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Above the Clouds: a Berkeley View of Cloud Computing<br />Presented by:<br />	Pat Helland<br />	Partner Architect (SQL SIA...
 Much of this paper’s content is well known to the folks working   in the cloud computing space.
 Hats off to the folks from Berkeley for such a crisp and thoughtful paper!</li></li></ul><li>Outline<br />Introduction<br...
Cool Paper Published on February 10, 2009<br />The UC Berkeley RAD Lab<br />Berkeley RAD Lab<br />(Reliable Adaptive Distr...
My Experiences with “Cloud Computing”<br />Over 25 Years Working in Distributed Computing<br />Tandem Computers(1982-1990)...
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
What Is Cloud Computing?<br />Cloud Computing: App and Infrastructure over Internet<br />Software as a Service:           ...
Economies of Scale and App Model<br />Economies of Scale for Humongous Datacenters<br />Electricity<br />Network<br />Oper...
Obstacles and Opportunities<br />
Elasticity, Risk, and User Incentives<br />Services Will Prefer Utility Computing to a Private Cloud When:<br />Demand Var...
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
The Dream of Cloud Computing<br />Integrated Circuit<br />Foundries<br />Utility Computing<br />Semiconductor Fabs Expensi...
Cloud Computing: Confusion<br />The interesting thing about cloud computing is that we’ve redefined Cloud Computing to inc...
Cloud Computing: Clarifications<br />“Above the Clouds” Paper from UC Berkeley RAD Lab<br />Goals for the Paper:<br /><ul>...
 Compare Cloud and Conventional  Computing
 Identify Top Obstacles & Opportunities</li></ul>Paper Shaped by:<br /><ul><li> Working Since 2005 in RAD Lab
 Users of Amazon AWS for 1 Year
 6 Months Brainstorming about Cloud</li></ul>Questions to Answer:<br />What New Economic Models Are Enabled by Cloud?  How...
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
Utilities, Services, & Clouds: Oh, My!!<br />Cloud Computing: Apps Delivered as Services over the Internet and the Datacen...
The New Perspective of Hardware Resources<br />3 New Aspects to Cloud Computing<br />All 3 Aspect Are Required to Succeed<...
Power and Cooling Is Expensive!<br />The Infrastructure for Power and Cooling Costs a LOT<br />Infrastructure PLUS Energy ...
Location and Scale: It’s Easier to Ship Data than Power!<br />Datacenters Are Popping Up in Surprising Places<br />Quincy,...
We Already Needed a Huge Datacenter…<br />Building a Very Large-Scale Datacenter Very Is Expensive<br />$100+ Million (Min...
Why Be a Cloud Provider?<br />Make a Lot of Money<br />Huge datacenters cost 5-7X less for computation, storage, and netwo...
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
New Technology Trends & Business Model<br />Web 2.0<br />Low-Touch, Low-Margin, Low-Commitment<br />Web 1.0<br />High-Touc...
New Application Opportunities<br />Gray’s Observation: <br />Jim Gray Looked at Trends in 2003<br />Wide-Area Networking F...
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
A Spectrum of Application Models<br />Constraints in the App Model  <br />Automated Management Services<br />More Constrai...
  Building a Web App in C++ Is a Lot of Cumbersome Work
  Ruby-on-Rails Hides the Mechanics but Only If You Follow    Request/Response and Ruby’s Abstractions</li></ul>More-Const...
Vendors and Virtualized Resources<br />
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
An Overview of the Economic Shift<br />Observations about Cloud Computing Economic Models<br />Fine-Grain and Elastic Econ...
Elasticity of Resources in Cloud Computing<br />Cloud Computing: Add or Remove Resources as Needed<br />In Amazon’s EC2, O...
Elasticity: Do the Math!!<br />Example: Elasticity<br />Assume Our Service:<br />Peaks at 500 Servers at Noon<br />Trough ...
Elasticity: Risks of Under-Provisioning<br />Under-Provisioning #1<br />Potential Revenue (Shaded Area) Is Sacrificed<br /...
Shifting Risk to the Cloud Provider<br />Example #1: Animoto<br />When Launched Surged from 50 Servers to 3500 in 3 Days<b...
My Favorite Queuing Theory Equation<br />Expected<br />Response<br />Time<br />Minimum Response Time<br />1 - Utilization<...
One More Look at the Cost Model<br />How Much You Make Total in a “Pay as You Go” Cloud<br />How Much You Make Per User Ho...
Comparing Costs: Should I Move to the Cloud?<br />In 2003, Jim Gray Calculated What $1 Purchased<br />How Much Disk for $1...
Costs of Computing: On-Premise versus the Cloud<br />Power, Cooling, & Physical Plant Cost<br />Operations Cost<br />It Ap...
