State of the Cloud presentation from Interop 09 Enterprise Cloud Summit


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State of the Cloud presentation from Interop 09 Enterprise Cloud Summit

  1. 1. State of the Cloud Enterprise Cloud Summit New York, NY November 17, 2009 Wednesday, November 18, 2009 Good morning, and welcome to ECS Some introductory thoughts on clouds, how we got here, where we’re going.
  2. 2. Tagging { #interop #ecs Wednesday, November 18, 2009 If you want to post pictures or comments, use #Interop and #ECS
  3. 3. Wednesday, November 18, 2009 If you want to post pictures or comments, use #Interop and #ECS
  4. 4. A bit about Bitcurrent Analysis and research of emerging technologies Cloud computing, web performance, human/computer interaction, emergent communications technology Wednesday, November 18, 2009
  5. 5. Peak of inflated Visibility expectations Plateau of productivity Slope of enlightenment Technology Trough of trigger disillusionment Time Wednesday, November 18, 2009 You’re probably all familiar with Gartner’s “Hype curve.” I’m sorry to say that, according to them, we’re at the apogee -- the peak of inflated expectations. Disillusionment awaits us. Then, of course, clouds will become a part of our lives.
  6. 6. The stages of grief The loss of traditional IT. Wednesday, November 18, 2009 I like to look at a slightly different curve. It’s the stages of grief, as IT loses its traditional environments. This loss comes from a number of things: - An inability to compete on cost versus the single-mindedness of cloud providers - The changing patterns of data, storage, and computation that put users everywhere and make workloads bursty - A newfound desire for agility and faster pace of change and experimentation
  7. 7. Visibility Bargaining cept ance Ac Anger Denial Depression You are Time here Wednesday, November 18, 2009
  8. 8. 100 75 50 )β β d 3) ce C2 t a ced s CS (S ne nc oun (E es ag ann er β rE zo n e β v d vic ou d ar SM CDN Ser lin nsta nn e ou fo ice e vic ity sti s nc er Ja oud in re Cl B e bil rv nt ws er 25 cL eS ou to ed en te Se eS a fro do kS n ail pu ay erv em ag an DB eu Av ela om loc or ,W i DB Qu St al IP, cB : R an cC g, LA ion ple ple ple tic sti sti 2S lat las ca im m im Ela Ela AW Re EC Cl Si :S :S :E :S g: g: v: v: n: t: t: ar ar c No No Au Au De Oc Oc M M M M 0 2005 2006 2007 2008 2009 Wednesday, November 18, 2009 Here’s a rough timeline of cloud computing’s growth in recent years. First: Mentions on Google Insight Then: Gartner’s Hype Curve, which they claim is a 2-5 year cycle Then: The introduction of various services from Amazon Then: Big computing companies that have inked deals with Amazon Then: A history of some of the vendors and their initial cloud products
  9. 9. What we agree on: Wednesday, November 18, 2009 In the last year, we’ve reached agreement on several things.
  10. 10. We have a taxonomy. Wednesday, November 18, 2009 For starters, we have a taxonomy -- which is good. Sadly, however, we abandon it at a moment’s notice.
  11. 11. Private Public w a nt you k SaaS If t al ck PaaS to , pi PaaS u d s t. IaaS c lo fi rs IaaS on e Managed hosting Wednesday, November 18, 2009 If someone wants to have a conversation with me about clouds, they need to pick a tier, and a private or public model. Then we can compare facts.
  12. 12. We (sort of) agree on how to classify things. Wednesday, November 18, 2009
  13. 13. Word processing SaaS (Google Apps, Office Standard app, Copy content Live, Basecamp, no differentiation Freshbooks, Wufoo) Support ticketing “Flavored” PaaS (Quickbase, Bungee, Custom app, key Rewrite process, Webex business process Connect) Rewrite code Agnostic PaaS Intranet site (App Engine, Heroku, Application code Reasonablysmart, Port code Azure) JBoss Server Infrastructure Application Port to VM/AMI cloud (ECS, Joyent, instance Rackspace, Azure) Wednesday, November 18, 2009 What you want to move into the cloud will affect how you do it and what you move to.
