Cloud Experiences <ul><li>Guy Coates
Wellcome Trust Sanger Institute
[email_address] </li></ul>
The Sanger Institute <ul><li>Funded by Wellcome Trust. </li><ul><li>2 nd  largest research charity in the world.
~700 employees.
Based in Hinxton Genome Campus, Cambridge, UK. </li></ul><li>Large scale genomic research. </li><ul><li>Sequenced 1/3 of t...
We have active cancer, malaria, pathogen and genomic variation / human health studies. </li></ul><li>All data is made publ...
DNA Sequencing  TCTTTATTTTAGCTGGACCAGACCAATTTTGAGGAAAGGATACAGACAGCGCCTG AAGGTATGTTCATGTACATTGTTTAGTTGAAGAGAGAAATTCATATTATT...
Moore's Law Compute/disk doubles every 18 months Sequencing doubles every 12 months
Economic Trends: <ul><li>The Human genome project:  </li><ul><li>13 years.
23 labs.
$500 Million. </li></ul><li>A Human genome today: </li><ul><li>3 days.
1 machine.
$8,000. </li></ul><li>Trend will continue: </li><ul><li>$500 genome is probable within 3-5 years. </li></ul></ul>
The scary graph Peak Yearly capillary sequencing: 30 Gbase Current weekly sequencing: 6000 Gbase
Our Science
UK 10K Project <ul><li>Decode the genome of 10,000 people in  the uk.
Will improve the understanding of human genetic variation and disease. </li></ul>Genome Research Limited Wellcome Trust la...
New scale, new insights . . . to common disease <ul><ul><li>Coronary heart disease
Hypertension
Bipolar disorder
Arthritis
Obesity
Diabetes (types I and II)
Breast cancer
Malaria
Tuberculosis </li></ul></ul>
Cancer Genome Project <ul><li>Cancer is a disease caused by abnormalities in a cell's genome. </li></ul>
Detailed Changes: <ul><li>Sequencing hundreds of cancer samples
First Comprehensive look at cancer genomes </li><ul><li>Lung Cancer
Malignant melanoma
Breast cancer </li></ul><li>Identify driver mutations for: </li><ul><li>Improved diagnostics
Development of novel therapies
Targeting of existing therapeutics </li></ul></ul>Lung Cancer and melanoma laid bare; 16 December 2009  www.sanger.ac.uk/a...
IT Challenges
Managing Growth <ul><li>Analysing the data takes a lot of compute and disk space </li><ul><li>Finished sequence is the sta...
1000$ genome*
*Informatics not included </li></ul>
Sequencing data flow. Alignments (200GB) Variation data (1GB) Feature (3MB) Raw data (10 TB) Sequence (500GB) Sequencer Pr...
Data centre <ul><li>4x250 M 2  Data centres. </li><ul><li>2-4KW / M 2  cooling.
1.8 MW power draw
1.5 PUE  </li></ul><li>Overhead aircon, power and networking. </li><ul><li>Allows counter-current cooling.
Focus on power & space efficient storage and compute.  </li></ul><li>Technology Refresh. </li><ul><li>1 data centre is an ...
Our HPC Infrastructure <ul><li>Compute </li><ul><li>8500 cores
10GigE / 1GigE networking. </li></ul><li>High performance storage </li><ul><li>1.5 PB DDN 9000&10000 storage
Lustre filesystem </li></ul><li>LSF queuing system </li></ul>
Ensembl <ul><li>Data visualisation / Mining web services. </li><ul><li>www.ensembl.org
Provides web / programmatic interfaces to genomic data.
10k visitors / 126k page views per day. </li></ul><li>Compute Pipeline (HPTC Workload) </li><ul><li>Take a raw genome and ...
Ensembl at Sanger/EBI provides automated analysis for 51 vertebrate genomes. </li></ul><ul><li>Software is Open Source (ap...
Data is free for download. </li></ul></ul>
Sequencing data flow. Alignments (200GB) Variation data (1GB) Feature (3MB) Raw data (10 TB) Sequence (500GB) Sequencer Pr...
TCCTCTCTTTATTTTAGCTGGACCAGACCAATTTTGAGGAAAGGATACAGACAGCGCCTG GAATTGTCAGACATATACCAAATCCCTTCTGTTGATTCTGCTGACAATCTATCTGAAAAA ...
Annotation
Annotation
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Cloud Experiences

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Sanger Institute's experiences with the cloud.

