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"

2. Much of this paper’s content is well known to the folks working in the cloud computing space.

4. Cool Paper Published on February 10, 2009 The UC Berkeley RAD Lab Berkeley RAD Lab (Reliable Adaptive Distributed Systems) These People Wrote the Paper RAD Lab Professors include: Armando Fox, Michael Jordan, Anthony Joseph, Ion Stoica, Randy Katz, and Dave Patterson I Simply Summarized It in This Presentation!

5. My Experiences with “Cloud Computing” Over 25 Years Working in Distributed Computing Tandem Computers(1982-1990) HaL Computers (1991-1994) Microsoft (1994-2005 and 2007-Present) Message Based Multiprocessor Microsoft Transaction Server (MTS): Transactional RPC and N-Tier Apps Chief Architect: Cache-CoherentNon-Uniform Memory Arch Multi-Processor WAN Distributed DB Distributed Transaction Coordinator Chief Architect: Fault-Tolerant TX Platform SQL Service Broker Service Oriented Architectures (SOA) 2 Years at Amazon (2005-2007) Worked to Make Software Accept Low Availability Datacenters Saw “Cloud Computing” Firsthand Extensive Monitoring Multiple Datacenters Drive to Commonality Pressure on Availability Worked On Product Catalog: 10s of Millions of Product Descriptions Drive to Commodity Creation of Dynamo Internals of AWS Cost Pressure on Services…

6. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

7. What Is Cloud Computing? Cloud Computing: App and Infrastructure over Internet Software as a Service: Applications over the Internet Utility Computing:“Pay-as-You-Go” Datacenter Hardware and Software Three New Aspects to Cloud Computing The Illusion of Infinite Computing Resources Available on Demand The Elimination of an Upfront Commitment by Cloud Users The Ability to Pay for Use of Computing Resources on a Short-Term Basis as Needed

8. Economies of Scale and App Model Economies of Scale for Humongous Datacenters Electricity Network Operations Hardware Put Datacenters at Cheap Power Put Datacenters on Main Trunks Standardize and Automate Ops Containerized Low-Cost Servers 5 to 7 Times Reduction in the Cost of Computing… App Model for Utility Computing SomethingNew Amazon EC2 Windows Azure Google AppEngine Close to Physical Hardware .NET and CLR… ASP.NET Support App Specific Traditional Web App Model ??? ??? User Controls Most of Stack More Constraints on User Stack Constrained Stateless/Stateful Tiers ??? Hard to Auto Scale and Failover Auto Provisioning of Stateless App Auto Scaling and Auto High-Availability Constraints on App Model Offer Tradeoffs… Lots of Ongoing Innovation…

9. Obstacles and Opportunities

10. Elasticity, Risk, and User Incentives Services Will Prefer Utility Computing to a Private Cloud When: Demand Varies over Time Demand Unknown in Advance Provisioning for Peak Leads to Underutilization at Other Times Web Startup May Experience a Huge Spike If It Becomes Popular Pay by the Hour(Even if the Hourly Rate is Higher) Pay as You Go Does Not Require Commitment in Advance The Value of Cost Associativity UserHourscloud× (revenue – Costcloud) ≥ UserHoursdatacenter× (revenue – ) Costdatacenter Utilization

11. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

12. The Dream of Cloud Computing Integrated Circuit Foundries Utility Computing Semiconductor Fabs Expensive Typically > $1 Billion Too Much for Most Designers Fabs Take Outside Work Fabs Amortize Cost Other Designers Make Chips Allowed Explosion of Designs More Players Afford Rented Fab New Datacenters Very Expensive Only a Few Companies Can Afford Huge Datacenters Utility Computing  Datacenter Owners Amortize Costs Utility Computing Users Get Advantages of Elasticity Datacenter Resources Shared Across Many Users

13. Cloud Computing: Confusion 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. Larry Ellison (Oracle CEO) , quoted in the Wall Street Journal, Sept 26, 2008 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” Andy Isherwood (HP VP of European Software Sales), in ZDNews, Dec 11, 2008 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. Richard Stallman (“free software” advocate), in The Guardian, Sept 29, 2008

