Outline What is it? Why now? Cloud killer apps Economics for - - PowerPoint PPT Presentation

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Outline What is it? Why now? Cloud killer apps Economics for - - PowerPoint PPT Presentation

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UC Berkeley Above the Clouds A Berkeley View of Cloud Computing UC Berkeley RAD Lab Presentation at RPI, September 2011 1 Outline What is it? Why now? Cloud killer apps Economics for users Economics for providers

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UC Berkeley

Above the Clouds

A Berkeley View of Cloud Computing

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UC Berkeley RAD Lab

Presentation at RPI, September 2011

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Outline

  • What is it?
  • Why now?
  • Cloud killer apps
  • Economics for users
  • Economics for providers
  • Challenges and opportunities
  • Implications

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Cloud computing is “hot”…

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Larry Ellison, Oracle’s CEO, quoted in Wall Street Journal, September 26, 2008 “A new term for the long-held dream of computing as a utility [D. Parkhill, The Challenge of the Computer Utility, Addison Wesley, 1966]”

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What is Cloud Computing?

  • Old idea: Software as a Service (SaaS)

– Def: delivering applications over the Internet

  • Recently: “[Hardware, Infrastructure,

Platform] as a service”

– Poorly defined so we avoid all “X as a service”

  • Utility Computing: pay-as-you-go computing

– Illusion of infinite resources – No up-front cost – Fine-grained billing (e.g. hourly)

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except SaaS…

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SaaS and Cloud: Users and Providers

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Why Now?

  • Experience with very large datacenters

– Unprecedented economies of scale

  • Other factors

– Pervasive broadband Internet – Fast x86 virtualization – Pay-as-you-go billing model – Standard software stack

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Spectrum of Clouds

  • Instruction Set VM (Amazon EC2, 3Tera)
  • Bytecode VM (Microsoft Azure)
  • Framework VM

– Google AppEngine, Force.com

EC2 Azure AppEngine Force.com Lower-level, Less management Higher-level, More management

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IaaS PaaS SaaS

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Composite Clouds

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SaaS PaaS IaaS

It is possible to stack/layer services, so that, e.g., Gmail (SaaS) uses the Google Apps Engine (PaaS) over virtual machines provided by Amazon (IaaS). Notice that layering hides SaaS user from back-end infrastructure.

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Cloud Killer Apps

  • Mobile and web applications
  • Extensions of desktop software

– Matlab, Mathematica

  • Batch processing / MapReduce

– Oracle at Harvard, Hadoop at NY Times

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Unused resources

Economics of Cloud Users

  • Pay by use instead of provisioning for peak

Static data center Data center in the cloud

Demand Capacity

Time Resources

Demand Capacity

Time Resources

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Unused resources

Economics of Cloud Users

  • Risk of over-provisioning: underutilization

Static data center

Demand Capacity

Time Resources

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Economics of Cloud Users

  • Heavy penalty for under-provisioning

Lost revenue Lost users

Resources Demand Capacity Time (days) 1 2 3 Resources Demand Capacity Time (days) 1 2 3 Resources Demand Capacity Time (days) 1 2 3

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To cloud or not to cloud?

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Revenue using public cloud vs revenue using private cloud Hybrid clouds combine the benefits of both!

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September 2011 Amazon EC2 Instance Costs

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Source: http://aws.amazon.com/ec2/pricing/

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September 2011 Amazon Data Transfer Costs

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Source: http://aws.amazon.com/ec2/pricing/

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September 2011 Amazon Free Trials Available!

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Source: http://aws.amazon.com/ec2/pricing/

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Economics of Cloud Providers

  • 5-7x economies of scale [Hamilton 2008]
  • Extra benefits

– Amazon: utilize off-peak capacity – Microsoft: sell .NET tools – Google: reuse existing infrastructure

Resource Cost in Medium DC Cost in Very Large DC Ratio Network $95 / Mbps / month $13 / Mbps / month 7.1x Storage $2.20 / GB / month $0.40 / GB / month 5.7x Administration ≈140 servers/admin >1000 servers/admin 7.1x

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Economics of Cloud Providers

  • Regional prices vary, e.g.:

Price per KWH Where Why 3.6 cents Idaho Hydroelectric power, not sent long distance 10.0 cents California Electricity transmitted long distance

  • ver the grid; no coal fired electricity

18.0 cents Hawaii Must ship fuel to generate electricity

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Opportunities for geographical, seasonal, re-distribution

  • f resources, e.g., cooling unneeded in northern/southern

hemisphere: cloud on a boat!

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Adoption Challenges

Challenge Opportunity Availability Multiple providers & DCs Data lock-in Standardization Data Confidentiality and Auditability Encryption, VLANs, Firewalls; Geographical Data Storage

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Lock-in/Business Continuity

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Data Lock-in

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Growth Challenges

Challenge Opportunity Data transfer bottlenecks FedEx-ing disks, Data Backup/Archival Performance unpredictability Improved VM support, flash memory, scheduling VMs Scalable storage Invent scalable store Bugs in large distributed systems Invent Debugger that relies

  • n Distributed VMs

Scaling quickly Invent Auto-Scaler that relies

  • n Machine Learning;

Snapshots

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Data is a Gravity Well

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See: http://aws.amazon.com/publicdatasets/  Possible interesting course projects here…

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Data is a Gravity Well

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Policy and Business Challenges

Challenge Opportunity Reputation Fate Sharing Offer reputation-guarding services like those for email Software Licensing Pay-for-use licenses; Bulk use sales

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Policy and Business Challenges

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Policy and Business Challenges

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Short Term Implications

  • Startups and prototyping
  • One-off tasks

– Washington post, NY Times

  • Cost associativity for scientific applications
  • Research at scale

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Long Term Implications

  • Application software:

– Cloud & client parts, disconnection tolerance

  • Infrastructure software:

– Resource accounting, VM awareness

  • Hardware systems:

– Containers, energy proportionality

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