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Cloud Experiences

  1. 1. Cloud Experiences <ul><li>Guy Coates
  2. 2. Wellcome Trust Sanger Institute
  3. 3. [email_address] </li></ul>
  4. 4. The Sanger Institute <ul><li>Funded by Wellcome Trust. </li><ul><li>2 nd largest research charity in the world.
  5. 5. ~700 employees.
  6. 6. Based in Hinxton Genome Campus, Cambridge, UK. </li></ul><li>Large scale genomic research. </li><ul><li>Sequenced 1/3 of the human genome. (largest single contributor).
  7. 7. We have active cancer, malaria, pathogen and genomic variation / human health studies. </li></ul><li>All data is made publicly available. </li><ul><li>Websites, ftp, direct database. access, programmatic APIs. </li></ul></ul>
  8. 8. DNA Sequencing TCTTTATTTTAGCTGGACCAGACCAATTTTGAGGAAAGGATACAGACAGCGCCTG AAGGTATGTTCATGTACATTGTTTAGTTGAAGAGAGAAATTCATATTATTAATTA TGGTGGCTAATGCCTGTAATCCCAACTATTTGGGAGGCCAAGATGAGAGGATTGC ATAAAAAAGTTAGCTGGGAATGGTAGTGCATGCTTGTATTCCCAGCTACTCAGGAGGCTG TGCACTCCAGCTTGGGTGACACAG CAACCCTCTCTCTCTAAAAAAAAAAAAAAAAAGG AAATAATCAGTTTCCTAAGATTTTTTTCCTGAAAAATACACATTTGGTTTCA ATGAAGTAAATCG ATTTGCTTTCAAAACCTTTATATTTGAATACAAATGTACTCC 250 Million * 75-108 Base fragments Human Genome (3GBases)
  9. 9. Moore's Law Compute/disk doubles every 18 months Sequencing doubles every 12 months
  10. 10. Economic Trends: <ul><li>The Human genome project: </li><ul><li>13 years.
  11. 11. 23 labs.
  12. 12. $500 Million. </li></ul><li>A Human genome today: </li><ul><li>3 days.
  13. 13. 1 machine.
  14. 14. $8,000. </li></ul><li>Trend will continue: </li><ul><li>$500 genome is probable within 3-5 years. </li></ul></ul>
  15. 15. The scary graph Peak Yearly capillary sequencing: 30 Gbase Current weekly sequencing: 6000 Gbase
  16. 16. Our Science
  17. 17. UK 10K Project <ul><li>Decode the genome of 10,000 people in the uk.
  18. 18. Will improve the understanding of human genetic variation and disease. </li></ul>Genome Research Limited Wellcome Trust launches study of 10,000 human genomes in UK; 24 June 2010 www.sanger.ac.uk/about/press/2010/100624-uk10k.html
  19. 19. New scale, new insights . . . to common disease <ul><ul><li>Coronary heart disease
  20. 20. Hypertension
  21. 21. Bipolar disorder
  22. 22. Arthritis
  23. 23. Obesity
  24. 24. Diabetes (types I and II)
  25. 25. Breast cancer
  26. 26. Malaria
  27. 27. Tuberculosis </li></ul></ul>
  28. 28. Cancer Genome Project <ul><li>Cancer is a disease caused by abnormalities in a cell's genome. </li></ul>
  29. 29. Detailed Changes: <ul><li>Sequencing hundreds of cancer samples
  30. 30. First Comprehensive look at cancer genomes </li><ul><li>Lung Cancer
  31. 31. Malignant melanoma
  32. 32. Breast cancer </li></ul><li>Identify driver mutations for: </li><ul><li>Improved diagnostics
  33. 33. Development of novel therapies
  34. 34. Targeting of existing therapeutics </li></ul></ul>Lung Cancer and melanoma laid bare; 16 December 2009 www.sanger.ac.uk/about/press/2009/091216.html
  35. 35. IT Challenges
  36. 36. Managing Growth <ul><li>Analysing the data takes a lot of compute and disk space </li><ul><li>Finished sequence is the start of the problem, not the end. </li></ul><li>Growth of compute & storage </li><ul><li>Storage /compute doubles every 12 months. </li><ul><li>2010 ~12 PB raw </li></ul></ul><li>Moore's law will not save us.
