UoLTemplateST.ppt

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CIS017-6 Distributed and Parallel
Architecture Grid Computing / Cloud
Computing Dr Ingo Frommholz
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Outline

• Grid Computing
• Introduction to Grid Computing
• Various Approaches of Grid Computing
• Genesis of Grid Computing
• Types of Grids
• Grid Architecture
• Conclusion
• Cloud Computing
• What is Cloud Computing?
• Characteristics and Models
• Discussion

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Grid Computing

• Part I

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What is Grid Computing?
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What is Grid Computing/Networking?

• The vision of grid computing is to virtualize
  computing, with the goal of creating a utility
  computing model over a distributed set of
  resources.
• Within a single computer exist standard elements
  including the processor, storage, operating
  system and I/O.
• The concept of grid computing is to create a
  similar environment, over a distributed area,
  made up of heterogeneous elements including
  servers, storage devices, and networks a
  scalable, wide-area computing platform.
• The software (also called Grid Middleware) that
  handles the coordination of the participating
  elements is analogous to the operating system of
  a computer or server.

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Grid To Virtualise Computing
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Virtualisation

• A broad term that refers to the abstraction of
  computer resources.
• A technique for hiding the physical
  characteristics of computing resources from the
  way in which other systems, applications or end
  users interact with those resources.
• Making a single physical resource (ie. a server,
  an operating system, an application or storage
  device) appear to function as multiple logical
  resources
• Making multiple physical resources (ie. storage
  devices or servers) appear as a single logical
  resource.

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Virtual Organization (VO)

• A virtual entity whose users and servers are
  geographically apart but share their resources
  collectively as a larger grid
• The users of the grid can be organized
  dynamically into a number of virtual
  organizations
• Each organisation may come with different policy
  requirements

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Virtual Organization (VO)
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Definitions of Grid

• Many different definitions exist.
• Sun Microsystems definition
• Grid Computing is a computing infrastructure that
  provides dependable, consistent, pervasive and
  inexpensive access to computational capabilities.
  

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Definitions of Grid (cont.)

• IBM Definition
• Grid computing enables the virtualization of
  distributed computing and data resources such as
  processing, network bandwidth and storage
  capacity to create a single system image,
  granting users and applications seamless access
  to vast IT capabilities. Just as an Internet user
  views a unified instance of content via the Web,
  a grid user essentially sees a single, large
  virtual computer.

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Definitions of Grid (cont.)

• 1998, Kesselman Foster
• A computational grid is a hardware and software
  infrastructure that provides dependable,
  consistent, pervasive, and inexpensive access to
  high-end computational capabilities.
• 2000, Kesselman, Foster, Tuecke
• coordinated resource sharing and problem
  solving in dynamic, multi-institutional virtual
  organizations.

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Ian Fosters Grid Checklist (2002) Criteria for a
Grid

• A Grid is a system that
• Coordinates resources that are not subject to
  centralized control
• Uses standard, open, general-purpose protocols
  and interfaces
• Delivers non-trivial qualities of service

Source What is the Grid? A Three Point
Checklist, Ian Foster, Argonne National
Laboratory University of Chicago
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Motivations for Grids

• Enable heavy applications in science and
  engineering
• Complex simulations with visualization and
  steering
• Access and analysis of large remote datasets
• Access to remote data sources and special
  instruments (telescopes, satellite data, particle
  accelerators)
• Distributed in wide-area networks, and
• Accessed through collaborative and
  multidisciplinary Problem Solving Environments
  PSE (ie. Matlab), via Web Portals.

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Purpose of Grid Computing
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Purposes of Grid Computing

• Distributed Supercomputing
• High-Throughput Computing
• On-Demand Computing
• Data-Intensive Computing
• Collaborative Computing

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Distributed Supercomputing

• Combines multiple high-capacity resources (ie.
  computer clusters) on a computational grid into a
  single, virtual distributed supercomputer.
• Tackles problems that cannot be solved on a
  single system.
• Grid aggregates computational resources to
  compute large complex problems
• Fast networks enabling true parallel computation
  and shared memory processing
• Select computational resources according to time
  and financial constraints

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Distributed Supercomputing (cont.)