Cloud Is Mostly Driven by Money<br />Economics of Cloud Computing Are Very Attractive to Some Users<br />Cloud Computing W...
Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time ...
Top 10 Obstacles and Opportunities<br />
Organizations Worry: Will Cloud Computing Be Highly Available?<br />Existing Web & SaaS Offerings (e.g. MSN, Google, Amazo...
#2 Obstacle: Data Lock-In<br />Cloud Storage Providers (So Far) Have Distinct APIs<br />Difficult (Impractical) to Store D...
#3 Obstacle: Data Confidentiality and Auditability<br />“My sensitive corporate data will never be in the cloud!”<br />Cur...
#4 Obstacle: Data Transfer Bottlenecks<br />Problem: At $100 to $150 per Terabyte Transferred, Data Placement and Movement...
#5 Obstacle: Performance Unpredictability<br />When Does Sharing Cause Problems with Performance?<br />Sharing CPU and Mai...
Obstacles #6, #7, #8, & #9<br />Obstacle # 6: Scalable Storage<br />Need Storage that Can Scale-Up and Scale-Down<br />It ...
#10 Obstacle: Software Licensing<br />Software Licenses Typically Restrict which Computers May Use the Software<br />Users...
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Above The Clouds

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Pat Helland's "book review" of the Above the Clouds: a Berkeley View of Cloud Computing paper.

As Pat says "If you are interested in cloud computing, you want to understand these ideas"

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  1. 1. Above the Clouds: a Berkeley View of Cloud Computing<br />Presented by:<br /> Pat Helland<br /> Partner Architect (SQL SIA)<br />Kinda’ Like a Book Report!<br />Clarification:<br /><ul><li> I did NOT write this paper – I am reporting on some excellent work.
  2. 2. Much of this paper’s content is well known to the folks working in the cloud computing space.
  3. 3. Hats off to the folks from Berkeley for such a crisp and thoughtful paper!</li></li></ul><li>Outline<br />Introduction<br />UC Berkeley: Above the Clouds<br />Pat’s Additional Thoughts<br />Conclusion<br />
  4. 4. Cool Paper Published on February 10, 2009<br />The UC Berkeley RAD Lab<br />Berkeley RAD Lab<br />(Reliable Adaptive Distributed Systems)<br />These People Wrote the Paper<br />RAD Lab Professors include:<br />Armando Fox, Michael Jordan, Anthony Joseph, Ion Stoica, Randy Katz, and Dave Patterson<br />I Simply Summarized It in This Presentation!<br />
  5. 5. My Experiences with “Cloud Computing”<br />Over 25 Years Working in Distributed Computing<br />Tandem Computers(1982-1990)<br />HaL Computers<br />(1991-1994)<br />Microsoft<br />(1994-2005 and 2007-Present)<br />Message Based Multiprocessor<br />Microsoft Transaction Server (MTS):<br />Transactional RPC and N-Tier Apps<br />Chief Architect:<br />Cache-CoherentNon-Uniform Memory Arch Multi-Processor<br />WAN Distributed DB<br />Distributed Transaction Coordinator<br />Chief Architect: Fault-Tolerant TX Platform<br />SQL Service Broker<br />Service Oriented Architectures (SOA)<br />2 Years at Amazon (2005-2007)<br />Worked to Make Software Accept Low Availability Datacenters<br />Saw “Cloud Computing” Firsthand<br />Extensive Monitoring<br />Multiple Datacenters<br />Drive to Commonality<br />Pressure on Availability<br />Worked On Product Catalog: 10s of Millions of Product Descriptions<br />Drive to Commodity<br />Creation of Dynamo<br />Internals of AWS<br />Cost Pressure on Services…<br />
  6. 6. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  7. 7. What Is Cloud Computing?<br />Cloud Computing: App and Infrastructure over Internet<br />Software as a Service: Applications over the Internet<br />Utility Computing:“Pay-as-You-Go” Datacenter Hardware and Software<br />Three New Aspects to Cloud Computing<br />The Illusion of Infinite Computing Resources Available on Demand<br />The Elimination of an Upfront Commitment by Cloud Users<br />The Ability to Pay for Use of Computing Resources on a Short-Term Basis as Needed<br />