  37. 37. 1000$ genome*
  38. 38. *Informatics not included </li></ul>
  39. 39. Sequencing data flow. Alignments (200GB) Variation data (1GB) Feature (3MB) Raw data (10 TB) Sequence (500GB) Sequencer Processing/ QC Comparative analysis datastore Structured data (databases) Unstructured data (Flat files) Internet
  40. 40. Data centre <ul><li>4x250 M 2 Data centres. </li><ul><li>2-4KW / M 2 cooling.
  41. 41. 1.8 MW power draw
  42. 42. 1.5 PUE </li></ul><li>Overhead aircon, power and networking. </li><ul><li>Allows counter-current cooling.
  43. 43. Focus on power & space efficient storage and compute. </li></ul><li>Technology Refresh. </li><ul><li>1 data centre is an empty shell. </li><ul><li>Rotate into the empty room every 4 years and refurb. </li></ul><li>“Fallow Field” principle. </li></ul></ul>rack rack rack rack
  44. 44. Our HPC Infrastructure <ul><li>Compute </li><ul><li>8500 cores
  45. 45. 10GigE / 1GigE networking. </li></ul><li>High performance storage </li><ul><li>1.5 PB DDN 9000&10000 storage
  46. 46. Lustre filesystem </li></ul><li>LSF queuing system </li></ul>
  47. 47. Ensembl <ul><li>Data visualisation / Mining web services. </li><ul><li>www.ensembl.org
  48. 48. Provides web / programmatic interfaces to genomic data.
  49. 49. 10k visitors / 126k page views per day. </li></ul><li>Compute Pipeline (HPTC Workload) </li><ul><li>Take a raw genome and run it through a compute pipeline to find genes and other features of interest.
  50. 50. Ensembl at Sanger/EBI provides automated analysis for 51 vertebrate genomes. </li></ul><ul><li>Software is Open Source (apache license).
  51. 51. Data is free for download. </li></ul></ul>
  52. 52. Sequencing data flow. Alignments (200GB) Variation data (1GB) Feature (3MB) Raw data (10 TB) Sequence (500GB) Sequencer Processing/ QC Comparative analysis datastore Structured data (databases) Unstructured data (Flat files) Internet HPC Compute Pipeline Web / Database infrastructure
  53. 53. TCCTCTCTTTATTTTAGCTGGACCAGACCAATTTTGAGGAAAGGATACAGACAGCGCCTG GAATTGTCAGACATATACCAAATCCCTTCTGTTGATTCTGCTGACAATCTATCTGAAAAA TTGGAAAGGTATGTTCATGTACATTGTTTAGTTGAAGAGAGAAATTCATATTATTAATTA TTTAGAGAAGAGAAAGCAAACATATTATAAGTTTAATTCTTATATTTAAAAATAGGAGCC AAGTATGGTGGCTAATGCCTGTAATCCCAACTATTTGGGAGGCCAAGATGAGAGGATTGC TTGAGACCAGGAGTTTGATACCAGCCTGGGCAACATAGCAAGATGTTATCTCTACACAAA ATAAAAAAGTTAGCTGGGAATGGTAGTGCATGCTTGTATTCCCAGCTACTCAGGAGGCTG AAGCAGGAGGGTTACTTGAGCCCAGGAGTTTGAGGTTGCAGTGAGCTATGATTGTGCCAC TGCACTCCAGCTTGGGTGACACAGCAAAACCCTCTCTCTCTAAAAAAAAAAAAAAAAAGG AACATCTCATTTTCACACTGAAATGTTGACTGAAATCATTAAACAATAAAATCATAAAAG AAAAATAATCAGTTTCCTAAGAAATGATTTTTTTTCCTGAAAAATACACATTTGGTTTCA GAGAATTTGTCTTATTAGAGACCATGAGATGGATTTTGTGAAAACTAAAGTAACACCATT ATGAAGTAAATCGTGTATATTTGCTTTCAAAACCTTTATATTTGAATACAAATGTACTCC
  54. 54. Annotation
  55. 55. Annotation
  56. 56. Why Cloud?
  57. 57. Web Services <ul><li>Ensembl has a worldwide audience.
  58. 58. Historically, web site performance was not great, especially for non-european institutes. </li><ul><li>Pages were quite heavyweight.
  59. 59. Not properly cached etc. </li></ul><li>Web team spent a lot of time re-designing the code to make it more streamlined. </li><ul><li>Greatly improved performance. </li></ul><li>Coding can only get you so-far. </li><ul><li>150-240ms round trip time from Europe to the US.
