PRESENTED TO : DR. NADIR SHAH
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OutlineWhat is it?Why now?Cloud killer appsEconomics for usersEconomics for providersChallenges and opportunitiesImplications
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What is Cloud Computing?Old idea: Software as a Service (SaaS)
Def: delivering applications over the InternetRecently: “[Hardware, Infrastrucuture,
Platform] as a service”Poorly defined so we avoid all “X as a service”
Utility Computing: pay-as-you-go computingIllusion of infinite resourcesNo up-front costFine-grained billing (e.g. hourly)
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Why Now?Experience with very large datacenters
Unprecedented economies of scaleOther factors
Pervasive broadband InternetFast x86 virtualizationPay-as-you-go billing modelStandard software stack
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Spectrum of CloudsInstruction Set VM (Amazon EC2, 3Tera)Bytecode VM (Microsoft Azure)Framework VM
Google AppEngine, Force.com
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EC2 Azure AppEngine Force.com
Lower-level,Less management
Higher-level,More management
Cloud Killer AppsMobile and web applicationsExtensions of desktop software
Matlab, MathematicaBatch processing / MapReduce
Oracle at Harvard, Hadoop at NY Times
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Unused resources
Economics of Cloud Users
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• Pay by use instead of provisioning for peak
Static data center Data center in the cloud
Demand
Capacity
Time
Demand
Capacity
Time
Unused resources
Economics of Cloud Users
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• Risk of over-provisioning: underutilization
Static data center
Demand
Capacity
Time
Economics of Cloud Users
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• Heavy penalty for under-provisioning
Lost revenue
Lost users
Demand
Capacity
Time (days)1 2 3
Demand
Capacity
Time (days)1 2 3
Demand
Capacity
Time (days)1 2 3
Economics of Cloud Providers5-7x economies of scale [Hamilton 2008]
Extra benefitsAmazon: utilize off-peak capacityMicrosoft: sell .NET toolsGoogle: reuse existing infrastructure
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ResourceCost in
Medium DCCost in
Very Large DCRatio
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
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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Growth ChallengesChallenge 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 on Distributed VMs
Scaling quickly Invent Auto-Scaler that relies on ML; Snapshots
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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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Short Term ImplicationsStartups and prototypingOne-off tasks
Washington post, NY TimesCost associativity for scientific applicationsResearch at scale
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Long Term ImplicationsApplication software:
Cloud & client parts, disconnection toleranceInfrastructure software:
Resource accounting, VM awarenessHardware systems:
Containers, energy proportionality
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