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!