  60. 60. We need a set of geographically dispersed mirrors. </li></ul></ul>
  61. 61. Colocation <ul><li>Real machines in a co-lo facility in California. </li><ul><li>Traditional mirror. </li></ul><li>Hardware was initially configured on site. </li><ul><li>16 servers, SAN storage, SAN switches, SAN management appliance, Ethernet switches, firewall, out-of-band management etc. </li></ul><li>Shipped to the co-lo for installation. </li><ul><li>Sent a person to California for 3 weeks.
  62. 62. Spent 1 week getting stuff into/out of customs. </li><ul><li>****ing FCC paperwork! </li></ul></ul><li>Additional infrastructure work. </li><ul><li>VPN between UK and US. </li></ul><li>Incredibly time consuming. </li><ul><li>Really don't want to end up having to send someone on a plane to the US to fix things. </li></ul></ul>
  63. 63. Cloud Opportunities <ul><li>We wanted more mirrors. </li><ul><li>US East coast, asia-pacific. </li></ul><li>Investigations into AWS already ongoing.
  64. 64. Many people would like to run ensembl webcode to visualise their own data. </li><ul><li>Non trivial for the non-expert user. </li><ul><li>Mysql, apache, perl. </li></ul></ul><li>Can we distribute AMIs instead? </li><ul><li>Ready to run. </li></ul><li>Can we eat our own dog-food? </li><ul><li>Run mirror site from the AMIs? </li></ul></ul>
  65. 65. What we actually did: AWS Sanger Sanger VPN
  66. 66. Building a mirror on AWS <ul><li>Application development was required </li><ul><li>Significant code changes required to make the webcode “mirror aware”. </li><ul><li>Mostly done for the original co-location site. </li></ul></ul><li>Some software development / sysadmin work needed. </li><ul><li>Preparation of OS images, software stack configuration.
  67. 67. VPN configuration </li></ul><li>Significant amount of tuning required. </li><ul><li>Initial mysql performance was pretty bad, especially for the large ensembl databases. (~1TB).
  68. 68. Lots of people doing Apache/mysql on AWS, so there is a good amount of best-practice etc available. </li></ul></ul>
  69. 69. Traffic
  70. 70. Is it cost effective? <ul><li>Lots of misleading cost statements made about cloud. </li><ul><li>“Our analysis only cost $500.”
  71. 71. CPU is only “$0.085 / hr”. </li></ul><li>What are we comparing against? </li><ul><li>Doing the analysis once? Continually?
  72. 72. Buying a $2000 server?
  73. 73. Leasing a $2000 server for 3 years?
  74. 74. Using $150 of time at your local supercomputing facility?
  75. 75. Buying a $2000 of server but having to build a $1M datacentre to put it in? </li></ul><li>Requires the dreaded Total Cost of Ownership (TCO) calculation. </li><ul><li>hardware + power + cooling + facilities + admin/developers etc </li><ul><li>Incredibly hard to do. </li></ul></ul></ul>
  76. 76. Breakdown: <ul><li>Comparing costs to the “real” Co-lo </li><ul><li>power, cooling costs are all included.
  77. 77. Admin costs are the same, so we can ignore them. </li><ul><li>Same people responsible for both. </li></ul></ul><li>Cost for Co-location facility: </li><ul><li>$120,000 hardware + $51,000 /yr colo.
  78. 78. $91,000 per year (3 years hardware lifetime). </li></ul><li>Cost for AWS site: </li><ul><li>$84,000 per year. </li></ul><li>We can run 3 mirrors for 90% of the cost of 1 mirror.
  79. 79. It is not free! </li></ul>
  80. 80. Advantages <ul><li>No physical hardware. </li><ul><li>Work can start as soon as we enter our credit card numbers...
  81. 81. No US customs, Fedex etc. </li></ul><li>Less hardware: </li><ul><li>No Firewalls, SAN management appliances etc. </li></ul><li>Much simpler management infrastructure. </li><ul><ul><li>AWS give you out of band management “for free”.
  82. 82. No hardware issues. </li></ul></ul><li>Easy path for growth. </li><ul><li>No space constraints. </li><ul><li>No need to get tin decommissioned /re-installed at Co-lo. </li></ul><li>Add more machines until we run out of cash. </li></ul></ul>
  83. 83. Downsides <ul><li>Underestimated the time it would take to make the web-code mirror-ready. </li><ul><li>Not a cloud specific problem, but something to be aware of when you take big applications and move them outside your home institution. </li></ul><li>Curation of software images takes time. </li><ul><li>Regular releases of new data and code.