15. Compare Cloud and Conventional Computing

17. Users of Amazon AWS for 1 Year

18. 6 Months Brainstorming about CloudQuestions to Answer: What New Economic Models Are Enabled by Cloud? How Can a Service Operator Decide for/against Cloud? What Is Cloud Computing? How Is It Different from Software as a Service? Why Is Cloud Computing Poised to Take Off Now When It Failed Before? How Can We Classify Cloud Computing Offerings? What Challenges Differ? What Does It Take to Be a Cloud Provider? Why Would You Do It? What Are Top 10 Obstacles to Cloud? What Opportunities Overcome Them? What New Opportunities Are Enabled by or Potential Drivers of Cloud? What Changes Are Needed for Future Apps, Infrastructure, and Hardware?

19. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

20. Utilities, Services, & Clouds: Oh, My!! Cloud Computing: Apps Delivered as Services over the Internet and the Datacenter Hardware and Software Providing Them Software as a Service: Application Services Delivered over the Internet Utility Computing: Virtualized Hardware and Compute Resources Delivered over the Internet Current Examples of Utility Computing Amazon Web Services Microsoft Azure Google’s AppEngine Advantages of SaaS: Service Providers Have SimplifiedSoftware Installation, Maintenance,and Centralized Versioning End Users Access “Anywhere, Anytime”, Share Data, Store Data Safely Cloud Computing Allows Deploying Software as a Service– and Scaling on Demand – without Building or Provisioning a Datacenter

21. The New Perspective of Hardware Resources 3 New Aspects to Cloud Computing All 3 Aspect Are Required to Succeed The Illusion of Infinite Computing Resources Available on Demand Failed Example: Intel Computing Services Required Negotiating a Contract and Longer Term Use than Per-Hour The Elimination of an Upfront Commitment by Cloud Users Successful Example: Amazon Web Services 1.0-GHz X86 “Slices” for 10 Cents/Hour Pay for Use of Computing Resources on a Short-Term Basis as Needed Can Add New “Slice” in 2 to 5 Minutes The Cloud Providers Big Bet: Multiple Instances (“Slices”) Can Be Statistically Multiplexed onto a Single Box Each Rented Instance Will Not Interfere with Other User’s Usage

22. Power and Cooling Is Expensive! The Infrastructure for Power and Cooling Costs a LOT Infrastructure PLUS Energy > Server Cost Since 2001 Infrastructure Alone> Server Cost Since 2004 Energy Alone> Server Cost Since 2008 Cost Effective to Discard Inefficient Servers Belady, C., “In the Data Center, Power and Cooling Costs More than IT Equipment it Supports”, Electronics Cooling Magazine (Feb 2007) Power Savings  Infrastructure Savings! Like Airlines Retiring Fuel-Guzzling Airplanes

23. Location and Scale: It’s Easier to Ship Data than Power! Datacenters Are Popping Up in Surprising Places Quincy, WA Google, Microsoft, Yahoo!, and Others… San Antonio, TX Microsoft, US NSA, and Others…

24. We Already Needed a Huge Datacenter… Building a Very Large-Scale Datacenter Very Is Expensive $100+ Million (Minimum) Large Internet Companies Already Building Huge DCs Google, Amazon, Microsoft… Large Internet Companies Already Building Software MapReduce, GoogleFS, BigTable, Dynamo James Hamilton, Internet Scale Service Efficiency, Large-Scale Distributed Systems and Middleware (LADIS) Workshop Sept‘08 Huge DCs 5-7X as Cost Effective as Medium-Scale DCs

25. Why Be a Cloud Provider? Make a Lot of Money 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. Leverage Existing Investments Web companies had to build software and datacenters anyway. Adding a new revenue stream at (hopefully) incremental cost. Defend a Franchise 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. Attack an Incumbent A large company (with software & datacenter) will want a beachhead before someone else dominates in the cloud provider space. Leverage Customer Relationships 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. Become a Platform Facebook offers plug-in apps. Google App-Engine…

26. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

27. New Technology Trends & Business Model Web 2.0 Low-Touch, Low-Margin, Low-Commitment Web 1.0 High-Touch, High-Margin, High-Commitment Credit Cards: Use PayPal or Similar Provider. Customer Simply Needs a Credit Card Credit Cards: Contractual Relationship with Payment Processing Service Ad Revenue: Easily Configured Ads for Web Pages (e.g. Google AdSense) Ad Revenue: Create Biz Relationship with Ad Placement Company like DoubleClick Content Distribution: Easily Configured Content Distribution Using Amazon’s CloudFront Content Distribution: Establish Relationship with Content Distribution Network like Akamai Amazon Web Services (Starting 2006) Pay-as-You-Go-Computing Start w/Credit Card Bring Your Own Software No Contract Hardware-Level VMs Share Hardware/Low Cost

28. New Application Opportunities Gray’s Observation: Jim Gray Looked at Trends in 2003 Wide-Area Networking Falling Slower than Other IT Costs Costs Require Putting the Data Near the Application! Some Interesting New Types of Applications Enable By the Cloud: Mobile Interactive Apps: Applications that respond in real time but work with lots of data. Cloud computing offers highly-available large datasets. 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. Rise of Analytics: Again, “Cost Associativity” – Many systems for a short time. Compute intensive data analysis which may be parallelized. 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

29. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

31. Building a Web App in C++ Is a Lot of Cumbersome Work

32. Ruby-on-Rails Hides the Mechanics but Only If You Follow Request/Response and Ruby’s AbstractionsMore-Constrained Clouds May Be Built on Less-Constrained Ones

33. Vendors and Virtualized Resources

34. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

35. An Overview of the Economic Shift Observations about Cloud Computing Economic Models Fine-Grain and Elastic Economic Models Hardware Declines at Variable Rates Consider Average Utilization and Peaks Costs Continue to Drop Predicting Application Growth Hard Tradeoff Decisions Are More Fluid Rate of Decline Varies (e.g. Net vs. Store) In-House, You Must Provision for Peak Investment Risks May Be Reduced Cloud Computing Will Track Changes Better than In-House Spikes Are Very Expensive This Section Will Examine These Economic Issues in More Depth

36. Elasticity of Resources in Cloud Computing Cloud Computing: Add or Remove Resources as Needed In Amazon’s EC2, One Server at a Time  Lead Time a Few Mins. Real World Server Utilization Is 5% to 20% Many Services Peak Exceeds Average by a Factor of 2 to 10 Most Provision for Peak Painful to Under-Provision (Lost Customers) Provisioning for Peak Without Elasticity, We Waste Resources(Shaded Areas)During Non-Peak Times

37. Elasticity: Do the Math!! Example: Elasticity Assume Our Service: Peaks at 500 Servers at Noon Trough Requires 100 Servers at Midnight Average Utilization Is 300 Servers Actual Utilization: Pay as You Go Break-Even Point 300 × 24 = 7200Server Hours / Day 12000 = 7200 × 1.667 ProvisionedResources: Cheaper When Pay as You Go Servers Are Less than 1.667 Times Purchased Servers 500 × 24 = 12000Servers Hours / Day Elasticity May Be More Cost-Effective Even with a Higher Per-Hour Charge! This Example Underestimates the Benefits of Elasticity Seasonal Demands Require Significant Provisioning Takes Weeks to Acquire and Install Equipment E-Commerce Peaks December Photo-Sharing Peaks January

38. Elasticity: Risks of Under-Provisioning Under-Provisioning #1 Potential Revenue (Shaded Area) Is Sacrificed Under-Provisioning #2 Some Users Respond to Under-Provisioning by Permanently Deserting the Site... Bad for Revenue!