  14. 14. Cloud responsibilities: Who owns what layer? IT user Cloud ? App logic APIs This is why clouds are new Operations Architecture Hardware Wednesday, November 18, 2009
  15. 15. This is managed hosting IT user Cloud User defines it, writes App logic code Users talk to APIs components directly Operations User runs the machines User designs how things Architecture fit together Hardware Service provider owns it Wednesday, November 18, 2009
  16. 16. This is strategic outsourcing IT user Cloud User defines it, writes App logic code Users talk to APIs components directly User runs VMs, not Operations physical ones User designs how things Architecture fit together Hardware User owns it Wednesday, November 18, 2009
  17. 17. This is an IaaS cloud IT user Cloud User defines it, writes App logic code Users talk to APIs components directly User runs VMs, not Operations physical ones User chooses from Architecture predefined menu Hardware Service provider owns it Wednesday, November 18, 2009
  18. 18. This is a PaaS cloud IT user Cloud User defines it, writes App logic code Users only talk to well- APIs defined services User runs VMs, not Operations physical ones User chooses from Architecture predefined menu Hardware Service provider owns it Wednesday, November 18, 2009
  19. 19. This is SaaS IT user Cloud Service provider writes App logic and maintains it Users only talk to well- APIs defined services User runs VMs, not Operations physical ones User chooses from Architecture predefined menu Hardware Service provider owns it Wednesday, November 18, 2009
  20. 20. This is a private cloud Internal clients Internal IT User defines it, writes App logic code Users talk to Users only talk to well- APIs components directly defined services User runs VMs, not Operations physical ones User chooses from Architecture predefined menu Hardware Service provider owns it Wednesday, November 18, 2009
  21. 21. We’ve stopped denying it. Wednesday, November 18, 2009
  22. 22. Denial: Just timesharing all over Insulates components Amazon S3 turns SOA from functionality storage into a through consistent APIs service Reduces minimum order Buy a slice for Virtualization quantity; turns physical things into logical ones just an hour Means users are OK with Standardization a menu of predefined LAMP, Rails, etc. configurations Increases the human-to- Automation 10x enterprise machine ratio & drives marginal cost towards 0 efficiency ratios Wednesday, November 18, 2009 This is the ranting of luddites and server-huggers Of SOA, the insulation of components by consistent APIs Of virtualization,which - Reduces the minimum order quantity - Makes automation possible by making the physical logical Of platform standardization
  23. 23. Denial: just for startups “[There are] 60,000 different customers across the various Amazon Web Services, and most of them are not the startups that are normally associated with on-demand computing. Rather the biggest customers in both number and amount of computing resources consumed are divisions of banks, pharmaceuticals companies and other large corporations who try AWS once for a temporary project, and then get hooked.” Wednesday, November 18, 2009 Even as early as last year, Amazon reported that the majority of its users and its compute cycles were consumed by enterprise customers.
  24. 24. 45267!6@:D/B!D?!@9B::>!H227#! Denial:037!HB::D3H!KB::B9 they’re not reliable .@9I%" JB@I%" -%%#%%& ""#"%& ""#,%& ""#+%& ""#*%& ""#)%& ""#(%& ""#'%& ""#$%& ./0123 4;;!4<; =>?@0AB =2??2!4<; E4$!F9B5037 G22H5B G22H5B!?D:B G22H5B!?D:B 452678923: .C0/0D .@@E3HD3B ! ! L?D3H!M2957IMD7B!/23D:29D3H!?:0:D23?!K>!N0:AO=26?B Cloud Encounters, Peter van Eijk, Wednesday, November 18, 2009 We have decent evidence that they can be relied on. Peter van Eijk is presenting this data at CMG next month, but gave us an early look at some performance benchmarking he’s done on Watchmouse, a European testing platform.
