Primary Modeling
o R-Studio, Shiny, Jupyter, Numpy, Pandas,
Libgeos
o Univa Grid Engine
• Build & Pipelines
o Luigi, Jenkins
• Database
o Percona, MariaDB
• Web
o HTML & home-grown viz frameworks
20
Current Architecture
Production Cluster
21,000 Cores:
Development Cluster
4,000 Cores:
Shared Storage
160 Gb/s 160 Gb/s
End User Web App
CL
The Path to NavOps
•Leverage existing UGE expertise and commitment.
o Researchers have intimate knowledge of UGE
scheduler.
•Maximize use of our environment.
o Ability to re-allocate resource at peak times is
mission-critical.
•Simplify resource management.
o There were too many tools being used.
Univa and IHME
The Solution for IHME – Mixed Workloads
Virtual Multi-
tenancy
Mixed
Workloads
Manage Cloud
Resources
Application
Workflows
Run Mesos
Frameworks
Share clusters
across teams
and
applications
Run
containerized
and non-
containerized
workloads on
shared
resources
Prioritize
workloads to
efficiently use
on-premises
and cloud
resources
Sequence
workflows to
address job
dependencies
Run
frameworks
seamlessly on a
Kubernetes
cluster
Navops Command K8s Integration
Navops Command Architecture
End User Admin
Kubectl Web UI
CLI
REST API Bridge
Container
App
Management
Container
Etcd Container
Kubernetes
API Server
etcd
Backend
App Launcher
REST Svc API
Master Process
Scheduler Thread
Assign pods to nodes
Kubernetes
Objects
Navops Command Pod
Advanced Policies for Kubernetes
Workload Priority
Ranking
• by Application
Profile
• by Resource
Proportional
Shares
Interleaving
• by Application
Profile
• by Resource
Workload Affiliation
Owner Project Application
Profile
Node Selection
Pod Placement
Maximize
Utilization
Pack Spread Mix
Enterprise Workload Policies
Workload Isolation
Runtime
Quotas
Access
Restrictions
Workflow
Management
Pod Dependencies
Navops Proportional Sharing
Mixed Workloads with Navops
Containerized
Application
Containerized
Application
Traditional Batch / Analytic Workloads Containerized Applications
execd execd execd execd execd execd
Mix of application workloads
with dynamic resource sharing
under control of Navops
Command and Kubernetes
Docker containerized
applications – containers,
services, application stacks
Shared IHME On-Premises Kubernetes Cluster
Univa’s Navops
Kubernetes Cluster
Various non-container HPC analytic
workloads – batch, interactive,
parallel, parametric etc.
Grid Engine deployed in pods
as a Kubernetes service
Using Navops Command with Grid Engine, customers can support mixed-
workloads on a shared Kubernetes cluster
Navops Command Delivers
Before: <20% Utilization After: >50% Utilization
Cluster A
MicroServices
Cluster B
MicroServices
Cluster C
Batch
MicroServices
& Batch Workloads
Virtual multi-tenancy Share clusters across teams and
applications
Mixed Workloads Allow batch and microservice applications
to run on shared resources
Management of Resource Scarcity Allow application loads to take advantage
of non peak times for other workloads
Benefits to IHME
•Simplified administration and improved efficiencies by
supporting multiple workloads across a single, shared
environment
•Increased flexibility by providing an easy migration path
for applications that cannot be readily containerized
Thank You!
• Questions? Ask now or ...
• Find us at booth #56
• Visit https://navops.io and https://univa.com
• Contact us at jsmith@univa.com or tgrand@uw.edu

More Related Content

What's hot

2017 bio it world
2017 bio it world2017 bio it world
2017 bio it worldChris Dwan
 
2016 09 cxo forum
2016 09 cxo forum2016 09 cxo forum
2016 09 cxo forumChris Dwan
 
Research methods group accelarating impact by sharing data
Research methods group  accelarating impact by sharing dataResearch methods group  accelarating impact by sharing data
Research methods group accelarating impact by sharing dataWorld Agroforestry (ICRAF)
 