39. Shifting Risk to the Cloud Provider Example #1: Animoto When Launched Surged from 50 Servers to 3500 in 3 Days Traffic Doubled Every Twelve Hours for Three Days After Peak, Traffic Fell to Well Below the Peak Example #2: Target.Com Large Retailer – ECommerce Site Run by Amazon Black Friday (Nov 28th, 2008) – Many ECommerce Sites Failed Target and Amazon Slower by Only About 50% Cloud Computing Transfers Many Risks to the Cloud Provider Assuming These Risks Allows the Cloud Provider to Change More – This Is OK! UserHourscloud× (revenue – Costcloud) ≥ UserHoursdatacenter× (revenue – ) Costdatacenter Utilization Over/Under Provisioning Affects the Datacenter Utilization Which Affects Cost Tradeoffs

40. My Favorite Queuing Theory Equation Expected Response Time Minimum Response Time 1 - Utilization = How Long Does the Work Take on an Empty System? Consider a 90% Busy Server When the Server Is Busy, Expect It to Take Longer Answer Taking Too Long?? Expect 10 Times the Minimum Lighten the Load! Some Other Examples That’s the Minimum Response Time The Work Needs to Fit in the Slack 99% Utilization: 100 Times Min 50% Utilization: Twice Min 20% Util: (1/.8) = 1.25 Times Min It Is Unrealistic to Run a System or Datacenter Above 60% - 80% Utilized !

41. One More Look at the Cost Model How Much You Make Total in a “Pay as You Go” Cloud How Much You Make Per User Hour in a “Pay as You Go” Cloud The Compute Cost of the Work in a Datacenter But You Pay for the Whole Datacenter Even When It Is Underutilized! UserHourscloud× (revenue – Costcloud) ≥ UserHoursdatacenter× (revenue – ) Utilization Assumptions Make a Big Difference in the Costs of Cloud versus Datacenter! How Much You Make Total in a Datacenter Implementation of Your App Costdatacenter Utilization Have to Increase the Charge for the Work You Do to Make Up for Underutilization

42. Comparing Costs: Should I Move to the Cloud? In 2003, Jim Gray Calculated What $1 Purchased How Much Disk for $1? How Much CPU for $1? How Much Network for $1?

43. Costs of Computing: On-Premise versus the Cloud Power, Cooling, & Physical Plant Cost Operations Cost It Appears AWS Is a Bad Deal Compared to Buying Your Computing the “Old Fashioned” Way Pay Separatelyper Resource Hardware Ops Cheap Today: Simple Tasks Power, Cooling, etc Cost as Much as the Computers!! Most Apps Are Not Balanced in Resource Use Software Ops: Patching, Upgrades May Remain… May Use More or Less CPU, Disk, or Network Bundled in the Cloud Costs, Not in Classic Datacenter Side Note: AWS Bandwidth Cheaper than Most Can Buy! Ops Burden Depends on Level of Virtualization! Figures Above Not Fair to the Cloud! Separate Charges May Be Better

44. Cloud Is Mostly Driven by Money Economics of Cloud Computing Are Very Attractive to Some Users Cloud Computing Will Track Cost Changes Better than In-House Predicting Application Growth Hard Investment Risks May Be Reduced In-House, You Must Provision for Peak

45. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

46. Top 10 Obstacles and Opportunities

47. Organizations Worry: Will Cloud Computing Be Highly Available? Existing Web & SaaS Offerings (e.g. MSN, Google, Amazon) Set a High Bar Expectations Often Exceed what Enterprise-IT Can Offer Outages in Cloud Infrastructure Get Lots of Press Enterprises Are Reluctant to Put Applications in the Cloud without Business Continuity Plans Another Obstacle Is DDOS (Distributed Denial of Service) Attack: Criminals Threaten to Cut Off SaaS Providers by Swamping Them Attacks Typically Use “BotNets” – Rent Simulated Users for 3 cents/week Cloud Computing Allows a Defense through Quick Scale-Up #1 Obstacle: Availability of a Service

48. #2 Obstacle: Data Lock-In Cloud Storage Providers (So Far) Have Distinct APIs Difficult (Impractical) to Store Data in Multiple Cloud Providers Users Must Trust Their Cloud Providers Not to Lose Data Cloud Users Vulnerable to Price Increases Richard Stallman Warned of This Standardizing APIs Gives SaaS Programmer Portability Some Argue May Lead to Commoditization of Cloud Providers UC Berkeley Thinks This Is Unlikely Quality of Cloud Providers Can Be a Differentiator Standard APIs Allow “Surge Computing”: On-Premise plus Cloud Squeeze Their Profits!