  8. 8. Economies of Scale and App Model<br />Economies of Scale for Humongous Datacenters<br />Electricity<br />Network<br />Operations<br />Hardware<br />Put Datacenters at Cheap Power<br />Put Datacenters on Main Trunks<br />Standardize and Automate Ops<br />Containerized Low-Cost Servers<br />5 to 7 Times Reduction in the Cost of Computing…<br />App Model for Utility Computing<br />SomethingNew<br />Amazon EC2<br />Windows Azure<br />Google AppEngine<br />Close to Physical Hardware<br />.NET and CLR… ASP.NET Support<br />App Specific Traditional Web App Model<br />???<br />???<br />User Controls Most of Stack<br />More Constraints on User Stack<br />Constrained Stateless/Stateful Tiers<br />???<br />Hard to Auto Scale and Failover<br />Auto Provisioning of Stateless App<br />Auto Scaling and Auto High-Availability<br />Constraints on App Model Offer Tradeoffs… Lots of Ongoing Innovation…<br />
  9. 9. Obstacles and Opportunities<br />
  10. 10. Elasticity, Risk, and User Incentives<br />Services Will Prefer Utility Computing to a Private Cloud When:<br />Demand Varies over Time<br />Demand Unknown in Advance<br />Provisioning for Peak Leads to Underutilization at Other Times<br />Web Startup May Experience a Huge Spike If It Becomes Popular<br />Pay by the Hour(Even if the Hourly Rate is Higher)<br />Pay as You Go Does Not Require Commitment in Advance<br />The Value of Cost Associativity<br />UserHourscloud× (revenue – Costcloud) ≥<br />UserHoursdatacenter× (revenue – )<br />Costdatacenter<br />Utilization<br />
  11. 11. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  12. 12. The Dream of Cloud Computing<br />Integrated Circuit<br />Foundries<br />Utility Computing<br />Semiconductor Fabs Expensive<br />Typically &gt; $1 Billion<br />Too Much for Most Designers<br />Fabs Take Outside Work<br />Fabs Amortize Cost <br />Other Designers Make Chips<br />Allowed Explosion of Designs<br />More Players Afford Rented Fab<br />New Datacenters Very Expensive<br />Only a Few Companies Can Afford Huge Datacenters<br />Utility Computing  Datacenter Owners Amortize Costs<br />Utility Computing Users Get Advantages of Elasticity<br />Datacenter Resources Shared Across Many Users<br />
  13. 13. Cloud Computing: Confusion<br />The interesting thing about cloud computing is that we’ve redefined Cloud Computing to include everything that we already do… I don’t understand what we would do differently in the light of Cloud Computing than change some of the words in our ads.<br />Larry Ellison (Oracle CEO) , quoted in the Wall Street Journal, Sept 26, 2008<br />A lot of people are jumping on the [cloud] bandwagon, but I have not heard two people say the same thing about it. There are multiple definitions out there of “the cloud”<br />Andy Isherwood (HP VP of European Software Sales), in ZDNews, Dec 11, 2008<br />It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign. Somebody is saying this is inevitable – and whenever you hear somebody saying that, it’s very likely to be a set of businesses campaigning to make it true.<br />Richard Stallman (“free software” advocate), in The Guardian, Sept 29, 2008<br />
  14. 14. Cloud Computing: Clarifications<br />“Above the Clouds” Paper from UC Berkeley RAD Lab<br />Goals for the Paper:<br /><ul><li> Clarify Terminology
  15. 15. Compare Cloud and Conventional Computing
  16. 16. Identify Top Obstacles & Opportunities</li></ul>Paper Shaped by:<br /><ul><li> Working Since 2005 in RAD Lab
  17. 17. Users of Amazon AWS for 1 Year
  18. 18. 6 Months Brainstorming about Cloud</li></ul>Questions to Answer:<br />What New Economic Models Are Enabled by Cloud? How Can a Service Operator Decide for/against Cloud?<br />What Is Cloud Computing? How Is It Different from Software as a Service?<br />Why Is Cloud Computing Poised to Take Off Now When It Failed Before?<br />How Can We Classify Cloud Computing Offerings? What Challenges Differ?<br />What Does It Take to Be a Cloud Provider? Why Would You Do It?<br />What Are Top 10 Obstacles to Cloud?<br />What Opportunities Overcome Them?<br />What New Opportunities Are Enabled by or Potential Drivers of Cloud?<br />What Changes Are Needed for Future Apps, Infrastructure, and Hardware?<br />
  19. 19. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  20. 20. Utilities, Services, & Clouds: Oh, My!!<br />Cloud Computing: Apps Delivered as Services over the Internet and the Datacenter Hardware and Software Providing Them<br />Software as a Service: Application Services Delivered over the Internet<br />Utility Computing: Virtualized Hardware and Compute Resources Delivered over the Internet<br />Current Examples of Utility Computing<br />Amazon Web Services<br />Microsoft Azure<br />Google’s AppEngine<br />Advantages of SaaS:<br />Service Providers Have SimplifiedSoftware Installation, Maintenance,and Centralized Versioning<br />End Users Access “Anywhere, Anytime”, Share Data, Store Data Safely<br />Cloud Computing Allows Deploying Software as a Service– and Scaling on Demand – without Building or Provisioning a Datacenter<br />