  25. 25. "#$$%&'!'()%*!+,#)!-.! '#!/)01#$!"2#34+,#$' Denial: they’re slow <; @< @; !"##"$%&'()$*+'*&'((%&+ ?< ?; >< >; =< =; < ; >@ABA >DABA >CABA ?;ABA =ACA ?ACA <ACA DACA CACA ==ACA =?ACA =<ACA =DACA >;;C >;;C >;;C >;;C >;;C >;;C >;;C >;;C >;;C >;;C >;;C >;;C >;;C Connect times to Amazon Cloudfront from NYC 5%''%,!6%%,($7!0$48#,!9%''%,!,#3'($7: Cloud Encounters, Peter van Eijk, Wednesday, November 18, 2009 ! ! Peter’s data also shows that Amazon is making significant headway with infrastructure upgrades that improve performance.
  26. 26. Reality is setting in. Wednesday, November 18, 2009
  27. 27. Reality: Cloud operators have an unbeatable cost advantage. Wednesday, November 18, 2009 At this point, it’s hard to argue that cloud operators will win on a cost basis alone.
  28. 28. How to think about costs 800,000 Variable Fixed Upfront 600,000 Cost 400,000 200,000 0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Wednesday, November 18, 2009 Any cost model consists of three kinds of spending: Upfront money spent to kick things off; fixed spending that doesn’t change whether you sell one or a billion units; and variable spending associated with the amount you sell.
  29. 29. IT costs: Upfront Capital investment (often, “capex”) Don’t overlook rewriting, retooling, retraining, data migration For many enterprises, this is just the cost of periodic upgrades. They already have equipment. Wednesday, November 18, 2009
  30. 30. IT costs: Fixed Happen no matter what; a measure of leanness May be shared with other activities, and therefore not eliminated (this is often invoked in defense of jobs) Wednesday, November 18, 2009 Then there are the fixed IT costs that you can’t avoid. Clouds can drop these, but if you have IT running internal systems, they won’t magically evaporate when things move to the cloud. What’s more, clouds mean new tasks for IT -- things like provisioning, managing policy, and so on.
  31. 31. IT costs: Variable Tied to delivery; a measure of efficiency 500 Needs to be less Servers per sysadmin 375 than the resulting revenue or you’ll be 250 called a cost center 125 Enterprises underestimate the true 0 costs of service delivery Enterprise Cloud provider Barry Lynn of 3Tera Wednesday, November 18, 2009 The variable costs are where clouds are really strong. This stuff is the costs that increase with service delivery volumes. Cloud operators can handle 500-1000 servers per person (they have to!) and completely automate everything. They also focus on cost measurement and accounting, which is a luxury for many enterprises but a necessity for clouds. Management software is an afterthought for many IT departments; but it’s a competitive advantage for cloud operators.
  32. 32. Clouds might seem pricey today £30,000,000 £22,500,000 Final score: DC: £15M Cloud: £26M £15,000,000 After year 3, cloud costs exceed DC £7,500,000 Even with 3-year refresh cycles of 30% DC remains cheaper £0 Start up cost Year 2 Year 4 Year 6 Year 8 Year 10 Data Centre Cloud 2009 IDC analysis of running 100% of a big enterprise’s IT in-house vs on-demand Used with permission. Copyright (c) IDC Wednesday, November 18, 2009 A naive look at clouds (intentionally naive, we should point out) from IDC says that clouds are expensive
  33. 33. But we’re deluded £50,000,000 Year 6 requires build- out for new facility + expensive refresh due DC reaches space to limited space £37,500,000 capacity in year 3, 50% refresh to high- end servers needed £25,000,000 £12,500,000 Cloud costs are dynamic so even if bad decisions are made initially, capacity can be ramped up linearly £0 Start up cost Year 2 Year 4 Year 6 Year 8 Year 10 Data Centre Cloud 2009 IDC analysis of running 100% of a big enterprise’s IT in-house vs on-demand Used with permission. Copyright (c) IDC Wednesday, November 18, 2009 Remember: Google gets 38% server utilization, with insane effort. So this is likely unattainable. What’s more, cloud providers are competing, and may (assuming interoperability of some kind) reduce their costs, too.