49. #3 Obstacle: Data Confidentiality and Auditability “My sensitive corporate data will never be in the cloud!” Current Clouds Are Essentially Public Networks Auditability Is Required Sarbanes-Oxley They Are Exposed to More Attacks HIPAA Berkeley Believes There Are No Fundamental Obstacles to Making Cloud Computing as Secure as Most In-House IT Encrypted Storage Network Middleboxes (Firewalls, Packet Filters) Virtual LANs Encrypted Data in the Cloud Is Likely More Secure than Unencrypted Data on Premises Maybe: Cloud Provided Auditability Concerns over National Boundaries More Focus on Virtual Capabilities… USA PATRIOT Act Gives Some Europeans Worries over SaaS in the USA Auditing Below VMs Foreign Subpoenas Maybe More Tamper Resistant Blind Subpoenas

50. #4 Obstacle: Data Transfer Bottlenecks Problem: At $100 to $150 per Terabyte Transferred, Data Placement and Movement Is an Issue Opportunity-1: Sneaker-Net Jim Gray Found Cheapest Transfer Was FedEx-ing Disks 1 Data Failure in 400 Attempts Opportunity-2: Keep Data in Cloud If the Data Is in the Cloud, Transfer Doesn’t Cost Amazon Hosting Large Data E.g. US Census Free on S3; Free on EC2 Entice EC2 Business Opportunity-3: Cheaper WAN High-End Routers Are a Big Part of the Cost of Data Transfer Research into Routing using Cheap Commodity Computers Example: Ship 10TB from UC Berkeley to Amazon -- WAN: S3 < 20Mbits/sec: 10TB  4Mil Seconds  > 45 Days $1000 in AMZN Net Fees -- FedEx: Ten 1TB Disks via Overnight Shipping < 1 Day to Write 10TB to Disks Locally Cost ≈ $400 Effective BW of 1500Mbits/Sec “NetFlix for Cloud Computing”

51. #5 Obstacle: Performance Unpredictability When Does Sharing Cause Problems with Performance? Sharing CPU and Main Memory Seems to Work Well Sharing I/O Seems to Cause Problems Sometimes Opportunities: Improve Architectures and OSes to Efficiently Virtualize Interrupts and I/O-Channels Hope  IBM Mainframes in the 1980s Did This Flash Memory May Decrease I/O Interference Scheduling Parallel Batch Operations Virtualizing High Performance Computing Is a Problem: Parallel Execution Is Slow when the Communicating Processes Are Virtual (and Not Always Running) Opportunity: Something Like “Gang Scheduling” for Cloud Computing

52. Obstacles #6, #7, #8, & #9 Obstacle # 6: Scalable Storage Need Storage that Can Scale-Up and Scale-Down It Is Not Completely Obvious the Storage Semantics Required Lots of Active Research and Development Here Obstacle #7: Bugs in Large-Scale Distributed Systems Tough to Debug Very Large Distributed Systems Common to Have Bugs Only Appear in Bug Deployments Can Tracing/Debugging Information Be Captured by VM Environment? Obstacle #8: Scaling Quickly Need to Scale-Up and Scale-Down Computation Obstacle #9: Reputation Fate Sharing Create Reputation-Guarding Services (like “Trusted Email”) What about Transfer of Legal Liability? Is Amazon Liable If an EC2 App Sends Spam?

53. #10 Obstacle: Software Licensing Software Licenses Typically Restrict which Computers May Use the Software Users Pay for Software and then Annual Maintenance Fees SAP & Oracle Charge 22% of Purchase per Annum Many Cloud Providers Used Only Open Source Software because the Licensing Model Is a Poor Fit for Cloud Computing Opportunity: Open Source vs. Changes to Licenses MSFT and AMZN Now Offer Pay-As-You-Go Licenses for Windows and SQL Server on EC2 EC2 on Windows  15 cents/hour EC2 on Linux  10 cents/hour Obstacle: Encourage Software Sales for the Cloud Awkward with Quarterly Sales Tracking Opportunity: Cloud Providers Offer Bulk Prepaid Plans E.g. Oracle Sells 100,000 Instance Hours for the Cloud

