Cs6703 grid and cloud computing unit 1

CS6703 GRID AND CLOUD COMPUTING
Unit 1
Dr Gnanasekaran Thangavel
Professor and Head
Faculty of Information Technology
R M K College of Engineering and
Technology

UNIT I INTRODUCTION
Evolution of Distributed computing: Scalable computing
over the Internet – Technologies for network based
systems – clusters of cooperative computers - Grid
computing Infrastructures – cloud computing -
service oriented architecture – Introduction to Grid
Architecture and standards – Elements of Grid –
Overview of Grid Architecture.
2 Dr Gnanasekaran Thangavel 8/30/2016

Distributed Computing
Definition
“A distributed system consists of multiple
autonomous computers that communicate through
a computer network.
“Distributed computing utilizes a network of many
computers, each accomplishing a portion of an
overall task, to achieve a computational result
much more quickly than with a single computer.”
“Distributed computing is any computing that
involves multiple computers remote from each
other that each have a role in a computation3 Dr Gnanasekaran Thangavel 8/30/2016

Introduction
 A distributed system is one in which hardware or
software components located at networked
computers communicate and coordinate their actions
only by message passing.
 In the term distributed computing, the word
distributed means spread out across space. Thus,
distributed computing is an activity performed on a
spatially distributed system.
 These networked computers may be in the same4 Dr Gnanasekaran Thangavel 8/30/2016

Introduction
Cooperation
Cooperation
Cooperation
Internet
Large-scale
Application
Resource
Management
Subscription
Distribution
Distribution Distribution
Distribution
Agent
Agent Agent
Agent
Job Request

Motivation
 Inherently distributed applications
 Performance/cost
 Resource sharing
 Flexibility and extensibility
 Availability and fault tolerance
 Scalability
 Network connectivity is increasing.
 Combination of cheap processors often more cost-effective
than one expensive fast system.
 Potential increase of reliability.

History
 1975 – 1985
Parallel computing was favored in the early years
Primarily vector-based at first
Gradually more thread-based parallelism was introduced
The first distributed computing programs were a pair of
programs called Creeper and Reaper invented in 1970s
Ethernet that was invented in 1970s.
ARPANET e-mail was invented in the early 1970s and
probably the earliest example of a large-scale distributed
application.

History
 1985 -1995
Massively parallel architectures start rising and message
passing interface and other libraries developed
Bandwidth was a big problem
The first Internet-based distributed computing project was
started in 1988 by the DEC System Research Center.
Distributed.net was a project founded in 1997 - considered
the first to use the internet to distribute data for calculation
and collect the results,

History
 1995 – Today
Cluster/grid architecture increasingly dominant
Special node machines eschewed in favor of COTS
technologies
Web-wide cluster software
Google take this to the extreme (thousands of nodes/cluster)
SETI@Home started in May 1999 - analyze the radio signals
that were being collected by the Arecibo Radio Telescope in
Puerto Rico.

Goal
 Making Resources Accessible
Data sharing and device sharing
 Distribution Transparency
Access, location, migration, relocation, replication,
concurrency, failure
 Communication
Make human-to-human comm. easier. E.g.. : electronic mail
 Flexibility
Spread the work load over the available machines in the
most cost effective way
 To coordinate the use of shared resources
 To solve large computational problem10 Dr Gnanasekaran Thangavel 8/30/2016

Characteristics
Resource Sharing
Openness
Concurrency
Scalability
Fault Tolerance
Transparency

Architecture
Client-server
3-tier architecture
N-tier architecture
loose coupling, or tight coupling
Peer-to-peer
Space based

Examples of commercial application :
Database Management System
Distributed computing using mobile agents
Local intranet
Internet (World Wide Web)
JAVA Remote Method Invocation (RMI)
Application

Distributed Computing Using Mobile Agents
 Mobile agents can be wandering around in a network
using free resources for their own computations.

Local Intranet
 A portion of Internet that is separately administered & supports
internal sharing of resources (file/storage systems and printers) is
called local intranet.