  34. 34. Wednesday, November 18, 2009 Just how big are clouds? Consider that in July 2008, Microsoft revealed that it had 96,000 servers at the Quincy facility, consuming "about 11 megawatts" More than 80% dedicated to Microsoft's Live Search and the remaining for Hotmail In August, a really good discovery was posted to a blog called "":  a screen shot of a software dashboard that illustrates power consumption and server count at each of Microsoft's fifteen data centers, caught in a Microsoft video posted to their web site.
  35. 35. Are you negotiating with cities & power companies? “...Microsoft pays an annual utility bill just north of $13 million, which translates to just over 3.8 cents/kwh as opposed to 5.7 cents/kwh for the ELP rate...” Wednesday, November 18, 2009 Consider a San Antonio, Texas facility from Microsoft. if the data center takes the full load of 44 megawatts at a 90% load factor, Microsoft pays an annual utility bill just north of $13 million, which translates to just over 3.8 cents/kwh as opposed to 5.7 cents/kwh for the ELP rate.  To prove that these assumptions are in the ballpark, public documents from another SLP customer in the San Antonio area reveal that its overall utility rate is 3.7 cents per kwh.
  36. 36. Three laws Wednesday, November 18, 2009
  37. 37. Wednesday, November 18, 2009 First, consider silicon. Look at the cost/capacity tradeoff of computing, as described by Moore’s Law.
  38. 38. Wednesday, November 18, 2009 Then think about another form of sand – glass. Then look at the cost/capacity tradeoff of networking. Netflix pays $0.06 to send a movie over the Internet today, and will pay $0.03 next year.
  39. 39. Wednesday, November 18, 2009 Finally, think about iron. And consider storage – which is dropping just as quickly.
  40. 40. The cloud trifecta Wednesday, November 18, 2009 This trifecta of computing, bandwidth, and storage are driving costs down dramatically. Every time Google builds a data center, it can do more than the last one did.
  41. 41. Everything will be free.* *Some restrictions apply. Wednesday, November 18, 2009 Cloud computing is on a breakneck ride to zero marginal costs because of sand, iron, and glass. This means the raw materials of clouds will be free -- or too cheap to bill -- for many of us. (if you want to know more about this, see Chris Anderson’s Free)
  42. 42. So you won’t be building your own data centers 70% of the Global 1000 must “Modify their data center facilities significantly” by 2012 Increase energy from 35 to 70 watts/sq. ft (sometimes up to 300 watts) Gartner says to Monitor energy use Quantifying all capital and operation changes needed Deploy virtualization and workload management tools Wednesday, November 18, 2009 Energy is a huge issue. Even Gartner’s recommendations for saving energy will only temporarily solve the problem at hand, because energy costs will have to be cut by more than 50% in order to keep up
  43. 43. Wednesday, November 18, 2009 This is a GM prototype of a car that drives itself. It’s actually green technology. Know why? Because in the end, the greenest thing you can do for a car isn’t fuel: It’s making it not crash as much. If cars didn’t crash, we could get rid of most of their weight, which in turn would make them efficient. It turns out that IT is the key to efficiency.
  44. 44. If you’re using others’ servers, you’ll get VMs. Wednesday, November 18, 2009 Let’s resign ourselves to the fact that we’ll get hardware from someone else (there’s a reason Intel is investing in cloud companies like Joyent, remember.) So how will that work? You’re going to get virtual machines, because that’s how the operators keep the costs low. IT and management, not cheaper machines, is the key to efficiency.