Elixir at de.nbi meeting
Elixir at de.nbi meetingElixir at de.nbi meeting
Elixir at de.nbi meetingNiklas Blomberg
 
Massive-Scale Analytics Applied to Real-World Problems
Massive-Scale Analytics Applied to Real-World ProblemsMassive-Scale Analytics Applied to Real-World Problems
Massive-Scale Analytics Applied to Real-World Problemsinside-BigData.com
 
Data-intensive applications on cloud computing resources: Applications in lif...
Data-intensive applications on cloud computing resources: Applications in lif...Data-intensive applications on cloud computing resources: Applications in lif...
Data-intensive applications on cloud computing resources: Applications in lif...Ola Spjuth
 
2015 09 emc lsug
2015 09 emc lsug2015 09 emc lsug
2015 09 emc lsugChris Dwan
 
Darwin ai covid-net mitre
Darwin ai   covid-net mitreDarwin ai   covid-net mitre
Darwin ai covid-net mitreianmitch
 
Workflow-Driven Geoinformatics Applications and Training in the Big Data Era
Workflow-Driven Geoinformatics Applications and Training in the Big Data EraWorkflow-Driven Geoinformatics Applications and Training in the Big Data Era
Workflow-Driven Geoinformatics Applications and Training in the Big Data EraIlkay Altintas, Ph.D.
 
Imaging dearry ncrdc 11062017
Imaging dearry ncrdc  11062017Imaging dearry ncrdc  11062017
Imaging dearry ncrdc 11062017imgcommcall
 
Big data service architecture: a survey
Big data service architecture: a surveyBig data service architecture: a survey
Big data service architecture: a surveyssuser0191d4
 
The pulse of cloud computing with bioinformatics as an example
The pulse of cloud computing with bioinformatics as an exampleThe pulse of cloud computing with bioinformatics as an example
The pulse of cloud computing with bioinformatics as an exampleEnis Afgan
 
Research Solutions for Education
Research Solutions for EducationResearch Solutions for Education
Research Solutions for EducationLee Stott
 
Chris Armit at IDW2018: Democratising Data Publishing: A Global Perspective
Chris Armit at IDW2018: Democratising Data Publishing: A Global PerspectiveChris Armit at IDW2018: Democratising Data Publishing: A Global Perspective
Chris Armit at IDW2018: Democratising Data Publishing: A Global PerspectiveGigaScience, BGI Hong Kong
 
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityA VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality Paul Courtney
 
Multi-omics methods and resources for Bioconductor
Multi-omics methods and resources for BioconductorMulti-omics methods and resources for Bioconductor
Multi-omics methods and resources for BioconductorLevi Waldron
 

What's hot (16)

2017 bio it world
2017 bio it world2017 bio it world
2017 bio it world
 
2016 09 cxo forum
2016 09 cxo forum2016 09 cxo forum
2016 09 cxo forum
 
Research methods group accelarating impact by sharing data
Research methods group  accelarating impact by sharing dataResearch methods group  accelarating impact by sharing data
Research methods group accelarating impact by sharing data
 
Elixir at de.nbi meeting
Elixir at de.nbi meetingElixir at de.nbi meeting
Elixir at de.nbi meeting
 
Massive-Scale Analytics Applied to Real-World Problems
Massive-Scale Analytics Applied to Real-World ProblemsMassive-Scale Analytics Applied to Real-World Problems
Massive-Scale Analytics Applied to Real-World Problems
 
Data-intensive applications on cloud computing resources: Applications in lif...
Data-intensive applications on cloud computing resources: Applications in lif...Data-intensive applications on cloud computing resources: Applications in lif...
Data-intensive applications on cloud computing resources: Applications in lif...
 