Internet
 The Internet is a global system of interconnected computer
networks that use the standardized Internet Protocol Suite
(TCP/IP).

JAVA RMI
 Embedded in language Java:-
 Object variant of remote procedure call
 Adds naming compared with RPC (Remote Procedure Call)
 Restricted to Java environments
RMI Architecture

Categories of Applications in distributed
computing Science
 Life Sciences
 Cryptography
 Internet
 Financial
 Mathematics
 Language
 Art
 Puzzles/Games
 Miscellaneous
 Distributed Human Project
 Collaborative Knowledge Bases
 Charity

Advantages
 Economics:-
 Computers harnessed together give a better price/performance ratio
than mainframes.
 Speed:-
 A distributed system may have more total computing power than a
mainframe.
 Inherent distribution of applications:-
 Some applications are inherently distributed. E.g., an ATM-banking
application.
 Reliability:-
 If one machine crashes, the system as a whole can still survive if
you have multiple server machines and multiple storage devices
(redundancy).
 Extensibility and Incremental Growth:-
 Possible to gradually scale up (in terms of processing power and
functionality) by adding more sources (both hardware and software).

Disadvantages
 Complexity :-
 Lack of experience in designing, and implementing a distributed
system. E.g. which platform (hardware and OS) to use, which
language to use etc.
 Network problem:-
 If the network underlying a distributed system saturates or goes
down, then the distributed system will be effectively disabled thus
negating most of the advantages of the distributed system.
 Security:-
 Security is a major hazard since easy access to data means easy
access to secret data as well.

Issues and Challenges
 Heterogeneity of components :-
variety or differences that apply to computer hardware,
network, OS, programming language and implementations
by different developers.
All differences in representation must be deal with if to do
message exchange.
Example : different call for exchange message in UNIX
different from Windows.
 Openness:-
System can be extended and re-implemented in various
ways.
Cannot be achieved unless the specification and
documentation are made available to software developer.
The most challenge to designer is to tackle the complexity of21 Dr Gnanasekaran Thangavel 8/30/2016

Issues and Challenges cont…
Transparency:-
Aim : make certain aspects of distribution are
invisible to the application programmer ; focus
on design of their particular application.
They not concern the locations and details of
how it operate, either replicated or migrated.
Failures can be presented to application
programmers in the form of exceptions – must
be handled.22 Dr Gnanasekaran Thangavel 8/30/2016

 Transparency:-
This concept can be summarize as shown in this
Figure:

 Security:-
Security for information resources in distributed system
have 3 components :
a. Confidentiality : protection against disclosure to
unauthorized individuals.
b. Integrity : protection against alteration/corruption
c. Availability : protection against interference with the
means to access the resources.
The challenge is to send sensitive information over Internet
in a secure manner and to identify a remote user or other
agent correctly.24 Dr Gnanasekaran Thangavel 8/30/2016

Issues and Challenges cont..
 Scalability :-
Distributed computing operates at many different scales,
ranging from small Intranet to Internet.
A system is scalable if there is significant increase in the
number of resources and users.
The challenges is :
a. controlling the cost of physical resources.
b. controlling the performance loss.
c. preventing software resource running out.
d. avoiding performance bottlenecks.

 Failure Handling :-
Failures in a distributed system are partial – some
components fail while others can function.
That’s why handling the failures are difficult
a. Detecting failures : to manage the presence of failures
cannot be detected but may be suspected.
b. Masking failures : hiding failure not guaranteed in the
worst case.
 Concurrency :-
Where applications/services process concurrency, it will
effect a conflict in operations with one another and produce
inconsistence results.26 Dr Gnanasekaran Thangavel 8/30/2016

Conclusion
 The concept of distributed computing is the most efficient
way to achieve the optimization.
 Distributed computing is anywhere : intranet, Internet or
mobile ubiquitous computing (laptop, PDAs, pagers, smart
watches, hi-fi systems)
 It deals with hardware and software systems, that contain
more than one processing / storage and run in concurrently.
 Main motivation factor is resource sharing; such as files ,
printers, web pages or database records.
 Grid computing and cloud computing are form of distributed
computing.

