An Introduction to Cloud Computing (2009)
 

An Introduction to Cloud Computing (2009)

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This is a talk I gave in 2009 introducing cloud computing. It is a bit dated now.

This is a talk I gave in 2009 introducing cloud computing. It is a bit dated now.

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An Introduction to Cloud Computing (2009) An Introduction to Cloud Computing (2009) Presentation Transcript

  • An Introduction to Cloud Computing
    Robert Grossman
    December 8, 2009
  • Part 1
    Introduction
    2
  • What is a Cloud?
    Clouds provide elastic, on-demand resources or services over a network, often the Internet, with the scale and reliability of a data center.
    The NIST definition has become standard.
    Cloud architectures are not new.
    What is new:
    Scale
    Ease of use
    Pricing model.
    3
  • 4
    Scale is new.
  • Elastic, Usage Based Pricing Is New
    5
    costs the same as
    1 computer in a rack for 120 hours
    120 computers in three racks for 1 hour
    • Elastic, usage based pricing turns capex into opex.
    • Clouds can manage surges in computing needs.
  • Simplicity Offered By the Cloud is New
    6
    +
    .. and you have a computer ready to work.
    A new programmer can develop a program to process a container full of data with less than day of training using MapReduce.
  • Two Types of Clouds
    On-demand resources & services over a network at the scale of a data center
    On-demand, elastic computing instances (IaaS)
    IaaS: Amazon EC2, S3, etc.; Eucalyptus
    supports many Web 2.0 applications/users
    Large data clouds (Large Data PaaS)
    GFS/MapReduce/Bigtable, Hadoop, Sector, …
    Manage and compute with large data (say 100+ TB)
    7
  • Ease of use – With Google’s GFS & MapReduce, it is simple to compute with 10 terabytes of data over 100 nodes. With Amazon’s AMIs, it is simple to respond to a surge of 100 additional web servers.
    8
  • Cloud Architectures – How Do You Fill a Data Center?
    on-demand computing capacity
    App
    App
    App
    App
    App
    on-demand computing instances
    Cloud Data Services (BigTable, etc.)
    Quasi-relational Data Services
    App
    App
    Cloud Compute Services (MapReduce & Generalizations)
    App
    App

    App
    App
    App
    Cloud Storage Services
  • Varieties of Clouds
    Architectural Model
    Computing Instances vs Computing Capacity
    Economic Model
    Elastic, usage based pricing, lease/own, …
    Management Model
    Private vs Public; Single vs Multiple Tenant; …
    Programming Model
    Queue Service, MPI, MapReduce, Distributed UDF
    10
    Computing instances vs computing capacity
    Private internal vspublic external
    Elastic, usage-based pricing or not
    All combinations occur.
  • Payment Models
    Buying racks, containers and data centers
    Leasing racks containers and data centers
    Utility based computing (pay as you go)
    Moves cap ex to op ex
    Handle surge requirements (use 1000 servers for 1 hour vs 1 server for 1000 hours)
    11
  • Management Models
    Public, private and hybrid models
    Single tenant vs multiple tenant (shared vs non-shared hardware)
    Owned vs leased
    Manage yourself vs outsource management
    All combinations are possible
    12
  • Programming Model
    13
    on-demand
    computing instances
    on-demand computing capacity
    Amazon’s Simple Queue Service
    MPI, sockets, FIFO
    • DryadLINQ
    • Azure services
    • MapReduce
    • Distributed UDF
  • Applications
    Apps
    Compute Services
    Data Services
    Metadata Services
    PaaS
    Storage Services
    Identity Manager
    Virtual Machine Manager
    Virtual Network Manager
    IaaS
    Network Transport
  • Instances, Services & Frameworks
    15
    Hadoop DFS & MapReduce
    Google AppEngine
    Microsoft Azure
    Force.com
    VMWare
    Vmotion…
    many instances
    Amazon’s SQS
    Azure Services
    Amazon’s EC2
    single instance
    S3
    instance
    (IaaS)
    service
    framework
    (PaaS)
    operating system
  • Part 2. Cloud Computing Industry
    “Cloud computing has become the center of investment and innovation.”Nicholas Carr, 2009 IDC Directions
    16
    Cloud computing is approaching the top of the Gartner hype cycle.
  • Cloud Computing Eco-System
    No agreed upon terminology
    Vendors supporting data centers
    Vendors providing cloud apps & services to end users
    Vendors supporting the industry i.e. those developing cloud applications and services for themselves or to sell to end users
    Communities developing software, standards, benchmarks, etc.
    17
  • Cloud Computing Ecosystem
    18
    Consumers of Software as a Service
    Providers of Software as a Service
    Data Centers
    Consumers of Cloud Services
    Providers of Cloud Services
    Berkeley RAD Report on cloud computing divides industry into these layers.
  • Transition Taking Place
    A hand full of players are building multiple data centers a year and improving with each one.
    This includes Google, Microsoft, Yahoo, …
    A data center today costs $200 M – $400+ M
    Berkeley RAD Report points out analogy with semiconductor industry as companies stopped building their own Fabs and starting leasing Fabs from others as Fabs approached $1B
    19
  • Data Center Operating Systems
    20