2015 09 emc lsug
2015 09 emc lsug2015 09 emc lsug
2015 09 emc lsug
 
Darwin ai covid-net mitre
Darwin ai   covid-net mitreDarwin ai   covid-net mitre
Darwin ai covid-net mitre
 
Workflow-Driven Geoinformatics Applications and Training in the Big Data Era
Workflow-Driven Geoinformatics Applications and Training in the Big Data EraWorkflow-Driven Geoinformatics Applications and Training in the Big Data Era
Workflow-Driven Geoinformatics Applications and Training in the Big Data Era
 
Imaging dearry ncrdc 11062017
Imaging dearry ncrdc  11062017Imaging dearry ncrdc  11062017
Imaging dearry ncrdc 11062017
 
Big data service architecture: a survey
Big data service architecture: a surveyBig data service architecture: a survey
Big data service architecture: a survey
 
The pulse of cloud computing with bioinformatics as an example
The pulse of cloud computing with bioinformatics as an exampleThe pulse of cloud computing with bioinformatics as an example
The pulse of cloud computing with bioinformatics as an example
 
Research Solutions for Education
Research Solutions for EducationResearch Solutions for Education
Research Solutions for Education
 
Chris Armit at IDW2018: Democratising Data Publishing: A Global Perspective
Chris Armit at IDW2018: Democratising Data Publishing: A Global PerspectiveChris Armit at IDW2018: Democratising Data Publishing: A Global Perspective
Chris Armit at IDW2018: Democratising Data Publishing: A Global Perspective
 
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityA VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
 
Multi-omics methods and resources for Bioconductor
Multi-omics methods and resources for BioconductorMulti-omics methods and resources for Bioconductor
Multi-omics methods and resources for Bioconductor
 

Similar to Running Mixed Workloads on Kubernetes at IHME

Neue Lösungen für Life Sciences und die Pharmaindustrie mit Graphdatenbanken
Neue Lösungen für Life Sciences und die Pharmaindustrie mit GraphdatenbankenNeue Lösungen für Life Sciences und die Pharmaindustrie mit Graphdatenbanken
Neue Lösungen für Life Sciences und die Pharmaindustrie mit GraphdatenbankenNeo4j
 
Data Virtualization Modernizes Biobanking
Data Virtualization Modernizes BiobankingData Virtualization Modernizes Biobanking
Data Virtualization Modernizes BiobankingDenodo
 
H2O for Medicine and Intro to H2O in Python
H2O for Medicine and Intro to H2O in PythonH2O for Medicine and Intro to H2O in Python
H2O for Medicine and Intro to H2O in PythonSri Ambati
 
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
 
Tag.bio aws public jun 08 2021
Tag.bio aws public jun 08 2021 Tag.bio aws public jun 08 2021
Tag.bio aws public jun 08 2021 Sanjay Padhi, Ph.D
 
ELIXIR . Technical Coordinator
ELIXIR. Technical CoordinatorELIXIR. Technical Coordinator
ELIXIR . Technical CoordinatorRafael C. Jimenez
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
 
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne UlitmatumElsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne UlitmatumAnita de Waard
 
General Introduction to the Oxford e-Research Centre
General Introduction to the Oxford e-Research CentreGeneral Introduction to the Oxford e-Research Centre
General Introduction to the Oxford e-Research CentreDavid Wallom
 
Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
Machine Learning in Modern Medicine with Erin LeDell at Stanford MedMachine Learning in Modern Medicine with Erin LeDell at Stanford Med
Machine Learning in Modern Medicine with Erin LeDell at Stanford MedSri Ambati
 
Docker in Open Science Data Analysis Challenges by Bruce Hoff
Docker in Open Science Data Analysis Challenges by Bruce HoffDocker in Open Science Data Analysis Challenges by Bruce Hoff
Docker in Open Science Data Analysis Challenges by Bruce HoffDocker, Inc.
 