Grid Computing
Grid computing is a form of distributed computing whereby a
"super and virtual computer" is composed of a cluster of
networked, loosely coupled computers, acting in concert to
perform very large tasks.
Grid computing (Foster and Kesselman, 1999) is a growing
technology that facilitates the executions of large-scale
resource intensive applications on geographically distributed
computing resources.
Facilitates flexible, secure, coordinated large scale resource
sharing among dynamic collections of individuals, institutions,
and resource
Enable communities (“virtual organizations”) to share
8/30/201628 Dr Gnanasekaran Thangavel

Criteria for a Grid:
Coordinates resources that are not subject to centralized
control.
Uses standard, open, general-purpose protocols and
interfaces.
Delivers nontrivial qualities of service.
Benefits
 Exploit Underutilized resources
 Resource load Balancing
 Virtualize resources across an enterprise
 Data Grids, Compute Grids
 Enable collaboration for virtual organizations

Grid Applications
Data and computationally intensive applications:
This technology has been applied to computationally-intensive scientific,
mathematical, and academic problems like drug discovery, economic
forecasting, seismic analysis back office data processing in support of e-
commerce
 A chemist may utilize hundreds of processors to screen thousands of
compounds per hour.
 Teams of engineers worldwide pool resources to analyze terabytes of
structural data.
 Meteorologists seek to visualize and analyze petabytes of climate data
with enormous computational demands.
Resource sharing
 Computers, storage, sensors, networks, …
 Sharing always conditional: issues of trust, policy, negotiation, payment,
…

Grid Topologies
• Intragrid
– Local grid within an organization
– Trust based on personal contracts
• Extragrid
– Resources of a consortium of organizations
connected through a (Virtual) Private Network
– Trust based on Business to Business contracts
• Intergrid
– Global sharing of resources through the internet
– Trust based on certification

8/30/2016Dr Gnanasekaran Thangavel32
Computational Grid
“A computational grid is a hardware and software
infrastructure that provides dependable, consistent, pervasive,
and inexpensive access to high-end computational
capabilities.”
”The Grid: Blueprint for a New Computing Infrastructure”,
Kesselman & Foster
Example : Science Grid (US Department of Energy)

Data Grid
 A data grid is a grid computing system that deals with data —
the controlled sharing and management of large amounts
of distributed data.
 Data Grid is the storage component of a grid environment.
Scientific and engineering applications require access to large
amounts of data, and often this data is widely distributed. A
data grid provides seamless access to the local or remote data
required to complete compute intensive calculations.
Example :
Biomedical informatics Research Network (BIRN),
the Southern California earthquake Center (SCEC). 8/30/201633 Dr Gnanasekaran Thangavel

Methods of Grid Computing
 Distributed Supercomputing
 High-Throughput Computing
 On-Demand Computing
 Data-Intensive Computing
 Collaborative Computing
 Logistical Networking

Distributed Supercomputing
 Combining multiple high-capacity resources on a
computational grid into a single, virtual distributed
supercomputer.
 Tackle problems that cannot be solved on a single
system.

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

Collaborative Computing
 Concerned primarily with enabling and enhancing human-to-
human interactions.
 Applications are often structured in terms of a virtual shared
space.
Data-Intensive Computing
 The focus is on synthesizing new information from data that is
maintained in geographically distributed repositories, digital
libraries, and databases.
 Particularly useful for distributed data mining.