• Architectures for High Performance Computing
• Supercluster
• ie. Blue Gene/G (65536 processors/4096 nodes in 4
  racks)
• Clusters
• ie. iceberg
• Symmetric multiprocessors
• ie. 4 processor shared memory V40 node on iceberg
• Shared memory programming Open MP
• Vector Processor
• Ie. Amdahl VP at MCC (80s and 90s)

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High-Throughput Computing

• Uses the grid to schedule large numbers of
  loosely coupled or independent tasks, with the
  goal of putting unused processor cycles to work.
• Problems divided into many tasks
• Grid schedules tasks
• Seti_at_home
• The mother of _at_home projects
• Other _at_home projects
• Folding_at_home, fightAIDS_at_home, Xpulsar_at_home
• Condor
• Cycle scavenging from spare PCs

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On-Demand Computing

• Uses grid capabilities to meet short-term
  requirements for resources that are not locally
  accessible.
• Models real-time computing demands.

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Data-Intensive Computing

• Synthesizes new information from data that is
  maintained in geographically distributed
  repositories, digital libraries, and databases.
• Particularly useful for distributed data mining.

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Collaborative Computing

• Concerned primarily with enabling and enhancing
  human-to-human interactions.
• Applications are often structured in terms of a
  virtual shared space.

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Genesis of grid computing
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Distributed, parallel computing and the need for
distributed collaborative PSE led to the creation
of Grid.
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Distributed Computing

• Physically distributed computations and data
• Local (LAN) or large scale (WAN)
• Geographical distribution
• Users and access sites
• Processing sites and data archives
• Availability and Reliability
• Fault tolerance
• Replication of hardware and software
• Goals
• Adapt to geographical application distribution
• Provide appropriate levels of transparency

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Parallel Computing

• Computer System Architectures 1980s-90s
• Supercomputers
• Shared / Distributed memory multiprocessors
• LANs and Clusters of PCs
• Parallel Programming requires
• Decompose application in parts
• Launch tasks in parallel processes
• Plan the cooperation between tasks
• Goal to reduce execution time, compared to
  sequential execution
• Quite a difficult task!

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Problem-Solving Environments (PSE)

• Integrated environments for solving a class of
  related problems in an application domain
• Easy-to-use by the end-user
• Based on state-of-the-art algorithms
• Visualisation and GUI
• An old idea local and standalone
• Examples MatLab, Mathematica
• No collaboration
• A new idea distributed PSE
• Integrates heterogeneous components into an
  environment
• Transparent access to distributed resources
• Collaborative modelling and simulation
• Web-accessed

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Grid Enabled PSE

• PSE subsystems consist of
• Modelling, design and simulation tools
• Experimental devices
• Visualisation and knowledge-based tools
• Grid enabled PSE Requirements
• The software components must all be interoperable
• Resource availability predictable over wide area
• Must allow composition of heterogeneous
  collections of model and data
• Must be able to access, mine and join data from
  multiple sources
• Collaborative, secure, seamless, scalable.

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An Example of Distributed PSE NetSolve/GridSolve

• A client-server system for remote solutions of
  complex scientific problems
• On request performs computational tasks on a set
  of servers
• Based on agents or resource brokers
• Access to languages C, Fortran, MatLab,
  Mathematica
• Application Service Provider supports the
  resources for a particular set of services

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Some Application Characteristics of Grid

• Complex models simulations
• Large volumes of input / generated data
• High degree of User interaction
• Offline / online data processing /
  visualization
• Distinct user interfaces
• Multidisciplinary
• Heterogeneous models / components
• Interactions among multiple users,
  collaboration
• Require parallel and distributed processing

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Concept of a Grid

• Gathers a large diversity of distributed physical
  resources
• supercomputers and parallel machines
• clusters of PCs
• massive storage systems
• databases and data sources
• special devices
• Access is globally unified through virtual
  layers
• solve new or larger problems by aggregating
  available resources
• access a large diversity of computation, data and
  information services
• enable coordinated resource sharing and
  collaboration across virtual organizations
• Users to access Grid through a global shell or
  Grid portals

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Grids Are Very Complex Systems

• Aim at providing unifying abstractions to the
  end-user
• Large-scale universe of distributed,
  heterogeneous, and dynamic resources
• Critical aspects
• Distributed
• Large-scale
• Multiple administrative domains
• Security and access control
• Heterogeneity
• Dynamic

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Is Grid Technology New?