  21. 21. The New Perspective of Hardware Resources<br />3 New Aspects to Cloud Computing<br />All 3 Aspect Are Required to Succeed<br />The Illusion of Infinite Computing Resources Available on Demand<br />Failed Example: Intel Computing Services<br />Required Negotiating a Contract and Longer Term Use than Per-Hour<br />The Elimination of an Upfront Commitment by Cloud Users<br />Successful Example: Amazon Web Services<br />1.0-GHz X86 “Slices” for 10 Cents/Hour<br />Pay for Use of Computing Resources on a Short-Term Basis as Needed<br />Can Add New “Slice” in 2 to 5 Minutes<br />The Cloud Providers Big Bet:<br />Multiple Instances (“Slices”) Can Be Statistically Multiplexed onto a Single Box<br />Each Rented Instance Will Not Interfere with Other User’s Usage<br />
  22. 22. Power and Cooling Is Expensive!<br />The Infrastructure for Power and Cooling Costs a LOT<br />Infrastructure PLUS Energy &gt; Server Cost Since 2001<br />Infrastructure Alone&gt; Server Cost Since 2004<br />Energy Alone&gt; Server Cost Since 2008<br />Cost Effective to Discard Inefficient Servers<br />Belady, C., “In the Data Center, Power and Cooling Costs More than IT Equipment it Supports”, Electronics Cooling Magazine (Feb 2007)<br />Power Savings  Infrastructure Savings!<br />Like Airlines Retiring Fuel-Guzzling Airplanes<br />
  23. 23. Location and Scale: It’s Easier to Ship Data than Power!<br />Datacenters Are Popping Up in Surprising Places<br />Quincy, WA<br />Google, Microsoft, Yahoo!, and Others…<br />San Antonio, TX<br />Microsoft, US NSA, and Others…<br />
  24. 24. We Already Needed a Huge Datacenter…<br />Building a Very Large-Scale Datacenter Very Is Expensive<br />$100+ Million (Minimum)<br />Large Internet Companies Already Building Huge DCs<br />Google, Amazon, Microsoft…<br />Large Internet Companies Already Building Software<br />MapReduce, GoogleFS, BigTable, Dynamo<br />James Hamilton, Internet Scale Service Efficiency, Large-Scale Distributed Systems and Middleware (LADIS) Workshop Sept‘08<br />Huge DCs 5-7X as Cost Effective as Medium-Scale DCs<br />
  25. 25. Why Be a Cloud Provider?<br />Make a Lot of Money<br />Huge datacenters cost 5-7X less for computation, storage, and networking. Fixed software & deployment amortized over many users. Large company can leverage economies of scale and make money.<br />Leverage Existing Investments<br />Web companies had to build software and datacenters anyway. Adding a new revenue stream at (hopefully) incremental cost.<br />Defend a Franchise<br />What happens as conventional server and enterprise apps embrace cloud computing? Application vendors will want a cloud offering. For example, MSFT Azure should make cloud migration easy.<br />Attack an Incumbent<br />A large company (with software & datacenter) will want a beachhead before someone else dominates in the cloud provider space.<br />Leverage Customer Relationships<br />For example, IBM Global Services may offer a branded Cloud Computing offering. IBM and their Global Services customers would preserve their existing relationship and trust.<br />Become a Platform<br />Facebook offers plug-in apps. Google App-Engine…<br />
  26. 26. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  27. 27. New Technology Trends & Business Model<br />Web 2.0<br />Low-Touch, Low-Margin, Low-Commitment<br />Web 1.0<br />High-Touch, High-Margin, High-Commitment<br />Credit Cards: Use PayPal or Similar Provider. Customer Simply Needs a Credit Card<br />Credit Cards: Contractual Relationship with Payment Processing Service<br />Ad Revenue: Easily Configured Ads for Web Pages (e.g. Google AdSense)<br />Ad Revenue: Create Biz Relationship with Ad Placement Company like DoubleClick<br />Content Distribution: Easily Configured Content Distribution Using Amazon’s CloudFront<br />Content Distribution: Establish Relationship with Content Distribution Network like Akamai<br />Amazon Web Services (Starting 2006)<br />Pay-as-You-Go-Computing<br />Start w/Credit Card<br />Bring Your Own Software<br />No Contract<br />Hardware-Level VMs<br />Share Hardware/Low Cost<br />