Logistical Networking
 Logistical networks focus on exposing storage
resources inside networks by optimizing the global
scheduling of data transport, and data storage.
 Contrasts with traditional networking, which does not
explicitly model storage resources in the network.
 high-level services for Grid applications
 Called "logistical" because of the analogy it bears with
the systems of warehouses, depots, and distribution
channels. 8/30/201638 Dr Gnanasekaran Thangavel

P2P Computing vs Grid Computing
Differ in Target Communities
Grid system deals with more complex, more
powerful, more diverse and highly
interconnected set of resources than
P2P.
VO

A typical view of Grid environment
User
Resource Broker
Grid Resources
Grid Information Service
A User sends computation or data
intensive application to Global Grids in
order to speed up the execution of the
application.
A Resource Broker distribute the jobs in an
application to the Grid resources based on user’s
QoS requirements and details of available Grid
resources for further executions.
Grid Resources (Cluster, PC, Supercomputer,
database, instruments, etc.) in the Global Grid
execute the user jobs.
Grid Information Service system
collects the details of the available Grid
resources and passes the information
to the resource broker.
Computation result
Grid application
Computational jobs
Details of Grid resources
Processed jobs
1
2
3
4

Grid Middleware
 Grids are typically managed by grid ware -
a special type of middleware that enable sharing and manage grid
components based on user requirements and resource attributes (e.g.,
capacity, performance)
 Software that connects other software components or applications to
provide the following functions:
Run applications on suitable available resources
– Brokering, Scheduling
Provide uniform, high-level access to resources
– Semantic interfaces
– Web Services, Service Oriented Architectures
Address inter-domain issues of security, policy, etc.
– Federated Identities
Provide application-level status
monitoring and control 8/30/201641 Dr Gnanasekaran Thangavel

Middleware
 Globus –chicago Univ
 Condor – Wisconsin Univ – High throughput
computing
 Legion – Virginia Univ – virtual workspaces-
collaborative computing
 IBP – Internet back pane – Tennesse Univ –
logistical networking
 NetSolve – solving scientific problems in
heterogeneous env – high throughput & data
intensive 8/30/201642 Dr Gnanasekaran Thangavel

Two Key Grid Computing Groups
The Globus Alliance (www.globus.org)
 Composed of people from:
Argonne National Labs, University of Chicago, University of Southern
California Information Sciences Institute, University of Edinburgh and
others.
 OGSA/I standards initially proposed by the Globus Group
The Global Grid Forum (www.ggf.org)
 Heavy involvement of Academic Groups and Industry
 (e.g. IBM Grid Computing, HP, United Devices, Oracle, UK e-Science
Programme, US DOE, US NSF, Indiana University, and many others)
 Process
 Meets three times annually
 Solicits involvement from industry, research groups, and academics8/30/201643 Dr Gnanasekaran Thangavel

Some of the Major Grid Projects
Name URL/Sponsor Focus
EuroGrid, Grid
Interoperability (GRIP)
eurogrid.org
European Union
Create tech for remote access to super comp resources
& simulation codes; in GRIP, integrate with Globus
Toolkit™
Fusion Collaboratory fusiongrid.org
DOE Off. Science
Create a national computational collaboratory for fusion
research
Globus Project™ globus.org
DARPA, DOE, NSF,
NASA, Msoft
Research on Grid technologies; development and
support of Globus Toolkit™; application and deployment
GridLab gridlab.org
European Union
Grid technologies and applications
GridPP gridpp.ac.uk
U.K. eScience
Create & apply an operational grid within the U.K. for
particle physics research
Grid Research Integration
Dev. & Support Center
grids-center.org
NSF
Integration, deployment, support of the NSF
Middleware Infrastructure for research & education

Grid Architecture

The Hourglass Model
 Focus on architecture issues
 Propose set of core services as basic
infrastructure
 Used to construct high-level, domain-specific
solutions (diverse)
 Design principles
 Keep participation cost low
 Enable local control
 Support for adaptation
 “IP hourglass” model
Diverse global services
Core
services
Local OS
A p p l i c a t i o n s

Layered Grid Architecture
(By Analogy to Internet Architecture)
Application
Fabric
“Controlling things locally”: Access to, & control
of, resources
Connectivity
“Talking to things”: communication (Internet
protocols) & security
Resource
“Sharing single resources”: negotiating access,
controlling use
Collective
“Coordinating multiple resources”: ubiquitous
infrastructure services, app-specific distributed
services
Internet
Transport
Application
Link
InternetProtocolArchitecture