Opportunities and Challenges for International Cooperation Around Big Data
Opportunities and Challenges for International Cooperation Around Big DataOpportunities and Challenges for International Cooperation Around Big Data
Opportunities and Challenges for International Cooperation Around Big DataPhilip Bourne
 
Evaluating Cloud vs On-Premises for NGS Clinical Workflows
Evaluating Cloud vs On-Premises for NGS Clinical WorkflowsEvaluating Cloud vs On-Premises for NGS Clinical Workflows
Evaluating Cloud vs On-Premises for NGS Clinical WorkflowsGolden Helix
 
SGCI - The Science Gateways Community Institute: International Collaboration ...
SGCI - The Science Gateways Community Institute: International Collaboration ...SGCI - The Science Gateways Community Institute: International Collaboration ...
SGCI - The Science Gateways Community Institute: International Collaboration ...Sandra Gesing
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
 
Cave health
Cave health Cave health
Cave health polla1
 
ChemSpider – disseminating data and enabling an abundance of chemistry platforms
ChemSpider – disseminating data and enabling an abundance of chemistry platformsChemSpider – disseminating data and enabling an abundance of chemistry platforms
ChemSpider – disseminating data and enabling an abundance of chemistry platformsKen Karapetyan
 
Big data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at KitwareBig data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at Kitwarebigdataviz_bay
 

Similar to Running Mixed Workloads on Kubernetes at IHME (20)

Neue Lösungen für Life Sciences und die Pharmaindustrie mit Graphdatenbanken
Neue Lösungen für Life Sciences und die Pharmaindustrie mit GraphdatenbankenNeue Lösungen für Life Sciences und die Pharmaindustrie mit Graphdatenbanken
Neue Lösungen für Life Sciences und die Pharmaindustrie mit Graphdatenbanken
 
Data Virtualization Modernizes Biobanking
Data Virtualization Modernizes BiobankingData Virtualization Modernizes Biobanking
Data Virtualization Modernizes Biobanking
 
H2O for Medicine and Intro to H2O in Python
H2O for Medicine and Intro to H2O in PythonH2O for Medicine and Intro to H2O in Python
H2O for Medicine and Intro to H2O in Python
 
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...
 
Tag.bio aws public jun 08 2021
Tag.bio aws public jun 08 2021 Tag.bio aws public jun 08 2021
Tag.bio aws public jun 08 2021
 
ELIXIR . Technical Coordinator
ELIXIR. Technical CoordinatorELIXIR. Technical Coordinator
ELIXIR . Technical Coordinator
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health System
 
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne UlitmatumElsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
Elsevier‘s RDM Program: Habits of Effective Data and the Bourne Ulitmatum
 
General Introduction to the Oxford e-Research Centre
General Introduction to the Oxford e-Research CentreGeneral Introduction to the Oxford e-Research Centre
General Introduction to the Oxford e-Research Centre
 
Challenges in medical imaging and the VISCERAL model
Challenges in medical imaging and the VISCERAL modelChallenges in medical imaging and the VISCERAL model
Challenges in medical imaging and the VISCERAL model
 
Cri big data
Cri big dataCri big data
Cri big data
 
Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
Machine Learning in Modern Medicine with Erin LeDell at Stanford MedMachine Learning in Modern Medicine with Erin LeDell at Stanford Med
Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
 
Docker in Open Science Data Analysis Challenges by Bruce Hoff
Docker in Open Science Data Analysis Challenges by Bruce HoffDocker in Open Science Data Analysis Challenges by Bruce Hoff
Docker in Open Science Data Analysis Challenges by Bruce Hoff
 
Opportunities and Challenges for International Cooperation Around Big Data
Opportunities and Challenges for International Cooperation Around Big DataOpportunities and Challenges for International Cooperation Around Big Data
Opportunities and Challenges for International Cooperation Around Big Data
 
Evaluating Cloud vs On-Premises for NGS Clinical Workflows
Evaluating Cloud vs On-Premises for NGS Clinical WorkflowsEvaluating Cloud vs On-Premises for NGS Clinical Workflows
Evaluating Cloud vs On-Premises for NGS Clinical Workflows
 
SGCI - The Science Gateways Community Institute: International Collaboration ...
SGCI - The Science Gateways Community Institute: International Collaboration ...SGCI - The Science Gateways Community Institute: International Collaboration ...
SGCI - The Science Gateways Community Institute: International Collaboration ...
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health System
 