• No There are many predecessors, with different
  names (not grid)
• Yes New problems are being tackled today, on a
  larger scale than ever before
• How do you use thousands of computers
• in different institutions
• With different security constraints
• Separated by private networks and firewalls
• that are not all identical
• in a reliable fashion
• without losing your mind?

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Why Now?

• Moores law improvements in computing produce
  highly functional end systems
• The Internet and burgeoning wired and wireless
  communications networks provide universal
  connectivity
• Changing modes of working and problem solving
  emphasize teamwork, computation
• Network exponentials produce dramatic changes in
  geometry and geography

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Network Exponentials

• Network vs. computer performance
• Computer speed doubles every 18 months
• Network speed doubles every 9 months
• Difference order of magnitude per 5 years
• 1986 to 2000
• Computers x 500
• Networks x 340,000
• 2001 to 2010
• Computers x 60
• Networks x 4000

Moores Law vs. storage improvements vs. optical
improvements. Graph from Scientific American
(Jan-2001) by Cleo Vilett, source Vined Khoslan,
Kleiner, Caufield and Perkins.
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Grid Types
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Types of Grid

• From an application perspective, there are three
  types of grids compute grids, data grids,
  collaborative grids.
• From a topology perspective, it can be argued
  that there are additional types, including
  clusters, intra-grids, extra-grids, and
  inter-grids.
• In reality, clusters, intra-grids, extra-grids,
  and inter-grids are better defined as stages of
  evolution.

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Types of Grid (cont.)

• Cluster Grid
• Beowulf clusters
• Enterprise Grid, Campus
• Grid, Intra-Grid
• Departmental clusters, servers and PC network
• Utility Grid
• Access resources over internet on demand
• Global Grid, Inter-grid
• White Rose Grid, National Grid Service, Particle
  physics data grid

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Types of Grid (cont.)
SourceThe Grid, Ian Foster, Argonne National
Laboratory,University of Chicago, Globus Alliance
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Types of Grid (cont.)

• Compute Grid
• Essentially a collection of distributed computing
  resources, within or across locations.
• These resources are aggregated to act as a
  unified processing resource or virtual
  supercomputer.
• Data Grid
• Provides wide area, secure access to current
  data.
• Enables users and applications to manage and
  efficiently use database information from
  distributed locations.
• Like compute grids, data grids also rely on
  software for secure access and usage policies.
• Will be a key element in the rollout of Web
  services.

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Grid Types - Collaborative

• Internet videoconferencing
• Collaborative Visualisation

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EU DataGrid Project

• EU DataGrid project (until 2004)
• Large-scale environment for accessing and
  analysing large amounts of data
• High energy physics, Biology, Earth observation
• Petabytes of data (1 000 000 Gig)
• Thousands of researchers
• Scalable storage of datasets replicated,
  catalogued, distributed in distinct sites

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Grid Evolution
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Grid Architecture
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Grid Architecture
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Elements of a Grid Architecture

• User interfaces and grid portals
• Applications and PSEs
• Development environments and tools
• Grid middleware (ie. Globus, Condor, Legion)
• Resource management and scheduling
• Information registration and discovery
• Authentication, Security
• Storage access, and communication
• Heterogeneous and physical resources, and network
  infrastructure

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Middleware

• A connectivity software consisting of a set of
  enabling services that allow multiple processes
  running on one or more machines to interact
  across a network.
• Examples of Grid computing middleware
• Globus Toolkit
• Open Source Middleware
• Software services and libraries for resource
  resource monitoring, discovery, and management,
  plus security and file management
• BOINC (Berkeley Open Infrastructure for Network
  Computing)
• Open Source Middleware
• Condor-G
• An enhanced version of Condor that uses the
  Globus toolkit to manage jobs on the Grid