Example:
Data Grid Architecture
Discipline-Specific Data Grid Application
Coherency control, replica selection, task management, virtual data catalog,
virtual data code catalog, …
Replica catalog, replica management, co-allocation, certificate authorities,
metadata catalogs,
Access to data, access to computers, access to network performance data, …
Communication, service discovery (DNS), authentication, authorization,
delegation
Storage systems, clusters, networks, network caches, …
Collective
(App)
App
Collective
(Generic)
Resource
Connect
Fabric

Simulation tools
 GridSim – job scheduling
 SimGrid – single client multiserver scheduling
 Bricks – scheduling
 GangSim- Ganglia VO
 OptoSim – Data Grid Simulations
 G3S – Grid Security services Simulator – security
services

Simulation tool
GridSim is a Java-based toolkit for modeling, and
simulation of distributed resource management and
scheduling for conventional Grid environment.
GridSim is based on SimJava, a general purpose discrete-
event simulation package implemented in Java.
All components in GridSim communicate with each other
through message passing operations defined by SimJava.

Salient features of the GridSim
 It allows modeling of heterogeneous types of resources.
 Resources can be modeled operating under space- or time-
shared mode.
 Resource capability can be defined (in the form of MIPS
(Million Instructions Per Second) benchmark.
 Resources can be located in any time zone.
 Weekends and holidays can be mapped depending on
resource’s local time to model non-Grid (local) workload.
 Resources can be booked for advance reservation.
 Applications with different parallel application models can
be simulated.

Salient features of the GridSim
 Application tasks can be heterogeneous and they can be
CPU or I/O intensive.
 There is no limit on the number of application jobs that can be
submitted to a resource.
 Multiple user entities can submit tasks for execution
simultaneously in the same resource, which may be time-
shared or space-shared. This feature helps in building
schedulers that can use different market-driven economic
models for selecting services competitively.
 Network speed between resources can be specified.
 It supports simulation of both static and dynamic schedulers.
 Statistics of all or selected operations can be recorded and
they can be analyzed using GridSim statistics analysis
methods.

A Modular Architecture for GridSim Platform and Components.
Appn Conf Res Conf User Req Grid Sc Output
Application, User, Grid Scenario’s input and Results
Grid Resource Brokers or Schedulers
…
Appn
modeling
Res entity Info serv Job mgmt Res alloc Statis
GridSim Toolkit
Single CPU SMPs Clusters Load Netw Reservation
Resource Modeling and Simulation
SimJava Distributed SimJava
Basic Discrete Event Simulation Infrastructure
PCs Workstation ClustersSMPs Distributed Resources
Virtual Machine

Web 2.0, Clouds, and Internet of Things
HPC: High - Performance Computing HTC: High - Throughput Computing
P2P: Peer to Peer MPP: Massively Parallel Processors

55
What is a Service Oriented Architecture?

56
What is a Service Oriented Architecture (SOA)?
A method of design, deployment, and
management of both applications and the
software infrastructure where:
All software is organized into business
services that are network accessible and
executable.
Service interfaces are based on public
standards for interoperability.

57
Key Characteristics of SOA
Quality of service, security and
performance are specified.
Software infrastructure is responsible for
managing.
Services are cataloged and discoverable.
Data are cataloged and discoverable.
Protocols use only industry standards.

58
What is a “Service”?
A Service is a reusable component.
A Service changes business data from one state
to another.
A Service is the only way how data is accessed.
If you can describe a component in WSDL, it is a
Service.

59
Information Technology is Not SOA
Business Mission
Information Management
Information Systems
Systems Design
Computing & Communications
Information
Technology
SOA

60
Why Getting SOA Will be Difficult
 Managing for Projects:
Software: 1 - 4 years
Hardware: 3 - 5 years;
Communications: 1 - 3 years;
Project Managers: 2 - 4 years;
Reliable funding: 1 - 4 years;
User turnover: 30%/year;
Security risks: 1 minute or less.
 Managing for SOA:
Data: forever.
Infrastructure: 10+ years.