  45. 45. Reality: Clouds are part of the IT toolbox Wednesday, November 18, 2009
  46. 46. Wednesday, November 18, 2009 Clouds let IT focus on things that actually add business value. Very few companies have a competitive advantage because of their hardware infrastructure.
  47. 47. Wednesday, November 18, 2009 And they eliminate many of the tasks you really didn’t want to do anyway.
  48. 48. Reality: Security is a pro and a con. Wednesday, November 18, 2009
  49. 49. New kinds of attack The best infosec people Third-party access More automation Traveling across wires High-end tools Shared infrastructure Billing to catch use spikes Wednesday, November 18, 2009 With hypervisors, other people involved, wires to cross, and so on, there are new vectors for attack. Those have to be compared to the more rigorous standardization that a cloud is likely to subject things to.
  50. 50. Reason to avoid clouds 23% Reason to move to clouds 43% No opinion 34% Wednesday, November 18, 2009 In a study commissioned by PEER 1, users reported security as a big impediment to cloud adoption -- and a reason for doing so!"
  51. 51. Reality: It’s about services, not machines Wednesday, November 18, 2009 While virtual machines were easy to understand and embrace, we’ve finally realized that it’s the services, not the machines, that matter.
  52. 52. Embracing clouds means giving up architectural opinions. Wednesday, November 18, 2009
  53. 53. SOA may matter more than virtualization I used to think here... SimpleDB RDB Elastic MapReduce EC2 SQS Loadbalance CloudFront S3 I think out here Wednesday, November 18, 2009 What started out as pay-by-the-drink storage (S3) and computational processing (EC2), now includes a simple database (SimpleDB), a content delivery network (CloudFront), and computer-to-computer messaging (SQS). Most recently, Amazon added a web-scale data processing engine with Amazon Elastic MapReduce. (It is a framework for accessing data stored in file systems and databases). It allows developers leverage Amazon’s cloud computing power by creating applications which process huge reservoirs of data (conveniently stored in Amazon S3) in parallel. Developers become systems integrators
  54. 54. Reality: Clouds are ubiquitous. Wednesday, November 18, 2009
  55. 55. ;<=7>?7;;?7@A@ !+)&98(#C!F707B7 !$++$),-"#.)(%"(-"##,-% ;@O7;H=7?=7@A@ !B:3%*5-8:C!/*%)*5,8'-3 ; <; =;; =<; >;; ><; ?;; ?<; ! !+#2*')8(*'C!.*':851 Cloud "-#7/!.*+! processing is $,(-$.(//01/21345//)**+*$,(#(6$$6#"(-$.( ! -$.(/01/3/789(:;(<79(<=< $D F%1!G%*-*$/! !.$N,&'C!J5*,8'- ! -$.(/01/3/789(:;(<>;(<=< (+!0%*--:!A*0! !P5#'&'(*'C!/*%)*5,8'-3 ! everywhere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loud Encounters, Peter van Eijk, ! !/8(8'#C!Q828' ! Wednesday, November 18, 2009 ! !08'!W58'9&39#C!F707B7 ! !0)8'()8&C!+)&'8 ! !0&'(82#5*C!0&'(82#5* !0G-'*GC!B$3%58,&8 VA7@;?7H=7@A@ !B:3%*5-8:HC!/*%)*5,8'-3 ! !L85&3C!W58'9* ! VA7@;?7AH7@A@ ! !+#,#('*C!P*5:8'G !X&,,*C!W58'9*
  56. 56. Reality: He who owns the storage, owns the computation. Wednesday, November 18, 2009
  57. 57. It’s all about the data Wednesday, November 18, 2009 Data is the most important part of a cloud. MS fellow Jim Gray, in his 2003 analysis, said that compared to the cost of moving bytes around, everything else is effectively free. The economics of storage services like Flickr don’t hold up well to churn.