  28. 28. New Application Opportunities<br />Gray’s Observation: <br />Jim Gray Looked at Trends in 2003<br />Wide-Area Networking Falling Slower than Other IT Costs<br />Costs Require Putting the Data Near the Application!<br />Some Interesting New Types of Applications Enable By the Cloud:<br />Mobile Interactive Apps: Applications that respond in real time but work with lots of data. Cloud computing offers highly-available large datasets.<br />Parallel Batch Processing: “Cost Associativity” – Many systems for a short time. Washington Post used 200EC2 instances to process 17,481 pages of Hillary Clinton’s travel documents within 9 hours of their release.<br />Rise of Analytics: Again, “Cost Associativity” – Many systems for a short time. Compute intensive data analysis which may be parallelized.<br />Compute Intensive Desktop Apps: For example, symbolic mathematics requires lots of computing per unit of data. Cost efficient to push the data to the cloud for computation<br />
  29. 29. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  30. 30. A Spectrum of Application Models<br />Constraints in the App Model <br />Automated Management Services<br />More Constrained<br />Less Constrained<br />More Automation<br />Less Automation<br />Microsoft Azure<br />.NET CLR/Windows Only<br />Choice of Language<br />Some Auto Failover/ Scale (but needs declarative application properties)<br />Google App Engine<br />Traditional Web Apps<br />Auto Scaling/Provisioning<br />Amazon AWS<br />VMs Look Like Hardware<br />No Limit on App Model<br />User Must Implement Scalability and Failover<br />Force.Com<br />SalesForce Biz Apps<br />Auto Scaling/Provisioning<br />Which Model Will Dominate??<br />High-Level Languages and Frameworks Can Be Built on Lower-Level<br />Analogy: Programming Languages and Frameworks<br /><ul><li> Low-Level Languages (C/C++) Allow Fine-Grained Control
  31. 31. Building a Web App in C++ Is a Lot of Cumbersome Work
  32. 32. Ruby-on-Rails Hides the Mechanics but Only If You Follow Request/Response and Ruby’s Abstractions</li></ul>More-Constrained Clouds May Be Built on Less-Constrained Ones<br />
  33. 33. Vendors and Virtualized Resources<br />
  34. 34. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  35. 35. An Overview of the Economic Shift<br />Observations about Cloud Computing Economic Models<br />Fine-Grain and Elastic Economic Models<br />Hardware Declines at Variable Rates<br />Consider Average Utilization and Peaks<br />Costs Continue to Drop<br />Predicting Application Growth Hard<br />Tradeoff Decisions Are More Fluid<br />Rate of Decline Varies (e.g. Net vs. Store)<br />In-House, You Must Provision for Peak<br />Investment Risks May Be Reduced<br />Cloud Computing Will Track Changes Better than In-House<br />Spikes Are Very Expensive<br />This Section Will Examine These Economic Issues in More Depth<br />
  36. 36. Elasticity of Resources in Cloud Computing<br />Cloud Computing: Add or Remove Resources as Needed<br />In Amazon’s EC2, One Server at a Time  Lead Time a Few Mins.<br />Real World Server Utilization Is 5% to 20%<br />Many Services Peak Exceeds Average by a Factor of 2 to 10<br />Most Provision for Peak<br />Painful to Under-Provision (Lost Customers)<br />Provisioning for Peak<br />Without Elasticity, We Waste Resources(Shaded Areas)During Non-Peak Times<br />
  37. 37. Elasticity: Do the Math!!<br />Example: Elasticity<br />Assume Our Service:<br />Peaks at 500 Servers at Noon<br />Trough Requires 100 Servers at Midnight<br />Average Utilization Is 300 Servers<br />Actual Utilization:<br />Pay as You Go Break-Even Point<br />300 × 24 = 7200Server Hours / Day<br />12000 = 7200 × 1.667 <br />ProvisionedResources:<br />Cheaper When Pay as You Go Servers Are Less than 1.667 Times Purchased Servers<br />500 × 24 = 12000Servers Hours / Day<br />Elasticity May Be More Cost-Effective Even with a Higher Per-Hour Charge!<br />This Example Underestimates the Benefits of Elasticity<br />Seasonal Demands Require Significant Provisioning <br />Takes Weeks to Acquire and Install Equipment<br />E-Commerce Peaks December<br />Photo-Sharing Peaks January<br />
  38. 38. Elasticity: Risks of Under-Provisioning<br />Under-Provisioning #1<br />Potential Revenue (Shaded Area) Is Sacrificed<br />Under-Provisioning #2<br />Some Users Respond to Under-Provisioning by Permanently Deserting the Site... Bad for Revenue!<br />