61
Why Managing Business Systems is Difficult?
 40 Million lines of code in Windows XP is unknowable.
 Testing application (3 Million lines) requires >1015
tests.
 Probability correct data entry for a supply item is
<65%.
 There are >100 formats that identify a person in DoD.

62
How to View Organizing for SOA
STABILITY HERE
VARIETY HERE
C orp o rate Po licy, C o rp o rate Stan d ard s, Referen ce M o d els,
D ata M anagem en t and To o ls, I n tegrated System s
C o nfigu ratio n D ata Base, Shared C o m p utin g an d
Telecom m un icatio n s
A p p licatio ns D evelo p m en t & M ainten ance
EN T ERPRI SE LEV EL
PRO C ESS LEV EL
BU SI N ESS LEV EL
A PPLI C AT I O N LEV EL
LO C A L LEV EL
G rap h ic I n fo W in d o w , Perso nal To o ls, I n q u iry Lan gu ages
C u sto m iz ed A p plicatio n s, Pro to typ in g To ols, Lo cal
A pp licatio n s and Files
A p p l icati on s
Secu rity Barri er
Bu si n ess
Secu ri ty Barrier
Process
Secu ri ty B arrier
Priv acy an d
I n d i v i d u al
Secu rity Barri er
G LO BA L LEV EL
I n d ustry Stan d ard s, C o m m ercial O ff-the-Sh elf
Pro du cts an d Services
PERSO N A L LEV ELPrivate A p plication s and Files
Fu n ctio n al Pro cess A
Fu n ction al Pro cess B
Fu n ction al Pro cess C
Fun ction al Pro cess D
OSDService A Service B

63
SOA Must Reflect Timing
Corporate Policy, Corporate Standards, Reference Models,
Data Management and Tools, Integrated Systems
Configuration Data Base, Shared Computing and
Telecommunications, Security and Survivability
Business A Business B
Infrastructure
Support
Applications Development & Maintenance
ENTERPRISE
PROCESS
BUSINESS
APPLICATION
LOCAL
Graphic InfoWindow, Personal Tools, Inquiry Languages
Customized Applications, Prototyping Tools, Local
Applications and Files
GLOBAL
Industry Standards, Commercial Off-the-Shelf
Products and Services
PERSONALPrivate Applications and Files
Functional Process A
Functional Process B
Functional Process C
Functional Process D
LONG TERM
STABILITY &
TECHNOLOGY
COMPLEXITY
SHORT TERM
ADAPTABILITY &
TECHNOLOGY
SIMPLICITY

64
SOA Must Reflect Conflicting Interests
Enterprise
Missions
Organizations
Local
Personal

65
Organization of Infrastructure Services
Infrastructure
Services
(Enterprise Information)
Data
Services
Security
Services
Computing
Services
Communication
Services
Application
Services

66
Organization of Data Services
Data
Services
Discovery
Services
Management
Services
Collaboration
Services
Interoperability
Services
Semantic
Services

67
Data Interoperability Policies
 Data are an enterprise resource.
 Single-point entry of unique data.
 Enterprise certification of all data definitions.
 Data stewardship defines data custodians.
 Zero defects at point of entry.
 De-conflict data at source, not at higher levels.
 Data aggregations from sources data, not from reports.

68
Data Concepts
 Data Element Definition
Text associated with a unique data element within a data
dictionary that describes the data element, give it a specific
meaning and differentiates it from other data elements.
Definition is precise, concise, non-circular, and
unambiguous. (ISO/IEC 11179 Metadata Registry
specification)
 Data Element Registry
A label kept by a registration authority that describes a
unique meaning and representation of data elements,
including registration identifiers, definitions, names, value

69
Data and Services Deployment Principles
 Data, services and applications belong to the Enterprise.
 Information is a strategic asset.
 Data and applications cannot be coupled to each other.
 Interfaces must be independent of implementation.
 Data must be visible outside of the applications.
 Semantics and syntax is defined by a community of
interest.
 Data must be understandable and trusted.

