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Above The Clouds
 

Above The Clouds

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

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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    Above The Clouds Above The Clouds Presentation Transcript

    • Above the Clouds: a Berkeley View of Cloud Computing
      Presented by:
      Pat Helland
      Partner Architect (SQL SIA)
      Kinda’ Like a Book Report!
      Clarification:
      • I did NOT write this paper – I am reporting on some excellent work.
      • 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!
    • Outline
      Introduction
      UC Berkeley: Above the Clouds
      Pat’s Additional Thoughts
      Conclusion
    • 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!
    • 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…
    • 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
    • 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
    • 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…
    • Obstacles and Opportunities
    • 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
    • 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
    • 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
    • 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
    • Cloud Computing: Clarifications
      “Above the Clouds” Paper from UC Berkeley RAD Lab
      Goals for the Paper:
      • Clarify Terminology
      • Compare Cloud and Conventional Computing
      • Identify Top Obstacles & Opportunities
      Paper Shaped by:
      • Working Since 2005 in RAD Lab
      • Users of Amazon AWS for 1 Year
      • 6 Months Brainstorming about Cloud
      Questions 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?
    • 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
    • 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
    • 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
    • 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
    • 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…
    • 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
    • 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…
    • 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
    • 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
    • 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
    • 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
    • A Spectrum of Application Models
      Constraints in the App Model
      Automated Management Services
      More Constrained
      Less Constrained
      More Automation
      Less Automation
      Microsoft Azure
      .NET CLR/Windows Only
      Choice of Language
      Some Auto Failover/ Scale (but needs declarative application properties)
      Google App Engine
      Traditional Web Apps
      Auto Scaling/Provisioning
      Amazon AWS
      VMs Look Like Hardware
      No Limit on App Model
      User Must Implement Scalability and Failover
      Force.Com
      SalesForce Biz Apps
      Auto Scaling/Provisioning
      Which Model Will Dominate??
      High-Level Languages and Frameworks Can Be Built on Lower-Level
      Analogy: Programming Languages and Frameworks
      • Low-Level Languages (C/C++) Allow Fine-Grained Control
      • 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
      More-Constrained Clouds May Be Built on Less-Constrained Ones
    • Vendors and Virtualized Resources
    • 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
    • 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
    • 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
    • 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
    • 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!
    • 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
    • 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 !
    • 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
    • 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?
    • 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
    • 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
    • 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
    • Top 10 Obstacles and Opportunities
    • 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
    • #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!
    • #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
    • #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”
    • #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
    • 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?
    • #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
    • 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
    • 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
    • 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???
    • Outline
      Introduction
      UC Berkeley: Above the Clouds
      Pat’s Additional Thoughts
      Conclusion
    • 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
    • Outline
      Introduction
      UC Berkeley: Above the Clouds
      Pat’s Additional Thoughts
      Conclusion
    • 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!