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Layered Grid Architecture(by Analogy to Internet
Architecture)
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Protocols, Services,and APIs Occur at Each Level
Applications
Languages/Frameworks
Collective Service APIs and SDKs
Collective Service Protocols
Collective Services
Resource APIs and SDKs
Resource Service Protocols
Resource Services
Connectivity APIs
Connectivity Protocols
Local Access APIs and Protocols
Fabric Layer
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Important Points

• Built on Internet protocols and services
• Communication, routing, name resolution, etc.
• Layering here is conceptual, does not imply
  constraints on who can call what
• Protocols/services/APIs/SDKs will, ideally, be
  largely self-contained
• Some things are fundamental e.g., communication
  and security
• But, advantageous for higher-level functions to
  use common lower-level functions

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Fabric Layer Protocols and Services

• Just what you would expect the diverse mix of
  resources that may be shared
• Individual computers, Condor pools, file systems,
  archives, metadata catalogs, networks, sensors,
  etc., etc.
• Few constraints on low-level technology
  connectivity and resource level protocols form
  the neck in the hourglass
• Defined by interfaces not physical characteristics

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Connectivity Layer Protocols and Services

• Communication
• Internet protocols IP, DNS, routing, etc.
• Security Grid Security Infrastructure (GSI)
• Uniform authentication, authorization, and
  message protection mechanisms in
  multi-institutional setting
• Single sign-on, delegation, identity mapping
• Public key technology, SSL, X.509, GSS-API
• Supporting infrastructure Certificate
  Authorities, certificate key management,

GSI www.gridforum.org/security/gsi
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Resource Layer Protocols and Services(as used in
Globus)

• Grid Resource Allocation Management (GRAM)
• Remote allocation, reservation, monitoring,
  control of compute resources
• GridFTP protocol (FTP extensions)
• High-performance data access transport
• Grid Resource Information Service (GRIS)
• Access to structure state information
• Others emerging Catalog Access, Code Repository
  Access, Accounting, etc.
• All built upon connectivity layer GSI (Grid
  Security Infrastructure) and IP

GRAM, GridFTP, GRIS www.globus.org
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Collective Layer Protocols and Services

• Index servers aka metadirectory services
• Custom views on dynamic resource collections
  assembled by a community
• Resource brokers (ie. Condor Matchmaker)
• Resource discovery and allocation
• Replica catalogs
• Replication services
• Co-reservation and co-allocation services
• Workflow management services
• etc.

Condor www.cs.wisc.edu/condor
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Conclusions

• The vision of grid computing is to virtualize
  computing, with the goal of creating a utility
  computing model over a distributed set of
  resources. Computing electricity (utility)
• Supercomputers lt-gt power stations
• Network lt-gt electrical cables
• Grid is used to create VOs for sharing resources
  and collaborations.
• Grid middleware (ie. Globus) for VOs is analogous
  to the operating system of a computer or server.
• Grid may be as important as WWW

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Cloud Computing

• Part II

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What is The Cloud?

• Metaphor for the Internet
• Cloud drawing used in the past for (telephone)
  networks
• Used as an abstraction for the underlying network

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Cloud Computing
http//techsling.com/2010/03/challenges-of-cloud-c
omputing/
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Cloud Examples
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What is Cloud Computing?

• The Cloud is a natural evolution of distributed
  computing and of the widespread adaption of
  virtualization and SOA. In Cloud Computing,
  IT-related capabilities and resources are
  provided as services, via the Internet and
  on-demand, accessible without requiring detailed
  knowledge of the underlying technology.
• (from the Call for Papers of the 3rd IEEE
  International Conference on Cloud Computing
  Technology and Science (IEEE CloudCom 2011))

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What is Cloud Computing? A Definition by NIST

• Cloud computing is a model for enabling
  convenient, on-demand network access to a shared
  pool of configurable computing resources (ie.
  networks, servers, storage, applications, and
  services) that can be rapidly provisioned and
  released with minimal management effort or
  service provider interaction.

http//www.nist.gov/itl/cloud/
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What is Cloud Computing? A Definition by NIST