54. Introduction UC Berkeley: Above the Clouds 1) Executive Summary 2) Cloud Computing: an Old Idea Whose Time Has (Finally) Come 3) What Is Cloud Computing? 4) Clouds in a Perfect Storm: Why Now, Not Then? 5) Classes of Utility Computing 6) Cloud Computing Economics 7) Top 10 Obstacles and Opportunities for Could Computing 8) Conclusions and Questions about the Cloud of Tomorrow Pat’s Additional Thoughts Conclusion Outline

55. Conclusions and Questions about the Cloud of Tomorrow Utility Computing: It’s Happening! Grow and Shrink on Demand Pay-As-You-Go Cloud Provider’s View Huge Datacenters Opened Economies and Possibilities Cloud User’s View Startups Don’t Need Datacenters Established Organizations Leverage Elasticity UC Berkeley Has Extensively Leveraged Elasticity to Meet Deadlines Cloud Computing: High-Margin or Low-Margin Business? Potential Cost Factor of 5-7X Today’s Cloud Providers Had Big Datacenter Infrastructure Anyway Implications of Cloud: Application Software: Scale-Up and Down Rapidly; Client and Cloud Infrastructure Software: Runs on VMs; Has Built-in Billing Hardware Systems: Huge Scale; Container-Based; Energy Proportional

56. Trends in Cloud Computing Changes in Technology and Prices Over Time What Will the Billing Units Be for Higher-Level Cloud Offerings? What Will the Billing Units for Flash Be Clearly, Cores per Chip Will Increase, Doubling Each 2-4 Years How Will the Prices of the Resources Change Over Time? Will Network Bandwidth Prices Drop? What Will Cause That? What Will Be the Impact of Flash Memory? How Will It Be Priced? Virtualization Level Low-Level VMs (Amazon EC2), Intermediate-Level (MSFT Azure), or High-Level Framework (Google AppEngine) ? Will There Be a Single Standard API? Will a Standard API Lead to a “Race-to-the-Bottom” Commoditization? Will There Be Many Virtualization Levels for Different Apps? Will Commoditization Drive Away Cloud Providers???

57. Outline Introduction UC Berkeley: Above the Clouds Pat’s Additional Thoughts Conclusion

58. Some Additional Thoughts Scalable Infrastructure versus Scalable Applications Scalable Infrastructure: Can Run Many Applications Each of Which Is Small Scalable Application: A Single Application that Support Lots of Users/Work Microsoft’s New SDS Offering Offers SQL “in the Cloud” Scalable Infrastructure Supporting Non-Scalable Applications Excellent Product Offering – Very Much in Demand for the Cloud We Will Still Need to Work on Scalable Applications, Too The "Open Cloud Manifesto“ (Spring 2009) Lots of Fuss This Week – IBM Led Declaration of Openness for the Cloud Support Quickly Waned Due to Lack of Open Discussion – May Come Back None of the Major Cloud Providers (Amazon, Google, Salesforce, Microsoft) Were Shown the Manifesto until Shortly before Announcement Pushing for Standardized APIs Arguably Premature – See Motivations Above

59. Outline Introduction UC Berkeley: Above the Clouds Pat’s Additional Thoughts Conclusion

60. Takeaways Cloud Computing: Apps Delivered as Services over the Internet and the Datacenter Hardware and Software Providing Them Software as a Service: Application Services Delivered over the Internet Utility Computing: Virtualized Hardware and Compute Resources Delivered over the Internet The Economics Are Changing towards Cloud Computing Big Datacenters Offer Big Economies of Scale Cloud Computing Transfers Risks Away from the Application Providers The Application Model for Cloud Computing Is Evolving Advantages to Being “Close to the Metal” versus Advantages to Higher Level Applications Typically Cannot Port Transparently Just Because the Infrastructure Is Scalable Doesn’t Mean the App Is!! There Are Many Obstacles to Ubiquitous Cloud Computing Technical Obstacles to Adoption and Growth Policy and Business Obstacles to Adoption The Economic Forces Will Dominate the Obstacles There’s Too Much to Gain… It Will Grow!