70
Organization of Security Services
Security
Services
Transfer
Services
Protection
Services
Certification
Services
Systems
Assurance
Authentication
Services

71
Security Services = Information Assurance
 Conduct Attack/Event Response
Ensure timely detection and appropriate response to
attacks.
Manage measures required to minimize the
network’s vulnerability.
 Secure Information Exchanges
Secure information exchanges that occur on the
network with a level of protection that is matched to
the risk of compromise.
 Provide Authorization and Non-Repudiation Services

72
Organization of Computing Services
Computing
Services
Computing
Facilities
Resource
Planning
Control &
Quality
Configuration
Services
Financial
Management

73
Computing Services
 Provide Adaptable Hosting Environments
Global facilities for hosting to the “edge”.
Virtual environments for data centers.
• Distributed Computing Infrastructure
Data storage, and shared spaces for information
sharing.
• Shared Computing Infrastructure Resources

74
Organization of Communication Services
Communication
Services
Interoperability
Services
Spectrum
Management
Connectivity
Arrangements
Continuity of
Services
Resource
Management

75
Network Services Implementation
From point-to-point communications (push
communications) to network-centric
processes (pull communications).
Data posted to shared space for retrieval.
Network controls assure data synchronization
and access security.

76
Communication Services
Provide Information Transport
Transport information, data and services
anywhere.
Ensures transport between end-user devices
and servers.

77
Organization of Application Services
Application
Services
Component
Repository
Code Binding
Services
Maintenance
Management
Portals
Experimental
Services

78
Application Services and Tools
• Provide Common End User Interface Tools
 Application generators, test suites, error identification, application
components and standard utilities.
 Common end-user Interface Tools.
 E-mail, collaboration tools, information dashboards, Intranet portals,
etc.

79
Example of Development Tools
 Business Process Execution Language, BPEL, is an executable
modeling language. Through XML it enables code generation.
Traditional Approach BPEL Approach
- Hard-coded decision logic - Externalized decision logic
- Developed by IT - Modeled by business analysts
- Maintained by IT - Maintained by policy managers
- Managed by IT - Managed by IT
- Dependent upon custom logs - Automatic logs and process
capture
- Hard to modify and reuse - Easy to modify and reuse

80
A Few Key SOA Protocols
 Universal Description, Discovery, and Integration, UDDI.
Defines the publication and discovery of web service
implementations.
 The Web Services Description Language, WSDL, is an XML-
based language that defines Web Services.
 SOAP is the Service Oriented Architecture Protocol. It is a
key SOA in which a network node (the client) sends a request
to another node (the server).
 The Lightweight Directory Access Protocol, or LDAP is
protocol for querying and modifying directory services.
 Extract, Transform, and Load, ETL, is a process of moving

References
1. Kai Hwang, Geoffery C. Fox and Jack J. Dongarra, “Distributed and Cloud
Computing: Clusters, Grids, Clouds and the Future of Internet”, First Edition, Morgan
Kaufman Publisher, an Imprint of Elsevier, 2012.
2. Distributed Computing. http://distributedcomputing.info/index.html
3. Jie Wu, Distributed System Design, CRC Press, 1999.
4. Distributed Computing, Wikipedia http://en.wikipedia.org/wiki/Distributed_computing
5. www.psgtech.edu/yrgcc/attach/GridComputing-an%20introduction.ppt
6. www.cse.unr.edu/~mgunes/cpe401/cpe401sp12/lect15_cloud.ppt
7. csnotes.upm.edu.my/kelasmaya/web.nsf/.../$FILE/Distributed%20Computing.ppt
8. www.strassmann.com/pubs/gmu/2007-11-slides.ppt

Other presentations
http://www.slideshare.net/drgst/presentations

83
Thank You
Questions and Comments?
Dr Gnanasekaran Thangavel 8/30/2016

Cs6703 grid and cloud computing unit 1

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