  58. 58. Moving data’s not easy Speed Rent $/TB Context $/Mbps Time/TB Mbps $/month Sent Home phone 0.04 40 1,000 3,086 6 years Home DSL 0.6 50 117 360 5 months T1 1.5 1,200 800 2,469 2 months T3 43 28,000 651 2,010 2 days OC3 155 49,000 316 976 14 hours OC 192 9600 1,920,000 200 617 14 minutes 100 Mpbs 100 1 day Gbps 1000 2.2 hours Source: TeraScale Sneakernet, Microsoft Research, Gray et. al 58 Wednesday, November 18, 2009 One dirty secret of cloud computing is that from a cost perspective, everything’s pretty much free compared to the price of moving bytes around. This means you can no more build an app that’s “half cloud” than you can be “half pregnant.”
  59. 59. Wednesday, November 18, 2009
  60. 60. Reality: The big guys are here. Wednesday, November 18, 2009 Legitimacy, at the cost of FUD and a slow-down of experimentation because big vendors can promise.
  61. 61. IBM Replacing Global Services Architecture defines clouds Wednesday, November 18, 2009
  62. 62. Microsoft SaaS cannibalizes existing software addiction Wednesday, November 18, 2009
  63. 63. AT&T It’s about data centers and connectivity Wednesday, November 18, 2009
  64. 64. We’re stalling. Wednesday, November 18, 2009
  65. 65. What’s the consensus? (the no clear direction problem) Wednesday, November 18, 2009
  66. 66. No straight answer 38% 47% ITI “Unsure about adopting “Won’t consider the cloud in cloud services” next 12 months” F5 Networks 82% “In trial, implementation, or use of public clouds” “Implementing cloud services” 60% 8% 29% “Actively researching (cloud on “No interest in the cloud” radar)” 0% 25% 50% 75% 100% Wednesday, November 18, 2009
  67. 67. What’s included? (the roofrack problem) Wednesday, November 18, 2009
  68. 68. Wednesday, November 18, 2009
  69. 69. Wednesday, November 18, 2009
  70. 70. Wednesday, November 18, 2009
  71. 71. Too much choice (the wait it out problem) Wednesday, November 18, 2009
  72. 72. Wednesday, November 18, 2009 Jim Sivers reminded me recently of the paradox of choice. Sheena Iyengar has been studying choice. For her research paper, “When Choice is Demotivating”,They set up a free tasting booth in a grocery store, with six different jams. 40% of the customers stopped to taste. 30% of those bought some. A week later, they set up the same booth in the same store, but this time with twenty-four different jams. 60% of the customers stopped to taste. But only 3% bought some!
  73. 73. 60 45 30 15 0 Stopped to taste Actually bought some 6 jams 24 jams Wednesday, November 18, 2009 Both groups actually tasted an average of 1.5 jams. So the huge difference in buying can’t be blamed on the 24-jam customers being full. Lessons learned: Having many choices seems appealing (40% vs 60% stopped to taste) Having many choices makes them 10 times less likely to buy (30% vs 3% actually bought) Surgeon Atul Gawande found that 65% of people surveyed said if they were to get cancer, they’d want to choose their own treatment. Among people surveyed who really do have cancer, only 12% of patients want to choose their own treatment.
  74. 74. General population Cancer patients 13% 35% 65% 87% Choose their own treatment Have others choose Wednesday, November 18, 2009 Surgeon Atul Gawande found that 65% of people surveyed said if they were to get cancer, they’d want to choose their own treatment. Among people surveyed who really do have cancer, only 12% of patients want to choose their own treatment.