  84. 84. Ensembl team now has a dedicated person responsible for the cloud.
  85. 85. Somebody has to look after the systems. </li></ul><li>Management overhead does not necessarily go down. </li><ul><li>But it does change. </li></ul></ul>
  86. 86. Going forward <ul>Change code to remove all dependencies on Sanger. <ul><li>Full DR capability. </li></ul><li>Make the AMIs publically available. </li><ul><li>Today we have Mysql servers + data. </li><ul><li>Data generously hosted on Amazon public datasets. </li></ul><li>Allow users to simply run their own sites. </li></ul></ul>
  87. 87. HPC Workloads
  88. 88. Why HPC in the Cloud? <ul><li>We already have a data-centre. </li><ul><li>Not seeking to replace our existing infrastructure.
  89. 89. Not cost effective. </li></ul><li>But: Long lead-times for installing kit. </li><ul><li>~3-6 months from idea to going live.
  90. 90. Longer than the science can wait.
  91. 91. Ability to burst capacity might be useful. </li></ul><li>Test environments. </li><ul><li>Test at scale.
  92. 92. Large clusters for a short amount of time. </li></ul></ul>
  93. 93. Distributing analysis tools <ul><li>Sequencing is becoming a commodity.
  94. 94. Informatics / analysis tools needs to be commodity too.
  95. 95. Requires a significant amount of domain knowledge. </li><ul><li>Complicated software installs, relational databases etc. </li></ul><li>Goal: </li><ul><li>Researcher with no IT knowledge can take their sequence data, upload it to AWS, get it analysed and view the results. </li></ul></ul>
  96. 96. Life Sciences HPC Workloads Tightly Coupled (MPI) Embarrassingly Parallel CPU Bound IO Bound modelling/ docking Simulation Genomics
  97. 97. Our Workload <ul><li>Embarrassingly Parallel. </li><ul><li>Lots of single threaded jobs.
  98. 98. 10,000s of jobs.
  99. 99. Core algorithms in C
  100. 100. Perl pipeline manager to generate and manage workflow.
  101. 101. Batch schedular to execute jobs on nodes.
  102. 102. mysql database to hold results & state. </li></ul><li>Moderate memory sizes. </li><ul><li>3 GB/core </li></ul><li>IO bound. </li><ul><li>Fast parallel filesystems. </li></ul></ul>
  103. 103. Life Sciences HPC Workloads Tightly Coupled (MPI) Embarrassingly Parallel CPU Bound IO Bound modelling/ docking Simulation Genomics
  104. 104. Different Architectures VS CPU CPU CPU Fat Network POSIX Global filesystem CPU CPU CPU CPU thin network Local storage Local storage Local storage Local storage Batch schedular S3 Hadoop?
  105. 105. Life Sciences HPC Workloads Tightly Coupled (MPI) Embarrassingly Parallel CPU Bound IO Bound modelling/ docking Simulation Genomics
  106. 106. Careful choice of problem: <ul><li>Choose a simple part of the pipeline </li><ul><li>Re-factor all the code that expects global filesystem and make it use S3. </li></ul><li>Why not use hadoop? </li><ul><li>Production code that works nicely inside Sanger.
  107. 107. Vast effort to port code, for little benefit.
  108. 108. Questions about stability for multi-user systems internally. </li></ul><li>Build self assembling HPC cluster. </li><ul><li>Code which will spin up AWS images and self assembles into a HPC cluster and batch schedular. </li></ul><li>Cloud allows you to simplify. </li><ul><li>Sanger compute cluster is shared. </li><ul><li>Lots of complexity in ensuring applications/users play nicely together. </li></ul><li>AWS clusters are unique to a user/application. </li></ul></ul>
  109. 109. The real problem: Internet <ul><li>Data transfer rates (gridFTP/FDT via our 2 Gbit/s site link). </li><ul><li>Cambridge -> EC2 East coast: 12 Mbytes/s (96 Mbits/s)
  110. 110. Cambridge -> EC2 Dublin: 25 Mbytes/s (200 Mbits/s)
  111. 111. 11 hours to move 1TB to Dublin.