Cave health
Cave health Cave health
Cave health
 
ChemSpider – disseminating data and enabling an abundance of chemistry platforms
ChemSpider – disseminating data and enabling an abundance of chemistry platformsChemSpider – disseminating data and enabling an abundance of chemistry platforms
ChemSpider – disseminating data and enabling an abundance of chemistry platforms
 
Big data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at KitwareBig data visualization frameworks and applications at Kitware
Big data visualization frameworks and applications at Kitware
 

More from Tyrone Grandison

Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...
Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...
Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...Tyrone Grandison
 
Learning From the COViD-19 Global Pandemic
Learning From the COViD-19 Global PandemicLearning From the COViD-19 Global Pandemic
Learning From the COViD-19 Global PandemicTyrone Grandison
 
Systemic Barriers in Technology: Striving for Equity and Access
Systemic Barriers in Technology: Striving for Equity and AccessSystemic Barriers in Technology: Striving for Equity and Access
Systemic Barriers in Technology: Striving for Equity and AccessTyrone Grandison
 
Are There Ethical Limits to What Science Can Achieve or Should Pursue?
Are There Ethical Limits to What Science Can Achieve or Should Pursue?Are There Ethical Limits to What Science Can Achieve or Should Pursue?
Are There Ethical Limits to What Science Can Achieve or Should Pursue?Tyrone Grandison
 
Using Data and Computing for the Greater Good
Using Data and Computing for the Greater GoodUsing Data and Computing for the Greater Good
Using Data and Computing for the Greater GoodTyrone Grandison
 
How to effectively collaborate with your IT Departments to Develop Secure IA ...
How to effectively collaborate with your IT Departments to Develop Secure IA ...How to effectively collaborate with your IT Departments to Develop Secure IA ...
How to effectively collaborate with your IT Departments to Develop Secure IA ...Tyrone Grandison
 
DOES innovation Lab Launch
DOES innovation Lab LaunchDOES innovation Lab Launch
DOES innovation Lab LaunchTyrone Grandison
 
Creating Chandler's IT Strategic Plan
Creating Chandler's IT Strategic PlanCreating Chandler's IT Strategic Plan
Creating Chandler's IT Strategic PlanTyrone Grandison
 
Inventing with Purpose, Intention and Focus
Inventing with Purpose, Intention and FocusInventing with Purpose, Intention and Focus
Inventing with Purpose, Intention and FocusTyrone Grandison
 
Becoming a Nation of Innovation
Becoming a Nation of InnovationBecoming a Nation of Innovation
Becoming a Nation of InnovationTyrone Grandison
 
ISPAB Presentation - The Commerce Data Service
ISPAB Presentation - The Commerce Data ServiceISPAB Presentation - The Commerce Data Service
ISPAB Presentation - The Commerce Data ServiceTyrone Grandison
 
Building APIs in Government for Social Good
Building APIs in Government for Social GoodBuilding APIs in Government for Social Good
Building APIs in Government for Social GoodTyrone Grandison
 
Strategies and Tactics for Accelerating IT Modernization
Strategies and Tactics for Accelerating IT ModernizationStrategies and Tactics for Accelerating IT Modernization
Strategies and Tactics for Accelerating IT ModernizationTyrone Grandison
 
The Creative Economy within the United States of America
The Creative Economy within the United States of AmericaThe Creative Economy within the United States of America
The Creative Economy within the United States of AmericaTyrone Grandison
 
Enabling Data-Driven Private-Public Collaborations
Enabling Data-Driven Private-Public CollaborationsEnabling Data-Driven Private-Public Collaborations
Enabling Data-Driven Private-Public CollaborationsTyrone Grandison
 
Creating a Data-Driven Government: Big Data With Purpose
Creating a Data-Driven Government: Big Data With PurposeCreating a Data-Driven Government: Big Data With Purpose
Creating a Data-Driven Government: Big Data With PurposeTyrone Grandison
 
Security and Privacy in Healthcare
Security and Privacy in HealthcareSecurity and Privacy in Healthcare
Security and Privacy in HealthcareTyrone Grandison
 
Publishing in Biomedical Data Science
Publishing in Biomedical Data SciencePublishing in Biomedical Data Science
Publishing in Biomedical Data ScienceTyrone Grandison
 

More from Tyrone Grandison (20)

Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...
Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...
Global Scientific Research as a Tool to Unlock and Engage Talent and Expand t...
 