  75. 75. Striking a balance How we move ahead Wednesday, November 18, 2009
  76. 76. Focus on service architectures Wednesday, November 18, 2009
  77. 77. Composed designs replace component architectures High performance Compliant “Embarrassingly distributed” Bursty/seasonal Resilient & highly available Scalable but eventually consistent Wednesday, November 18, 2009
  78. 78. Have a hybrid/private strategy Wednesday, November 18, 2009
  79. 79. “Private” and “hybrid” concepts emerge Private cloud: On-premise infrastructure with cloud-like properties Hybrid cloud: Policy-driven combination of on-premise and on-demand components Virtual private cloud: On-premise privacy on someone else’s machines Wednesday, November 18, 2009
  80. 80. Compute task (service cloud) Virtual machine (infrastructure cloud) Always on Can be done Always in premise anywhere cloud Load/pricing engine Private Partner access Compliance- Testing enforced Proximity to cloud Training services (storage, Policy engine Need to track and Prototyping CDN, etc.) audit Batch processing Massively grid/ Legislative Seasonal load parallel (genomic, Data near local modelling) computation Wednesday, November 18, 2009 Going forward, we’ll see hybrid on-premise/on demand hybrid clouds that can intelligently move processing tasks between private an public infrastructure according to performance requirements, pricing policies, and security restrictions.
  81. 81. Reconsider what’s possible Wednesday, November 18, 2009
  82. 82. Better economics Developer empowerment Pay-per-use pricing Self-service portal No capital investment Infrastructure managed by cloud provider No long term contracts Developer-ready framework Ideal for spiky applications For all levels of developers Optimized for Web 2.0 apps Cheap test and dev Scales easily* One button deploy * Easy scaling may not be included James Staten, Forrester Wednesday, November 18, 2009 Clouds promise a lot: James Staten of Forrester loves clouds, not only for the economies of scale they offer, but also for the way in which they empower developers to build and experiment by speeding up the IT cycle time.
  83. 83. Enterprise tech Disruptive tech Minimize maintenance costs Do new things Cooling Perform mission tasks that were not Electricity able to achieve otherwise Servers maintenance, backups, etc. New tools like Hadoop and Map Elasticity and scalability Reduce allows for amazing processing Massive scale leads to true Speed up the organization economies of scale Faster, cheaper innovation Eliminate need to build for Transform how gov does business infrequent peaks Prototyping enablement Make capacity available on demand Publish databases Ops cost reduction Reduce start-up Flat data sets Work differently Streamlined data management Realtime collaboration Data availability - enabling Ubiquitous access to unlimited information generation amount of computing power DR cost reduction Ubiquitous access to unlimited amount of storage Rod Fontecilla, Booz, Allen, Hamilton Wednesday, November 18, 2009
  84. 84. • 60 seconds per page Desktop EC2 • 200 machine Pages 17,481 17,481 instances Minutes/page 1 1 • 1,407 hours of virtual # of machines 1 200 machine time Total minutes 17,481 • Searchable database Total hours 291.4 26.0 available 26 hours Total days 12.1 1.1 later • $144.62 total cost Wednesday, November 18, 2009 One of the most interesting uses of cloud computing is time dilation. Okay, not really, but close: The Washington Post, needed to get all 17,481 pages of Hillary Clinton’s White House schedule scanned and searchable quickly. Using 200 machines, the Post was able to get the data to reporters in only 26 hours. In fact, the experiment is even more compelling: Desktop OCR took about 30 minutes per page to properly scan, read, resize, and format each page – which means that it would have taken nearly a year, and cost $123 in power, to do the work on a single machine.
  85. 85. Two kinds of data center Really big data centers for really big Requires lots of communication problems between servers, so network propagation affects computation Tens of thousands or more servers speed Tens of Mega-Watts of power at Micro data centers for “embarrassingly peak distributed” applications Aimed at massive data analysis Thousands of servers applications (search indexes, social media, genomics) 100s of kilowatts. Variety of workloads Aimed at highly interactive apps (Interactive, office productivity apps) Huge amounts of fast RAM Placed close to populations to Massive numbers of CPU cycles minimize network transit impact High-volume disk I/O bandwidth The Cost of a Cloud: Research Problems in Data Center Networks Albert Greenberg, James Hamilton, David A. Maltz, Parveen Patel Microsoft Research, Redmond, WA, USA Wednesday, November 18, 2009
  86. 86. Think about risk in the context of openness Wednesday, November 18, 2009
  87. 87. Sharing > Protection Drew Bartkiewicz, The Hartford, quoted in Unseen Liability Wednesday, November 18, 2009 According to Drew “Bartievitz” of the Hartford, there’s a shift in the value of information assets underway.
