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Above the Clouds: A View From Academia

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The closing keynote by Armando Fox at the Eduserv Symposium 2011 - Virtualisation and the Cloud.

The closing keynote by Armando Fox at the Eduserv Symposium 2011 - Virtualisation and the Cloud.

Published in Technology , Education
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  • You can watch a video of Armando Fox giving his presentation at the Eduserv Symposium 2011:

    http://www.eduserv.org.uk/newsandevents/events/eduserv-symposium-2011/closing-keynote---above-the-clouds---a-view-from-academia
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  • 1. Above the Clouds: A View From Academia Armando Fox, UC Berkeley EDUSERV Symposium, 12 May 2011 Presentation slides licensed under Creative Commons Attribution-ShareAlike 3.0 Unported License. Image: John Curley http://www.flickr.com/photos/jay_que/1834540/
  • 2. Who Am I?
    • Research: Internet-scale systems; productive parallel programming
    • Teaching: software engineering
    • Writing: co-author Above the Clouds tech report
    • Disclaimer 1: I don’t speak for UC
    • Disclaimer 2: Relationship with Amazon
  • 3. How We Got Into the Cloud: RAD Lab’s 5-year Mission
    • Enable 1 entrepreneur to prototype a great Web app over 3-day weekend, then deploy at scale
    • Key technology: Statistical machine learning
    • Early critiques: “Demonstrate your ideas at scale!”
    • Moved from Sun Blackbox to EC2 in mid-2008
    • Feb. 2009: Above the Clouds tech report *
      • Over 50K downloads, influenced high-profile IT co.’s
    * abovetheclouds.cs.berkeley.edu, or CACM April 2010
  • 4. Outline: Two Themes
    • Academic clouds: public or private?
      • Theme 1 : save money or improve research?
      • Theme 2: cloud user or cloud provider?
    • Assumption : familiar with cloud basics
      • Public/pay-as-you-go
      • Private/closed/“condo”
    • Non-goal : regulatory thickets around cloudifying “sensitive” information
  • 5. Public Cloud: CS Research
    • Over $350,000 spent on AWS since 2008
      • PhD student ~ US$75k/year => cloud ~ 1/3 student/mo.
    • Experiments: 100-300 nodes common, 900 max
      • large-scale storage, cloud programming, MapReduce
      • results at scale now required for top-tier conferences
      • most experiments last 0-4 hours
      • “ Small” experiments also in cloud for convenience
    • Comparison: Sun BlackBox at Berkeley
      • $200k acquire & install
      • $300k+ in hardware donations
      • staff: ≥0.5 FTE
  • 6. Public Cloud: CS Education
    • Great Ideas in Computer Architecture (reinvented Fall 2010): 190 students
    • Software Engineering for Software-as-a-Service: 70 students
    • Operating Systems: 70 students
    • Intro. Data Science: 30 students
    • Adv. topics in HCI: 20 students
    • Natural language processing: 20 students
    • Large-scale programming abstractions for the cloud: ~20 students (Fall 2011)
    • Administration, provisioning, sizing much easier on public cloud than UC instructional computing
  • 7. Cloud Economics
    • “ Private should be cheaper if you have stable utilization”
    Demand Capacity Time Demand Capacity Time
  • 8. $Private < $Public?
    • Capital: hardware, networking, power 5-7x cheaper at 100K’s scale (Hamilton 2007)
    • Operations: heavy automation => 1000’s machines per FTE admin
    • R&D: cloud providers had to serve internal business need
    • Services: “Scale makes availability affordable”: wide-area disaster recovery facilities
    • Hidden/shared costs: power, cooling, staff, ....
  • 9. Hard to Compete on Cost
    • Zero-touch metering/billing infrastructure
    • Optimized for low margin
      • $0.08/hr: virtual CPU on EC2
      • $0.02-0.08/hr: as-available “spot instances”
      • Reserved (prepay 1-3 years, save if utilization > 25%)
      • $0.00/hr: 1 year free usage tier for all services
      • Private: ≥ $0.075/hr ($2000 private server amortized over 3 years with no indirect costs)
    • “ Moving to EC2 would cost about a factor of 2”
      • Highly placed colleague at major social site
  • 10. Try to smooth out peaks?
    • Not waiting in queues accelerates research!
      • Run several experiments simultaneously, each using 100’s of machines for 1-2 hours, without queueing up
      • Basic queueing theory: trade utilization vs service time
      • Better performance isolation than private cloud (!)
      • N.B. for long jobs, some queueing may be OK
    • Corollary 1: cost-associative billing encourages research spontaneity
    • Corollary 2: incentive to stop using is important!
    • Effective metering & billing is key to on-demand usage model
  • 11. Example: wait times on UC Berkeley “Mako” cluster
    • Mako has 272 dual-socket (quad-core per socket) nodes with 24 GB RAM each
    • Source: ShaRCS—Shared Research Computing Services, presentation by UC Office of the President at the UC Cloud Summit, April 2011
  • 12. On the other hand...Big Data * Simson L. Garfinkel, An Evaluation of Amazon’s Grid Computing Services: EC2, S3 and SQS, Technical Report TR-08-07, School of Engineering & Applied Sciences, Harvard University, 2008.  Source: Ed Lazowska, eScience 2010, Microsoft Cloud Futures Workshop, lazowska.cs.washington.edu/cloud2010.pdf
    • Challenge: Long-haul networking is most expensive cloud resource, and improving most slowly
