Impact of Cloud Computing: Life Sciences

Slide 1

Slide 1 text

IMPACT The of Cloud Computing a presentation by DR. MATT WOOD

Slide 2

Slide 2 text

THANK YOU Hello, and

Slide 3

Slide 3 text

INTRO WHY CLOUD COMPUTING? LIFE SCIENCES IN THE CLOUD IMPACT ON LIFE SCIENCES

Slide 4

Slide 4 text

SEVEN years young

Slide 5

Slide 5 text

No content

Slide 6

Slide 6 text

SERVICES to support virtually any workload Broad and deep

Slide 7

Slide 7 text

2007 2008 2009 2010 2011 2012 159 82 61 48 24 9

Slide 8

Slide 8 text

SECURITY capabilities to support virtually any workload Comprehensive

Slide 9

Slide 9 text

No content

Slide 10

Slide 10 text

No content

Slide 11

Slide 11 text

EVERY DAY to power amazon.com in 2003 Add enough server capacity

Slide 12

Slide 12 text

UTILITY Computing delivered as a

Slide 13

Slide 13 text

No content

Slide 14

Slide 14 text

No content

Slide 15

Slide 15 text

ECONOMIES of scale to lower prices Take advantage of the

Slide 16

Slide 16 text

Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012 Q1 2013 2 TRILLION OBJECTS

Slide 17

Slide 17 text

5/22/2010 6/12/2010 7/3/2010 7/24/2010 8/14/2010 9/4/2010 9/25/2010 10/16/2010 11/6/2010 11/27/2010 12/18/2010 1/8/2011 1/29/2011 2/19/2011 3/12/2011 4/2/2011 4/23/2011 5/14/2011 6/4/2011 6/25/2011 7/16/2011 8/6/2011 8/27/2011 9/17/2011 10/8/2011 10/29/2011 11/19/2011 12/10/2011 12/31/2011 1/21/2012 2/11/2012 3/3/2012 3/24/2012 4/14/2012 5/5/2012 5/26/2012 6/16/2012 7/7/2012 7/28/2012 8/18/2012 9/8/2012 9/29/2012 10/20/2012 11/10/2012 12/01/2012 12/22/2012 1/12/2013 2/2/2013 2/23/2013 3/16/2013 4/6/2013 5.5 MILLION HADOOP CLUSTERS

Slide 18

Slide 18 text

FLEXIBILITY in using these resources Provide

Slide 19

Slide 19 text

Free steak campaign Facebook page Mars exploration ops Consumer social app Ticket pricing optimization SAP & Sharepoint Securities Trading Data Archiving Marketing web site Interactive TV apps Financial markets analytics Consumer social app Big data analytics Web site & media sharing Disaster recovery Media streaming Web and mobile apps Streaming webcasts Facebook app Consumer social app Business line of sight Mobile analytics IT operations Digital media Core IT and media Ground campaign

Slide 20

Slide 20 text

ADOPTING cloud computing? Why are customers

Slide 21

Slide 21 text

CAPITAL EXPENSE for variable expense Trade

Slide 22

Slide 22 text

$0 vs TO GET STARTED

Slide 23

Slide 23 text

VARIABLE EXPENSE than companies can do themselves Lower

Slide 24

Slide 24 text

On-premises AWS $3.01M $0.90M Source: IDC Whitepaper, sponsored by Amazon, “The Business Value of Amazon Web Services Accelerates Over Time.” July 2012 70% LOWER 5 YEAR TCO PER APP

Slide 25

Slide 25 text

GUESS capacity You don’t need to

Slide 26

Slide 26 text

TIME DEMAND

Slide 27

Slide 27 text

TIME PREDICTED DEMAND

Slide 28

Slide 28 text

DEMAND TIME ACTUAL PREDICTED

Slide 29

Slide 29 text

TIME WASTE ACTUAL DEMAND PREDICTED

Slide 30

Slide 30 text

TIME WASTE OPPORTUNITY COST ACTUAL DEMAND PREDICTED

Slide 31

Slide 31 text

TIME ACTUAL DEMAND AWS

Slide 32

Slide 32 text

Sunday Monday Tuesday Wednesday Thursday Friday Saturday Weekly traffic to amazon.com

Slide 33

Slide 33 text

Sunday Monday Tuesday Wednesday Thursday Friday Saturday Weekly traffic to amazon.com

Slide 34

Slide 34 text

Sunday Monday Tuesday Wednesday Thursday Friday Saturday 39% 61% Weekly traffic to amazon.com

Slide 35

Slide 35 text

November traffic to amazon.com

Slide 36

Slide 36 text

76% 24% November traffic to amazon.com

Slide 37

Slide 37 text

SPEED and agility Dramatically increase

Slide 38

Slide 38 text

WEEKS MINUTES to

Slide 39

Slide 39 text

EXPERIMENTATION is fast and low risk Increase innovation when

Slide 40

Slide 40 text

UNDIFFERENTIATED heavy lifting Stop spending money on

Slide 41

Slide 41 text

data centers power cooling cabling networking racks servers storage labor

Slide 42

Slide 42 text

GLOBAL in minutes Go

Slide 43

Slide 43 text

OPEX Replace CapEx with COST Lower overall AGILITY Speed and CAPACITY Don’t guess FOCUS Enhance GLOBAL Go

Slide 44

Slide 44 text

LIFE SCIENCES in the cloud? What are the drivers for

Slide 45

Slide 45 text

DATA generation Decreasing cost of

Slide 46

Slide 46 text

GENERATION COLLECTION & STORAGE ANALYTICS & COMPUTATION COLLABORATION & SHARING

Slide 47

Slide 47 text

GENERATION COLLECTION & STORAGE ANALYTICS & COMPUTATION COLLABORATION & SHARING Lower costs, higher throughput