Learning From the COViD-19 Global Pandemic
Learning From the COViD-19 Global PandemicLearning From the COViD-19 Global Pandemic
Learning From the COViD-19 Global Pandemic
 
Systemic Barriers in Technology: Striving for Equity and Access
Systemic Barriers in Technology: Striving for Equity and AccessSystemic Barriers in Technology: Striving for Equity and Access
Systemic Barriers in Technology: Striving for Equity and Access
 
COVID and the Ederly
COVID and the EderlyCOVID and the Ederly
COVID and the Ederly
 
Are There Ethical Limits to What Science Can Achieve or Should Pursue?
Are There Ethical Limits to What Science Can Achieve or Should Pursue?Are There Ethical Limits to What Science Can Achieve or Should Pursue?
Are There Ethical Limits to What Science Can Achieve or Should Pursue?
 
Using Data and Computing for the Greater Good
Using Data and Computing for the Greater GoodUsing Data and Computing for the Greater Good
Using Data and Computing for the Greater Good
 
How to effectively collaborate with your IT Departments to Develop Secure IA ...
How to effectively collaborate with your IT Departments to Develop Secure IA ...How to effectively collaborate with your IT Departments to Develop Secure IA ...
How to effectively collaborate with your IT Departments to Develop Secure IA ...
 
DOES innovation Lab Launch
DOES innovation Lab LaunchDOES innovation Lab Launch
DOES innovation Lab Launch
 
Creating Chandler's IT Strategic Plan
Creating Chandler's IT Strategic PlanCreating Chandler's IT Strategic Plan
Creating Chandler's IT Strategic Plan
 
Inventing with Purpose, Intention and Focus
Inventing with Purpose, Intention and FocusInventing with Purpose, Intention and Focus
Inventing with Purpose, Intention and Focus
 
Becoming a Nation of Innovation
Becoming a Nation of InnovationBecoming a Nation of Innovation
Becoming a Nation of Innovation
 
The Power Of Open
The Power Of OpenThe Power Of Open
The Power Of Open
 
ISPAB Presentation - The Commerce Data Service
ISPAB Presentation - The Commerce Data ServiceISPAB Presentation - The Commerce Data Service
ISPAB Presentation - The Commerce Data Service
 
Building APIs in Government for Social Good
Building APIs in Government for Social GoodBuilding APIs in Government for Social Good
Building APIs in Government for Social Good
 
Strategies and Tactics for Accelerating IT Modernization
Strategies and Tactics for Accelerating IT ModernizationStrategies and Tactics for Accelerating IT Modernization
Strategies and Tactics for Accelerating IT Modernization
 
The Creative Economy within the United States of America
The Creative Economy within the United States of AmericaThe Creative Economy within the United States of America
The Creative Economy within the United States of America
 
Enabling Data-Driven Private-Public Collaborations
Enabling Data-Driven Private-Public CollaborationsEnabling Data-Driven Private-Public Collaborations
Enabling Data-Driven Private-Public Collaborations
 
Creating a Data-Driven Government: Big Data With Purpose
Creating a Data-Driven Government: Big Data With PurposeCreating a Data-Driven Government: Big Data With Purpose
Creating a Data-Driven Government: Big Data With Purpose
 
Security and Privacy in Healthcare
Security and Privacy in HealthcareSecurity and Privacy in Healthcare
Security and Privacy in Healthcare
 
Publishing in Biomedical Data Science
Publishing in Biomedical Data SciencePublishing in Biomedical Data Science
Publishing in Biomedical Data Science
 

Recently uploaded

Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 

Recently uploaded (20)

Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 

Running Mixed Workloads on Kubernetes at IHME

  • 1. Running Mixed Workloads on Kubernetes at IHME Dr Tyrone Grandison, IHME Jason Smith, Univa
  • 2. Your Speakers Jason Smith Principal Solutions Architect, Navops Tyrone Grandison Chief Information Officer, IHME
  • 3. Flow •Introducing the Institute for Health Metrics and Evaluation (IHME) •Introducing Univa •The IHME Environment •Univa and IHME
  • 4. Introducing the Institute for Health Metrics and Evaluation (IHME)
  • 5. Institute for Health Metrics and Evaluation • Identity: UW-affiliated, population health-focused research institute. • Mission: improve the health of the world by collecting synthesizing providing the world’s best population health data. • Product: high-quality population health data. • Other Products: training, visualizations, special analyses. • Customers: researchers, advocates, policy makers, media, academics.5
  • 7. High-Quality Population Health Data • Global Burden of Disease: a systematic, scientific effort to quantify the comparative magnitude of health loss due to diseases, injuries, and risk factors by age, sex, and geography over time. • Global Health Data Exchange: the world’s most comprehensive catalog of public health data sources. • Geospatial Analysis: measure all components of the GBD from 1990 to present at the 1 km X 1 km level. • Forecasting, Scenarios, and Cost-Effectiveness: Develop probabilistic baseline forecasts of population health, including microsimulations exploring a broad range of what-if scenarios. • Special analyses: geographic- or subject-specific projects. 7
  • 8. Example: Global Burden of Disease 2016 • Billions of points of data • More than 30.3 TB of data • More than 3,000 points of metadata • More than 150,000 data sources • 335 diseases and injuries • 1,974 sequelae of disease • 84 risk factors of disease • 2,613 cause-risk pairs • 269 covariates • 323 locations • 23 age groups • 3 sexes • 26 years • 36 measures • 3 metrics
  • 9. Example: Global Burden of Disease 2016 •GBD Publications •GBD Reports •GBD Visualizations and Tools oMortality Visualization oCauses of Death Visualization oEpi Visualization oGBD Compare oGBD Data Input Sources Tool oGBD Results Tool 9
  • 10. Impacts of Data – Policy • Collaborators: World Bank, WHO, MDG Health Alliance, etc. • Governments: UK, Mexico, China, Saudi Arabia, Indonesia, Norway, Georgia, India, Rwanda, etc. • Examples: o Public Health England o China GBD Collaborative Research Center o State-level India disease burden o Data requests daily from more than 72 countries
  • 12. Who is Univa? Univa is the leading innovator of workload orchestration and container optimization solutions • Global reach – based in Chicago with offices in Canada and Germany • Fast growing enterprise software company • Support some of the largest clusters in global Fortune 500 companies
  • 13. Univa Customers Data Services Energy Gov’t Financial Life Sciences Manufacturing / Technology
  • 14. Navops for Kubernetes Virtual Multi- tenancy Mixed Workloads Manage Cloud Resources Application Workflows Run Mesos Frameworks Share clusters across teams and applications Run containerized and non- containerized workloads on shared resources Prioritize workloads to efficiently use on-premises and cloud resources Sequence workflows to address job dependencies Run frameworks seamlessly on a Kubernetes cluster
  • 16. IHME Technology Team Mission: To enable, empower and engage our partners in improving public health globally through data and innovative technologies.​ Details: Sixty-one People across Infrastructure/DevOps, Data Management, Visualization, Data Science, Engineering, Workforce Technology Enablement.
  • 17. IHME Technology Users • Researchers o Differing technology backgrounds o Need to run sophisticated statistical models o Need to have customized tech stack • IHME Support Functions (Finance & Planning Operations, Human Resources & Training, Global Engagement, Executive Support Team) o Document Management o Collaboration Management o Customer Relationship Management