  88. 88. Embrace cloud technology even if you don’t use clouds Wednesday, November 18, 2009
  89. 89. An example: eventual consistency Wednesday, November 18, 2009
  90. 90. Clouds as Clouds as peripherals IT strategy Wednesday, November 18, 2009 Most of the enterprises I’ve spoken with use clouds as peripherals. In the same way we used to plug peripherals into our computers, enterprises plug clouds into their IT. They might have it for backup, or messaging, or content delivery, or for a specific business process. But to really harness the power of cloud computing, enterprises need to embrace it as more than just a bunch of things to plug into the organization. It needs to become part of their strategy.
  91. 91. Support Contracts UI Language Computing Storage Delivery Protocol API Policies Onboarding Wednesday, November 18, 2009 You can target a vertical. There are always ways to specialize within a specific industry. This isn’t about the computing -- as we’ve seen, this is a commodity. But you can <click> focus on a specific language or protocol, <click> UI or API, <click>, set of contracts and policies, <click>, or even support and onboarding. Every industry or target customer has specific needs. Maybe it’s the AMQP protocol, or HTML 5 optimization, or JavaScript code, or long contract terms, or high-touch support for small businesses.
  92. 92. Worry about user experience, billing Wednesday, November 18, 2009
  93. 93. What user experience can you afford? Wednesday, November 18, 2009
  94. 94. Traffic (requests/sec) Delay (in seconds) = Capactity (# of machines) Wednesday, November 18, 2009 There’s a basic equation in computing. Performance equals traffic divided by capacity. Put another way, more users and something gets slower. More machines and something gets faster.
  95. 95. Wednesday, November 18, 2009 This is an example of that relationship. As usage grows, performance gets worse.
  96. 96. Wednesday, November 18, 2009 Normally, IT adds capacity to a system and things get better.
  97. 97. Traffic (requests/sec) Delay (in seconds) = Capactity (# of machines) ∞ Wednesday, November 18, 2009 But when if the capacity is infinite?
  98. 98. Wednesday, November 18, 2009 Then you set user experience (“under 1 second”) and the elastic platform adds capacity as needed. The only problem? The bill at the end of the month!
  99. 99. 100 75 50 ROI, TCO, Designs & Taxonomies Business Policy & business best & layers strategy standards cases practices 25 0 2008 2009 2010 2011 2012 What is Why How do I What new What must the cloud? should I use it? things are I still run use it? possible? in-house? Wednesday, November 18, 2009 Here are my predictions for the next few years, and what you’ll see at conferences, in the press, and in the boardroom.
  100. 100. Different Clouds for Different Folks Ian Knox (Skytap), Lew Moorman (Rackspace), Sesh Murthy (IBM), Scott Ryan (Asankya) The Risks of On-Demand Computing Anthony Arnott (Trend Micro), Drew Bartewicz (The Hartford), Marc Lindsey (Levine, Blaszak, Block & Boothby LLP) What's Working, What's Not: A Report from Cloud Adopters Colin Hostert (Grooveshark), Geir Magnusson (Gilt), Dominic Preuss (FiLife), Vince Stephens (Taser) Cloud Interoperability: Do We Need It? What Would it Look Like? Chris Brown (Opscode), Jason Hoffman (Joyent), John Willis (Zabovo) Cloud Computing Roadmaps Ken Comee (Cast Iron Systems), Morris Panner (OpenAir), Randy Bias (Cloudscaling) Wednesday, November 18, 2009
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