  39. 39. Shifting Risk to the Cloud Provider<br />Example #1: Animoto<br />When Launched Surged from 50 Servers to 3500 in 3 Days<br />Traffic Doubled Every Twelve Hours for Three Days<br />After Peak, Traffic Fell to Well Below the Peak<br />Example #2: Target.Com<br />Large Retailer – ECommerce Site Run by Amazon<br />Black Friday (Nov 28th, 2008) – Many ECommerce Sites Failed<br />Target and Amazon Slower by Only About 50%<br />Cloud Computing Transfers Many Risks to the Cloud Provider<br />Assuming These Risks Allows the Cloud Provider to Change More – This Is OK!<br />UserHourscloud× (revenue – Costcloud) ≥<br />UserHoursdatacenter× (revenue – )<br />Costdatacenter<br />Utilization<br />Over/Under Provisioning Affects the Datacenter Utilization Which Affects Cost Tradeoffs<br />
  40. 40. My Favorite Queuing Theory Equation<br />Expected<br />Response<br />Time<br />Minimum Response Time<br />1 - Utilization<br />=<br />How Long Does the Work Take on an Empty System?<br />Consider a 90% Busy Server<br />When the Server Is Busy, Expect It to Take Longer<br />Answer Taking Too Long??<br />Expect 10 Times the Minimum<br />Lighten the Load!<br />Some Other Examples<br />That’s the Minimum Response Time<br />The Work Needs to Fit in the Slack<br />99% Utilization: 100 Times Min<br />50% Utilization: Twice Min<br />20% Util: (1/.8) = 1.25 Times Min <br />It Is Unrealistic to Run a System or Datacenter Above 60% - 80% Utilized !<br />
  41. 41. One More Look at the Cost Model<br />How Much You Make Total in a “Pay as You Go” Cloud<br />How Much You Make Per User Hour in a “Pay as You Go” Cloud<br />The Compute Cost of the Work in a Datacenter<br />But You Pay for the Whole Datacenter Even When It Is Underutilized!<br />UserHourscloud× (revenue – Costcloud) ≥<br />UserHoursdatacenter× (revenue – )<br />Utilization Assumptions Make a Big Difference in the Costs of Cloud versus Datacenter!<br />How Much You Make Total in a Datacenter Implementation of Your App<br />Costdatacenter<br />Utilization<br />Have to Increase the Charge for the Work You Do to Make Up for Underutilization<br />
  42. 42. Comparing Costs: Should I Move to the Cloud?<br />In 2003, Jim Gray Calculated What $1 Purchased<br />How Much Disk for $1?<br />How Much CPU for $1?<br />How Much Network for $1?<br />
  43. 43. Costs of Computing: On-Premise versus the Cloud<br />Power, Cooling, & Physical Plant Cost<br />Operations Cost<br />It Appears AWS Is a Bad Deal Compared to Buying Your Computing the “Old Fashioned” Way<br />Pay Separatelyper Resource<br />Hardware Ops Cheap Today: Simple Tasks<br />Power, Cooling, etc Cost as Much as the Computers!!<br />Most Apps Are Not Balanced in Resource Use<br />Software Ops: Patching, Upgrades May Remain…<br />May Use More or Less CPU, Disk, or Network<br />Bundled in the Cloud Costs, Not in Classic Datacenter<br />Side Note: AWS Bandwidth Cheaper than Most Can Buy!<br />Ops Burden Depends on Level of Virtualization!<br />Figures Above Not Fair to the Cloud!<br />Separate Charges May Be Better<br />
  44. 44. Cloud Is Mostly Driven by Money<br />Economics of Cloud Computing Are Very Attractive to Some Users<br />Cloud Computing Will Track Cost Changes Better than In-House<br />Predicting Application Growth Hard<br />Investment Risks May Be Reduced<br />In-House, You Must Provision for Peak<br />
  45. 45. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  46. 46. Top 10 Obstacles and Opportunities<br />
  47. 47. Organizations Worry: Will Cloud Computing Be Highly Available?<br />Existing Web & SaaS Offerings (e.g. MSN, Google, Amazon) Set a High Bar<br />Expectations Often Exceed what Enterprise-IT Can Offer<br />Outages in Cloud Infrastructure Get Lots of Press<br />Enterprises Are Reluctant to Put Applications in the Cloud without Business Continuity Plans<br />Another Obstacle Is DDOS (Distributed Denial of Service) Attack:<br />Criminals Threaten to Cut Off SaaS Providers by Swamping Them<br />Attacks Typically Use “BotNets” – Rent Simulated Users for 3 cents/week<br />Cloud Computing Allows a Defense through Quick Scale-Up<br />#1 Obstacle: Availability of a Service<br />
  48. 48. #2 Obstacle: Data Lock-In<br />Cloud Storage Providers (So Far) Have Distinct APIs<br />Difficult (Impractical) to Store Data in Multiple Cloud Providers<br />Users Must Trust Their Cloud Providers Not to Lose Data<br />Cloud Users Vulnerable to Price Increases<br />Richard Stallman Warned of This<br />Standardizing APIs Gives SaaS Programmer Portability<br />Some Argue May Lead to Commoditization of Cloud Providers<br />UC Berkeley Thinks This Is Unlikely<br />Quality of Cloud Providers Can Be a Differentiator<br />Standard APIs Allow “Surge Computing”: On-Premise plus Cloud<br />Squeeze Their Profits!<br />