  112. 112. 23 hours to move 1 TB to East coast. </li></ul><li>What speed should we get? </li><ul><li>Once we leave JANET (UK academic network) finding out what the connectivity is and what we should expect is almost impossible. </li></ul><li>Do you have fast enough disks at each end to keep the network full? </li></ul>
  113. 113. Networking <ul><li>How do we improve data transfers across the public internet? </li><ul><li>CERN approach; don't.
  114. 114. 10 Gbit dedicated network between CERN and the T1 centres. </li></ul><li>Can it work for cloud? </li><ul><li>Buy dedicated bandwidth to a provider. </li><ul><li>Ties you in.
  115. 115. Should they pay? </li></ul></ul><li>What happens when you want to move? </li></ul>
  116. 116. Summary <ul><li>Moving existing HPC applications is painful.
  117. 117. Small data / high CPU applications work really well.
  118. 118. Large data applications less well. </li></ul>
  119. 119. Data Security
  120. 120. Are you allowed to put data on the cloud? <ul><li>Default policy:
  121. 121. “Our data is confidential/important/critical to our business.
  122. 122. We must keep our data on our computers.”
  123. 123. “Apart from when we outsource it already.” </li></ul>
  124. 124. Reasons to be optimistic: <ul><li>Most (all?) data security issues can be dealt with. </li><ul><li>But the devil is in the details.
  125. 125. Data can be put on the cloud, if care is taken. </li></ul><li>It is probably more secure there than in your own data-centre. </li><ul><li>Can you match AWS data availability guarantees? </li></ul><li>Are cloud providers different from any other organisation you outsource to? </li></ul>
  126. 126. Outstanding Issues <ul><li>Audit and compliance: </li><ul><li>If you need IP agreements, above your providers standard T&Cs, how do you push them through? </li></ul><li>Geographical boundaries mean little in the cloud. </li><ul><li>Data can be replicated across national boundaries, without end user being aware. </li></ul><li>Moving personally identifiable data outside of the EU is potentially problematic. </li><ul><li>(Can be problematic within the EU; privacy laws are not as harmonised as you might think.)
  127. 127. More sequencing experiments are trying to link with phenotype data. (ie personally identifiable medical records). </li></ul></ul>
  128. 128. Private Cloud to rescue? <ul><li>Can we do something different? </li></ul>
  129. 129. Traditional Collaboration DCC: Sequencing Centre + Archive Sequencing centre Sequencing centre Sequencing centre Sequencing centre IT IT IT IT
  130. 130. Dark Archives <ul><li>Storing data in an archive is not particularly useful. </li><ul><li>You need to be able to access the data and do something useful with it. </li></ul><li>Data in current archives is “dark”. </li><ul><li>You can put/get data, but cannot compute across it.
  131. 131. Is data in an inaccessible archive really useful? </li></ul></ul>
  132. 132. Private Cloud Collaborations Sequencing Centre Sequencing centre Sequencing centre Sequencing centre Private Cloud IaaS / SaaS Private Cloud IaaS / SaaS
  133. 133. Private Cloud <ul><li>Advantages: </li><ul><li>Small organisations leverage expertise of big IT organisations.
  134. 134. Academia tends to be linked by fast research networks. </li><ul><li>Moving data is easier. (move compute to the data via VMs) </li></ul><li>Consortium will be signed up to data-access agreements. </li><ul><li>Simplifies data governance. </li></ul></ul><li>Problems: </li><ul><li>Big change in funding model.
  135. 135. Are big centres set up to provide private cloud services? </li><ul><li>Selling services is hard if you are a charity. </li></ul><li>Can we do it as well as the big internet companies? </li></ul></ul>
  136. 136. Summary <ul><li>Cloud is a useful tool. </li><ul><li>Will not replace our local IT infrastructure. </li></ul><li>Porting existing applications can be hard. </li><ul><li>Do not underestimate time / people. </li></ul><li>Still need IT staff. </li><ul><li>End up doing different things. </li></ul></ul>
  137. 137. Acknowledgements <ul><li>Sanger </li><ul><li>Phil Butcher
  138. 138. James Beal
  139. 139. Pete Clapham
  140. 140. Simon Kelley
  141. 141. Gen-Tao Chiang </li></ul><ul><li>Steve Searle
  142. 142. Jan-Hinnerk Vogel
  143. 143. Bronwen Aken </li></ul></ul><ul><li>EBI
  144. 144. Glenn Proctor
  145. 145. Steve Keenan </li></ul>

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