• This cloud model promotes availability and is
  composed of
• five essential characteristics (On-demand
  self-service, Broad network access, Resource
  pooling, Rapid elasticity, Measured Service)
• three service models (Cloud Software as a Service
  (SaaS), Cloud Platform as a Service (PaaS), Cloud
  Infrastructure as a Service (IaaS)) and,
• four deployment models (Private cloud, Community
  cloud, Public cloud, Hybrid cloud).
• Key enabling technologies include (1) fast
  wide-area networks, (2) powerful, inexpensive
  server computers, and (3) high-performance
  virtualization for commodity hardware. 

http//www.nist.gov/itl/cloud/
63
Essential Characteristics

• On-demand self-service. A consumer can
  unilaterally provision computing capabilities,
  such as server time and network storage, as
  needed automatically without requiring human
  interaction with each services provider.
• Broad network access. Capabilities are available
  over the network and accessed through standard
  mechanisms that promote use by heterogeneous thin
  or thick client platforms (ie. mobile phones,
  laptops, and PDAs).

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Essential Characteristics (cont.)

• Resource pooling. The providers computing
  resources are pooled to serve multiple consumers
  using a multi-tenant model, with different
  physical and virtual resources dynamically
  assigned and reassigned according to consumer
  demand.
• There is a sense of location independence in that
  the customer generally has no control or
  knowledge over the exact location of the provided
  resources but may be able to specify location at
  a higher level of abstraction (ie. country,
  state, or datacenter). Examples of resources
  include storage, processing, memory, network
  bandwidth, and virtual machines.

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Essential Characteristics (contd)

• Rapid elasticity. Capabilities can be rapidly and
  elastically provisioned, in some cases
  automatically, to quickly scale out, and rapidly
  released to quickly scale in. To the consumer,
  the capabilities available for provisioning often
  appear to be unlimited and can be purchased in
  any quantity at any time.
• Measured service. Cloud systems automatically
  control and optimize resource use by leveraging a
  metering capability at some level of abstraction
  appropriate to the type of service (e.g.,
  storage, processing, bandwidth, and active user
  accounts). Resource usage can be monitored,
  controlled, and reported, providing transparency
  for both the provider and consumer of the
  utilized service.

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Cloud Service Models

• Software as a Service (SaaS)
• Platform as a Service (PaaS)
• Infrastructure as a Service (IaaS)
• More specific services, ie. Database as a Service
  (DbaaS)
• Cloud clients may be the Web browser, a mobile
  app or a terminal emulator

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Software as a Service (SaaS)

• The consumer uses the providers applications
  running on a cloud infrastructure.
• The consumer does not manage or control the
  underlying cloud infrastructure
• Possible exception of limited user-specific
  application configuration settings.
• Examples include services like
• Gmail
• Mendeley
• Dropbox
• Splashup (image editing)
• Amazon Cloud Drive

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Platform as a Service (PaaS)

• Deploy consumer-created or acquired applications
• Consumer does not manage or control the
  underlying cloud infrastructure
• Consumer has control over the deployed
  applications and possibly application hosting
  environment configurations
• Execution runtime, database, web server
• Examples
• Amazon Elastic Compute Cloud (Amazon EC2)
• Google App Engine

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Infrastructure as a Service (IaaS)

• Provision processing, storage, networks, and
  other fundamental computing resources
• Consumer is able to deploy and run arbitrary
  software
• Consumer does not manage or control the
  underlying cloud infrastructure
• Consumer has control over operating systems,
  storage, deployed applications, and possibly
  limited control of select networking components
  (ie. host firewalls).
• Billing on a utility computing basis (cost will
  reflect the amount of resources allocated and
  consumed)
• Example Virtual machines with firewalls

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Cloud Deployment Models

• Private Cloud
• Operated solely for an organization
• Managed by the organisation or a third party
• May exist on premise or off premise
• Community Cloud
• Shared by several organisations
• Supports a specific community that has shared
  concerns
• Managed by the organisations or a third party
• May exist on premise or off premise
• Public Cloud
• The cloud infrastructure is made available to the
  general public or a large industry group
• Owned by an organisation selling cloud services

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Cloud Deployment Models (cont.)