  49. 49. #3 Obstacle: Data Confidentiality and Auditability<br />“My sensitive corporate data will never be in the cloud!”<br />Current Clouds Are Essentially Public Networks <br />Auditability Is Required<br />Sarbanes-Oxley<br />They Are Exposed to More Attacks<br />HIPAA<br />Berkeley Believes There Are No Fundamental Obstacles to Making Cloud Computing as Secure as Most In-House IT<br />Encrypted Storage<br />Network Middleboxes (Firewalls, Packet Filters)<br />Virtual LANs<br />Encrypted Data in the Cloud Is Likely More Secure than Unencrypted Data on Premises<br />Maybe: Cloud Provided Auditability<br />Concerns over National Boundaries<br />More Focus on Virtual Capabilities…<br />USA PATRIOT Act Gives Some Europeans Worries over SaaS in the USA<br />Auditing Below VMs<br />Foreign Subpoenas<br />Maybe More Tamper Resistant<br />Blind Subpoenas<br />
  50. 50. #4 Obstacle: Data Transfer Bottlenecks<br />Problem: At $100 to $150 per Terabyte Transferred, Data Placement and Movement Is an Issue<br />Opportunity-1: Sneaker-Net<br />Jim Gray Found Cheapest Transfer Was FedEx-ing Disks<br />1 Data Failure in 400 Attempts<br />Opportunity-2: Keep Data in Cloud<br />If the Data Is in the Cloud, Transfer Doesn’t Cost<br />Amazon Hosting Large Data<br />E.g. US Census<br />Free on S3; Free on EC2<br />Entice EC2 Business<br />Opportunity-3: Cheaper WAN<br />High-End Routers Are a Big Part of the Cost of Data Transfer<br />Research into Routing using Cheap Commodity Computers<br />Example: Ship 10TB from UC Berkeley to Amazon<br />-- WAN: S3 &lt; 20Mbits/sec:<br /> 10TB  4Mil Seconds  &gt; 45 Days<br /> $1000 in AMZN Net Fees<br />-- FedEx: Ten 1TB Disks via Overnight Shipping<br /> &lt; 1 Day to Write 10TB to Disks Locally<br /> Cost ≈ $400 <br /> Effective BW of 1500Mbits/Sec<br /> “NetFlix for Cloud Computing”<br />
  51. 51. #5 Obstacle: Performance Unpredictability<br />When Does Sharing Cause Problems with Performance?<br />Sharing CPU and Main Memory Seems to Work Well<br />Sharing I/O Seems to Cause Problems Sometimes<br />Opportunities:<br />Improve Architectures and OSes to Efficiently Virtualize Interrupts and I/O-Channels<br />Hope  IBM Mainframes in the 1980s Did This<br />Flash Memory May Decrease I/O Interference<br />Scheduling Parallel Batch Operations<br />Virtualizing High Performance Computing Is a Problem:<br />Parallel Execution Is Slow when the Communicating Processes Are Virtual (and Not Always Running)<br />Opportunity:<br />Something Like “Gang Scheduling” for Cloud Computing<br />
  52. 52. Obstacles #6, #7, #8, & #9<br />Obstacle # 6: Scalable Storage<br />Need Storage that Can Scale-Up and Scale-Down<br />It Is Not Completely Obvious the Storage Semantics Required<br />Lots of Active Research and Development Here<br />Obstacle #7: Bugs in Large-Scale Distributed Systems<br />Tough to Debug Very Large Distributed Systems<br />Common to Have Bugs Only Appear in Bug Deployments<br />Can Tracing/Debugging Information Be Captured by VM Environment?<br />Obstacle #8: Scaling Quickly<br />Need to Scale-Up and Scale-Down Computation<br />Obstacle #9: Reputation Fate Sharing<br />Create Reputation-Guarding Services (like “Trusted Email”)<br />What about Transfer of Legal Liability?<br />Is Amazon Liable If an EC2 App Sends Spam?<br />
  53. 53. #10 Obstacle: Software Licensing<br />Software Licenses Typically Restrict which Computers May Use the Software<br />Users Pay for Software and then Annual Maintenance Fees<br />SAP & Oracle Charge 22% of Purchase per Annum<br />Many Cloud Providers Used Only Open Source Software because the Licensing Model Is a Poor Fit for Cloud Computing<br />Opportunity: Open Source vs. Changes to Licenses<br />MSFT and AMZN Now Offer Pay-As-You-Go Licenses for Windows and SQL Server on EC2<br />EC2 on Windows  15 cents/hour<br />EC2 on Linux  10 cents/hour<br />Obstacle: Encourage Software Sales for the Cloud<br />Awkward with Quarterly Sales Tracking<br />Opportunity: Cloud Providers Offer Bulk Prepaid Plans<br />E.g. Oracle Sells 100,000 Instance Hours for the Cloud<br />