    VM 50,000
    VM 1
    VM 1
    VM 5
    Data Center Operating System
    workstation
    Data center services include: VM management services, business continuity services, security services, power management services, etc.
  • Building Data Centers
    Sun’s Modular Data Center (MD)
    Formerly Project Blackbox
    Containers used by Google, Microsoft & others
    Data center consists of 10-60+ containers.
    21
  • Mindmeister Map of Cloud Computing
    Dupont’sMindmeister Map divides the industry:
    IaaS, PaaS, Management, Community
    http://www.mindmeister.com/maps/show_public/15936058
    22
  • Part 3
    Virtualization
    23
  • Virtualization
    Virtualization separates logical infrastructure from the underlying physical resources to decrease time to make changes, improve flexibility, improve utilization and reduce costs
    Example - server virtualization. Use one physical server to support multiple logical virtual machines (VMs), which are sometimes called logical partitions (LPARs)
    Technology pioneered by IBM in 1960s to better utilize mainframes
    24
  • Idea Dates Back to the 1960s
    25
    App
    App
    App
    CMS
    CMS
    MVS
    IBM VM/370
    IBM Mainframe
    Native (Full) Virtualization
    Examples: Vmware ESX
  • Two Types of Virtualization
    26
    Apps
    Apps
    Unmodified Guest OS 1
    Unmodified Guest OS 2
    Modified Guest OS 1
    Modified Guest OS 2
    Hyperviser
    Hyperviser
    Physical Hardware
    Physical Hardware
    Native (Full) Virtualization
    Examples: Vmware ESX
    Para Virtualization
    Examples: Xen
    Using the hypervisor, each guest OS sees its own independent copy of the CPU, memory, IO, etc.
  • Four Key Properties
    Partitioning: run multiple VMs on one physical server; one VM doesn’t know about the others
    Isolation: security isolation is at the hardware level.
    Encapsulation: entire state of the machine can be copied to files and moved around
    Hardware abstraction: provision and migrate VM to another server
    27
  • Managing Virtual Machines
    Provision VM
    Schedule VM
    Monitor VM
    Self-service portal for VM
    28
  • Part 4
    Technical differences between clouds for data intensive computing, databases and supercomputers
    29
  • Supercomputer Center Model
    or
    Data Center Model
  • What Resource is Managed?
    Scarce processors wait for data
    Manage cycles
    wait for an opening in the queue
    scatter the data to the processors
    and gather the results
    Persistent data wait for queries
    Manage data
    persistent data waits for queries
    computation done locally
    results returned
    Supercomputer Center Model
    (local)
    HPC Grid
    (distributed)
    Data Center 2.0
    Model
    Distributed 2.0
    Data Centers
  • Databases
    vs
    Data Clouds
    Trading functionality for scalability.
    32
  • Trading Functionality for Scalability
    33
  • Not Everyone Agrees
    David J. DeWitt and Michael Stonebraker, MapReduce: A Major Step Backwards, Database Column, Jane 17, 2008
    34
  • Part 5. Standards Efforts
    35
    Train gauge in Russia is 1520 mm
    Train gauge in China is 1435 mm
    How can a cloud application move from one cloud storage service to another?
    Change of gauge at Ussuriisk (near Vladivostok) at the Chinese –Russian border
  • Standards Efforts for Clouds
    Distributed Management Task Force (DMTF)
    Storage Network Industrial Association (SNIA)
    Cloud Computing Interoperability Forum (CCIF)
    Open Cloud Consortium (OCC)
    Open Grid Forum (OGF)
    Plus several others…
    36