    • Copy 8 TB to EC2 at ~20 Mbps*: ~35 days, ~$800
    • Ship four 2 TB drives to Amazon: 1 day, ~$150
    • Can private/shared networking resources be combined with public cloud to get best of both?
    Application Data generated per day  DNA Sequencing (Illumina HiSeq machine) 1 TB Large Synoptic Survey Telescope 30 TB; 400 Mbps sustained data rate between Chile and NCSA Large Hadron Collider 60 TB
  • 13. On the other hand...Cloud Provider
    • Hard research on public cloud:
      • scheduling/provisioning research
      • security: honeypots, malware containment,epidemic modeling
      • energy efficiency or other physical monitoring
      • experimenting with networking fabric, multicast, etc.
    • N.B., cloud provider research needs cloud users!
      • Example: Microsoft Research Silicon Valley “Sherwood” cluster (~240 nodes)
    • Demanding customers drove cloud research
  • 14. Nonprofit/Academic clouds
    • PlanetLab & Emulab
      • highly successful from their customers’ point of view
      • lots of great research, some of which might have been impossible on today’s public cloud
    • Academic/research clusters
      • Yahoo/IBM/M45 cluster, Google/IBM cluster, TerraGrid: primarily application-level research
      • OpenCirrus (HP/Intel/Yahoo/UIUC/IDA Singapore/Karlsruhe): bare-metal, federated, 1K+ cores/site
    • Access model: write proposal; closed community
    • Saving money is non-goal (in fact, a subsidized investment by universities & industrial partners)
  • 15. OpenCirrus
    • Infrastructure costs increase with # sites
    • Claim: even at ~50% utilization, owning your infrastructure pays for itself in ~3 years
    • Source: R. Campbell et al., OpenCirrus..., Proc. 2011 Workshop on Hot Topics in Cloud Computing (HotCloud’09), June 2011 (to appear)
  • 16. Public & private clouds don’t see same benefits * Implies ability to meter, and incentive to release idle resources Benefit Public Private “ infinite” resources on-demand Yes No Instantaneous provisioning Yes Varies Better hardware Yes No Zero-commitment pay-as-you-go* Yes No Reduced costs from economy of scale Yes No Can do “cloud provider” research No Yes Can trust co-tenants No Yes Better utilization through virtualization Yes Yes Quickly & inexpensively move big data No Yes Address data-custody regulatory issues Varies Yes
  • 17. So You Want to Build a Cloud...
    • Single point of failure?
    • Zero Touch?
    • Hidden costs?
  • 18. Single point of failure
    • 30+ hour EBS outage on 21 April 2011
      • triggered by human error (network config change)
    • Georedundant services (Netflix) largely unaffected
      • At least, georedundancy was an available option!
    • Non-redundant services had catastrophic outages
    • Question: would “more” operational expertise have resolved outage faster?
  • 19. Metering and Billing
    • Billing is policy. Metering is mechanism.
      • Pay-as-you-go policy allows cost associativity
      • Any policy only as flexible as its mechanism
      • Amazon’s mechanism: “zero touch” metering
      • So, Virtual Private Cloud ≠ your private cloud
    • Which of these need human intervention:
      • Signing up? Provisioning? Deploying? Billing?
      • Academic/nonprofit clouds don’t even try this
  • 20. Hidden Costs
    • Single billing scheme captures all costs, or must some costs be billed/accounted separately?
      • shared expenses: power, networking
      • general employment benefits/overhead for staff
    • Cost of keeping up with innovation
      • On average, AWS has deployed 1 new service every 2 months since EC2 beta launch*
    • Competition from new providers will exacerbate
      • Microsoft Azure, VMware CloudFoundry, ...
      • * 21 Web service APIs as of April 2011
  • 21. Two themes
    • Academic clouds: public or private?
      • Theme 1 : save money or improve research?
      • Theme 2: cloud user or cloud provider?
    • Capability
      • Cloud accelerates and enables new research
      • Scale that can’t be achieved any other way
    • Cost
      • Will private cloud cost less? Is that the main goal?
      • Have hidden costs been accounted for?
      • Cost-associativity allows bursty use, encourages spontaneity, but needs fine grained metering
  • 22. Two themes
    • Academic clouds: public or private?
      • Theme 1 : save money or improve research?
      • Theme 2: cloud user or cloud provider?
    • Cloud provider research may require private cloud
      • Security, energy, bare-metal, cloud provisioning, ...
      • But, still need cloud users (customers) to drive/validate
      • Need public-cloud-level APIs, service reliability
    • Cloud user
      • Big data may impede some public-cloud-ready apps
      • Exotic architectures (SSD, in-memory DB, ...)
      • Regulatory issues....
  • 23. Summary
    • Public cloud shows how to “move slider” between insourcing & outsourcing
    • Unlikely to compete on cost with very large scale public clouds
    • So, how much can/should you outsource...
      • ...for technical reasons (types of research possible)?
      • ...for regulatory reasons (data privacy, etc.)?
    • Remember the non-obvious costs
      • Metering & billing, esp. for shared overheads
      • Keeping up with the ecosystem
  • 24. Thanks!
    • UC Berkeley Reliable Adaptive Distributed Systems Lab & Affiliates
    • UC Cloud Computing Task Force
    • Andy Powell & Eduserv
    RAD Lab Team in 2009