  54. 54. Introduction<br />UC Berkeley: Above the Clouds<br />1) Executive Summary<br />2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come<br />3) What Is Cloud Computing?<br />4) Clouds in a Perfect Storm: Why Now, Not Then?<br />5) Classes of Utility Computing<br />6) Cloud Computing Economics<br />7) Top 10 Obstacles and Opportunities for Could Computing<br />8) Conclusions and Questions about the Cloud of Tomorrow<br />Pat’s Additional Thoughts<br />Conclusion<br />Outline<br />
  55. 55. Conclusions and Questions about the Cloud of Tomorrow<br />Utility Computing: It’s Happening!<br />Grow and Shrink on Demand<br />Pay-As-You-Go<br />Cloud Provider’s View<br />Huge Datacenters Opened Economies and Possibilities<br />Cloud User’s View<br />Startups Don’t Need Datacenters<br />Established Organizations Leverage Elasticity<br />UC Berkeley Has Extensively Leveraged Elasticity to Meet Deadlines<br />Cloud Computing: High-Margin or Low-Margin Business?<br />Potential Cost Factor of 5-7X<br />Today’s Cloud Providers Had Big Datacenter Infrastructure Anyway<br />Implications of Cloud:<br />Application Software: Scale-Up and Down Rapidly; Client and Cloud<br />Infrastructure Software: Runs on VMs; Has Built-in Billing<br />Hardware Systems: Huge Scale; Container-Based; Energy Proportional<br />
  56. 56. Trends in Cloud Computing<br />Changes in Technology and Prices Over Time<br />What Will the Billing Units Be for Higher-Level Cloud Offerings?<br />What Will the Billing Units for Flash Be<br />Clearly, Cores per Chip Will Increase, Doubling Each 2-4 Years<br />How Will the Prices of the Resources Change Over Time?<br />Will Network Bandwidth Prices Drop? What Will Cause That?<br />What Will Be the Impact of Flash Memory? How Will It Be Priced?<br />Virtualization Level<br />Low-Level VMs (Amazon EC2),<br />Intermediate-Level (MSFT Azure), or<br />High-Level Framework (Google AppEngine) ?<br />Will There Be a Single Standard API?<br />Will a Standard API Lead to a “Race-to-the-Bottom” Commoditization?<br />Will There Be Many Virtualization Levels for Different Apps?<br />Will Commoditization Drive Away Cloud Providers???<br />
  57. 57. Outline<br />Introduction<br />UC Berkeley: Above the Clouds<br />Pat’s Additional Thoughts<br />Conclusion<br />
  58. 58. Some Additional Thoughts<br />Scalable Infrastructure versus Scalable Applications<br />Scalable Infrastructure: Can Run Many Applications Each of Which Is Small<br />Scalable Application: A Single Application that Support Lots of Users/Work<br />Microsoft’s New SDS Offering<br />Offers SQL “in the Cloud”<br />Scalable Infrastructure Supporting Non-Scalable Applications<br />Excellent Product Offering – Very Much in Demand for the Cloud<br />We Will Still Need to Work on Scalable Applications, Too<br />The &quot;Open Cloud Manifesto“ (Spring 2009)<br />Lots of Fuss This Week – IBM Led Declaration of Openness for the Cloud<br />Support Quickly Waned Due to Lack of Open Discussion – May Come Back<br />None of the Major Cloud Providers (Amazon, Google, Salesforce, Microsoft) Were Shown the Manifesto until Shortly before Announcement<br />Pushing for Standardized APIs<br />Arguably Premature – See Motivations Above<br />
  59. 59. Outline<br />Introduction<br />UC Berkeley: Above the Clouds<br />Pat’s Additional Thoughts<br />Conclusion<br />
  60. 60. Takeaways<br />Cloud Computing: Apps Delivered as Services over the Internet and the Datacenter Hardware and Software Providing Them<br />Software as a Service: Application Services Delivered over the Internet<br />Utility Computing: Virtualized Hardware and Compute Resources Delivered over the Internet<br />The Economics Are Changing towards Cloud Computing<br />Big Datacenters Offer Big Economies of Scale<br />Cloud Computing Transfers Risks Away from the Application Providers<br />The Application Model for Cloud Computing Is Evolving<br />Advantages to Being “Close to the Metal” versus Advantages to Higher Level<br />Applications Typically Cannot Port Transparently<br />Just Because the Infrastructure Is Scalable Doesn’t Mean the App Is!!<br />There Are Many Obstacles to Ubiquitous Cloud Computing<br />Technical Obstacles to Adoption and Growth<br />Policy and Business Obstacles to Adoption<br />The Economic Forces Will Dominate the Obstacles<br />There’s Too Much to Gain… It Will Grow!<br />
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