Slide 48

Slide 48 text

GENERATION COLLECTION & STORAGE ANALYTICS & COMPUTATION COLLABORATION & SHARING Lower costs, higher throughput Highly constrained

Slide 49

Slide 49 text

1990 2000 2010 2020 The Data Analysis Gap Enterprise Data Data in Warehouse Generated data Available for analysis Data volume Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011 IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares

Slide 50

Slide 50 text

REMOVES resource constraints Utility computing

Slide 51

Slide 51 text

GENERATION COLLECTION & STORAGE ANALYTICS & COMPUTATION COLLABORATION & SHARING Lower costs, higher throughput Highly constrained

Slide 52

Slide 52 text

GENERATION COLLECTION & STORAGE ANALYTICS & COMPUTATION COLLABORATION & SHARING

Slide 53

Slide 53 text

Average daily number of cells that moved out from the communal sections. Linus Bengtsson et al. PLoS Medicine, 2011

Slide 54

Slide 54 text

You Are What You Tweet: Analyzing Twitter for Public Health. M. J. Paul and M. Dredze, 2011 Tweeting about Flu

Slide 55

Slide 55 text

Tweeting about Food

Slide 56

Slide 56 text

Tweets about the price of rice Official food price inflation Tweeting about Food

Slide 57

Slide 57 text

No content

Slide 58

Slide 58 text

No content

Slide 59

Slide 59 text

Chromosome 11 : ACTN3 : rs1815739

Slide 60

Slide 60 text

Chromosome X : rs6625163

Slide 61

Slide 61 text

Chromosome 19 : FUT2 : rs601338

Slide 62

Slide 62 text

Chromosome 2 : rs10427255

Slide 63

Slide 63 text

TYPE II Chromosome 10 : rs7903146

Slide 64

Slide 64 text

+0.25 Chromosome 15 : rs2472297

Slide 65

Slide 65 text

No content

Slide 66

Slide 66 text

GENERATION COLLECTION & STORAGE ANALYTICS & COMPUTATION COLLABORATION & SHARING

Slide 67

Slide 67 text

RATE LIMITING Data generation is no longer

Slide 68

Slide 68 text

ANALYTICS Unconstrained and computation

Slide 69

Slide 69 text

12.5 3 years hours

Slide 70

Slide 70 text

12.5 3 $20M $4k years hours

Slide 71

Slide 71 text

No content

Slide 72

Slide 72 text

$1k Less than today

Slide 73

Slide 73 text

1,000,000+ core hours

Slide 74

Slide 74 text

WAREHOUSING Petabyte scale, managed with high performance

Slide 75

Slide 75 text

WAREHOUSING Petabyte scale, managed with high performance aws.amazon.com/redshift

Slide 76

Slide 76 text

$1000 Less than per terabyte, per year

Slide 77

Slide 77 text

ARCHIVAL High durability

Slide 78

Slide 78 text

99.999999999% $0.01 durability per gigabyte, per month

Slide 79

Slide 79 text

As our company moves into the clinical space, we face a legal requirement to archive patient data for years that would drastically raise the cost of storage. Thanks to Amazon Glacier’s secure and scalable solution, we will be able to provide cost-effective, long-term storage and thereby eliminate a barrier to providing whole genome sequencing for medical treatment of cancer and other genetic diseases. ” “

Slide 80

Slide 80 text

MANAGEMENT and computation challenges Beyond the data

Slide 81

Slide 81 text

SHARING and integration Enable data

Slide 82

Slide 82 text

No content

Slide 83

Slide 83 text

AVAILABLE at no cost Public datasets

Slide 84

Slide 84 text

COMPLIANCE for security and privacy Regulatory

Slide 85

Slide 85 text

SHARED responsibility An environment of

Slide 86

Slide 86 text

HIPAA 21 CFR Part 11 CLIA, GxP and others...

Slide 87

Slide 87 text

CLOUD COMPUTING on life sciences? What is the impact of

Slide 88

Slide 88 text

RESEARCH Accelerating

Slide 89

Slide 89 text

No content

Slide 90

Slide 90 text

No content

Slide 91

Slide 91 text

Apps Public Genomic Databases Users EMR Support & Engineering Instruments

Slide 92

Slide 92 text

No content

Slide 93

Slide 93 text

No content

Slide 94

Slide 94 text

CLINICAL IMPACT Improving

Slide 95

Slide 95 text

Speeding server provisioning for R&D apps Extending capacity for internal grid environments Slowing internally hosted compute infrastructure growth On-boarding security, validation services and compliance Hosting research data Reducing cost while extending capabilities Challenges

Slide 96

Slide 96 text

Clinical pharmacology and pharmacometrics Molecular dynamics Computational genomics Research portfolio Primary uses

Slide 97

Slide 97 text

98% time saved for clinical trial simulations Internal System AWS Individual Clinical Trial Simulation Run Time (Min) 56 56 Total Number of Clinical Trial Simulations 2000 2000 No. Servers 2 256 No. CPU’s 32 2048 Total Analysis Run Time (hr) 60 1.2 Cost ?? $336

Slide 98

Slide 98 text

Reduced burden on pediatric subjects Traditional Design Design Optimized Using Clinical Trial Simulation # of subjects 60 40 # of blood samples per subject 12 5 Length of stay per subject 72 hours 26 hours Length of study 2.5 years 1.7 years Total study cost $700K $250K Length and cost projected based on historical data in pediatric subjects