  • 18. Environment Overview • HPC nodes: 550 o Intel and AMD o dev and prod • Virtual machines: 381 o VMware vSphere • Containers: 300 o Docker • Usable Storage: 5.8 PB o Qumulo clusters • Tape Storage: 9.2 PB 18 An Intel HPC Node 56 compute cores 512 GB of memory 800 GB of solid state storage
  • 19. Hardware • HPC Cluster o Primary Modeling: ─ 500x Heterogeneous x86 nodes for ~25k cores, 150TB Memory o Machine Learning: ─ 4x Nvidia CUDA on Kepler • Storage Tiers o Primary ingress & archival (StornextFS) o VMWare for public facing DB & Web (LSI & Netapp Arrays) o HPC transform & scratch (Qumulo) • Fabrics o 10/40G Ethernet o Infiniband & Fiberchannel 19
  • 20. Software • Primary Modeling o R-Studio, Shiny, Jupyter, Numpy, Pandas, Libgeos o Univa Grid Engine • Build & Pipelines o Luigi, Jenkins • Database o Percona, MariaDB • Web o HTML & home-grown viz frameworks 20
  • 21. Current Architecture Production Cluster 21,000 Cores: Development Cluster 4,000 Cores: Shared Storage 160 Gb/s 160 Gb/s End User Web App CL
  • 22. The Path to NavOps •Leverage existing UGE expertise and commitment. o Researchers have intimate knowledge of UGE scheduler. •Maximize use of our environment. o Ability to re-allocate resource at peak times is mission-critical. •Simplify resource management. o There were too many tools being used.
  • 24. The Solution for IHME – Mixed Workloads Virtual Multi- tenancy Mixed Workloads Manage Cloud Resources Application Workflows Run Mesos Frameworks Share clusters across teams and applications Run containerized and non- containerized workloads on shared resources Prioritize workloads to efficiently use on-premises and cloud resources Sequence workflows to address job dependencies Run frameworks seamlessly on a Kubernetes cluster
  • 25. Navops Command K8s Integration
  • 26. Navops Command Architecture End User Admin Kubectl Web UI CLI REST API Bridge Container App Management Container Etcd Container Kubernetes API Server etcd Backend App Launcher REST Svc API Master Process Scheduler Thread Assign pods to nodes Kubernetes Objects Navops Command Pod
  • 27. Advanced Policies for Kubernetes Workload Priority Ranking • by Application Profile • by Resource Proportional Shares Interleaving • by Application Profile • by Resource Workload Affiliation Owner Project Application Profile Node Selection Pod Placement Maximize Utilization Pack Spread Mix Enterprise Workload Policies Workload Isolation Runtime Quotas Access Restrictions Workflow Management Pod Dependencies
  • 29. Mixed Workloads with Navops Containerized Application Containerized Application Traditional Batch / Analytic Workloads Containerized Applications execd execd execd execd execd execd Mix of application workloads with dynamic resource sharing under control of Navops Command and Kubernetes Docker containerized applications – containers, services, application stacks Shared IHME On-Premises Kubernetes Cluster Univa’s Navops Kubernetes Cluster Various non-container HPC analytic workloads – batch, interactive, parallel, parametric etc. Grid Engine deployed in pods as a Kubernetes service Using Navops Command with Grid Engine, customers can support mixed- workloads on a shared Kubernetes cluster
  • 30. Navops Command Delivers Before: <20% Utilization After: >50% Utilization Cluster A MicroServices Cluster B MicroServices Cluster C Batch MicroServices & Batch Workloads Virtual multi-tenancy Share clusters across teams and applications Mixed Workloads Allow batch and microservice applications to run on shared resources Management of Resource Scarcity Allow application loads to take advantage of non peak times for other workloads
  • 31. Benefits to IHME •Simplified administration and improved efficiencies by supporting multiple workloads across a single, shared environment •Increased flexibility by providing an easy migration path for applications that cannot be readily containerized
  • 32. Thank You! • Questions? Ask now or ... • Find us at booth #56 • Visit https://navops.io and https://univa.com • Contact us at jsmith@univa.com or tgrand@uw.edu

Editor's Notes

  1. First two sections are intros to company End with solution for IHME IHME Env will be Ty Then Benefits
  2. Day-to-day support
  3. Day-to-day support
  4. Day-to-day support