• Hybrid Cloud
• Cloud infrastructure is a composition of two or
  more clouds (private, community, or public)
• Single clouds remain unique entities but are
  bound together by standardised or proprietary
  technology

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Grid and Cloud Computing

• Grid Computing is often a prerequisite for Cloud
  Computing
• Cloud Computing evolves from Grid Computing
• Both are scalable (through load balancing).
  Resources (CPU, network, storage) are allocated
  on demand
• Grids are used for data and computationally
  intensive operations (few but large allocation
  requests)
• Cloud services can also provide standard
  operations (lots of small allocation requests)

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Advantages and Disadvantages

• Some Advantages
• No need to manage your own hardware/infrastructure
  
• No need to bother about backups etc.
• More environmentally friendly
• Some Disadvantages
• No control over software (like in proprietary
  programs)
• Privacy
• Security
• Ownership

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Criticism

• "The interesting thing about cloud computing is
  that we've redefined cloud computing to include
  everything that we already do. The computer
  industry is the only industry that is more
  fashion-driven than women's fashion. Maybe I'm an
  idiot, but I have no idea what anyone is talking
  about. What is it? It's complete gibberish. It's
  insane. When is this idiocy going to stop?
  (Larry Ellison, founder of Oracle)
• "It's stupidity. It's worse than stupidity it's
  a marketing hype campaign. (Richard Stallmann,
  founder of the Free Software Foundation)

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References

• The Physiology of the Grid, An Open Services
  Architecture for Distributed Systems Integration,
  by Ian Foster et al, 2002.
• The Anatomy of the Grid, Enabling Scalable
  Virtual Organization, Ian Foster, Carl Kesselman,
  Steven Tuecke, 2001.
• Grid Networking, by Rick Thompson, at light
  reading web site.
• Open Grid Forum www.ggf.org
• Grid Computing, Joseph Fellenstein, IBM Press,
  2004
• Grid 2 Blueprint for a New Computing
  Infrastructure, Ian Foster et al, Morgan Kaufmann
  Publishers, 2004

76
References

• Next Generation Optical Networks The
  Convergence of IP Intelligence and Optical
  Technologies, P. Tomsu, C. Schmutzer, Prentice
  Hall, 2002
• Globus Alliance http//www.globus.org/
• Introduction to Grid Computing (ppt slides), The
  Globus Project, Argonne National Laboratory, USC
  Information Sciences Institute
• http//www.nist.gov/itl/cloud/ (visited
  15.05.2011)
• http//www.ibm.com/developerworks/web/library/wa-c
  loudgrid/ (visited 15.05.2011)
• http//www.guardian.co.uk/technology/2008/sep/29/c
  loud.computing
• .richard.stallman (visited 15.05.2011)
• http//wp.nmc.org/horizon2009/chapters/cloud-compu
  ting/ (visited 15.05.2011)

77
Recommended Reading (1)

• Besides the sources listed on the References
  slides, you are recommended to have a read of the
  following web sites
• What is the Grid? A three point checklist by
  Ian Foster http//www.gridtoday.com/02/0722/100136
  .html
• SETI_at_home http//setiathome.berkeley.edu/
• GridPP http//www.gridpp.ac.uk/
• TeraGrid Project http//www.teragrid.org/
• World Community Grid http//www.worldcommunitygr
  id.org
• ZDNet-Grid http//www.zdnet.com.au/news/business/
  soa/Australian-grid-computing-Creating-science-fac
  t/0,139023166,120267287-1,00.htm
• AgeCluster http//www.gridbus.org/papers/theagecl
  uster.html
• World Wide Grid http//gridbus.cs.mu.oz.au/sc2003
  /

78
Recommended Reading (2)

• Grid Computing Info Centre
• http//www.gridcomputing.com/
• Have a look at the following websites to learn
  the real-life examples of grid computing
  applications.
• EGEE (Enabling Grid for E-sciencE)
  http//www.eu-egee.org/
• SETI_at_home http//setiathome.berkeley.edu/
• GridPP http//www.gridpp.ac.uk/
• TeraGrid Project http//www.teragrid.org/
• World Community Grid http//www.worldcommunitygr
  id.org