Distributed computing

DISTRIBUTED COMPUTING
Presented by:
Alokeparna Choudhury (ME201310005)

CONTENTS
 CENTRALIZED VS. DISTRIBUTED COMPUTING
 INTRODUCTION
 ORGANIZATION
 ARCHITECTURE
 MOTIVATION
 HISTORY
 GOAL
 CHARACTERISTICS
 EXAMPLES OF DISTRIBUTED COMPUTING
 DISTRIBUTED COMPUTING USING MOBILE AGENT
 TYPICAL DISTRIBUTED COMPUTING
 A TYPICAL INTRANET

CONTD..
 INTERNET
 JAVA RMI
 TRANSPARENCY IN DISTRIBUTED SYSTEM
 CATEGORIES OF APPLICATIONS IN DISTRIBUTED
COMPUTING
 MONOLITHIC MAINFRAME APPLICATION vs DISTRIBUTED
APPLICATION
 ADVANTAGES
 DISADVANTAGES
 ISSUES & CHALLENGES
 CONCLUSION
 REFERENCES

CENTRALIZED VS. DISTRIBUTED
COMPUTING
m a in f r a m e c o m p u te r
w o r k s ta tio n
n e tw o r k h o s t
n e tw o r k lin k
te r m in a l
c e n t r a liz e d c o m p u t in g
d is t r ib u t e d c o m p u t in g

CENTRALIZED VS. DISTRIBUTED
COMPUTING
Early computing was
performed on a single
processor. Uni processor
computing can be called
Centralized computing.
A Distributed system is a
collection of independent
computers,
interconnected via a
network, capable of
collaborating on a task.
Centralized computing Distributed computing

INTRODUCTION
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 computation problem or information processing.”

• 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
computation problem or information processing.
• 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 same room, same
campus, same country, or in different continents.

Cooperation
Cooperation
Cooperation
InternetInternet
Large-scale
ApplicationResource
Management
Subscription
Distribution
Distribution Distribution
Distribution
Agent
Agent Agent
Agent
Job Request

ORGANIZATION
 Organizing the interaction between each computer is of prime
importance. In order to be able to use the widest possible range and
types of computers, the protocol or communication channel should
not contain or use any information that may not be understood by
certain machines. Special care must also be taken that messages are
indeed delivered correctly and that invalid messages are rejected
which would otherwise bring down the system and perhaps the rest
of the network.
 Another important factor is the ability to send software to another
computer in a portable way so that it may execute and interact with
the existing network. This may not always be possible or practical
when using differing hardware and resources, in which case other
methods must be used such as cross-compiling or manually porting
this software.

ARCHITECTURE
 Distributed programming typically falls into one of several
basic architectures or categories: Client-server, 3-tier
architecture, N-tier architecture, Distributed objects, loose
coupling, or tight coupling.
 Client-server — Smart client code contacts the server for data,
then formats and displays it to the user. Input at the client is
committed back to the server when it represents a permanent
change.
 3-tier architecture — Three tier systems move the client
intelligence to a middle tier so that stateless clients can be
used. This simplifies application deployment.
 N-tier architecture — N-Tier refers typically to web
applications which further forward their requests to other
enterprise services. This type of application is the one most
responsible for the success of application servers.

 Tightly coupled (clustered) — refers typically to a set of
highly integrated machines that run the same process in
parallel, subdividing the task in parts that are made
individually by each one, and then put back together to make
the final result.
 Peer-to-peer —an architecture where there is no special
machine or machines that provide a service or manage the
network resources. Instead all responsibilities are uniformly
divided among all machines, known as peers. Peers can serve
both as clients and servers.
 Space based — refers to an infrastructure that creates the
illusion (virtualization) of one single address-space. Data are
transparently replicated according to application needs.
Decoupling in time, space and reference is achieved.

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 - 1995
 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.
 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.

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 problem

CHARACTERISTICS
 Resource Sharing
 Openness
 Concurrency
 Scalability
 Fault Tolerance
 Transparency

EXAMPLES OF DISTRIBUTED
COMPUTING
 Network of workstations (NOW) / PCs: a group of
networked personal workstations or PCs connected to
one or more server machines.
 Distributed computing using mobile agents
 The Internet(World Wide Web)
 An intranet: a network of computers and workstations
within an organization, segregated from the Internet via a
protective device (a firewall).
 JAVA Remote Method Invocation (RMI)

DISTRIBUTED COMPUTING USING
MOBILE AGENTS
 Mobile agents can be wandering around in a network
using free resources for their own computations.

TYPICAL DISTRIBUTED
COMPUTING
intranet
ISP
desktop computer:
backbone
satellite link
server:
network link:

A TYPICAL INTRANET
the rest of
email server
Web server
Desktop
computers
File server
router/firewall
print and other servers
other servers
print
Local area
network
email server
the Internet

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

TRANSPARENCY IN DISTRIBUTED
SYSTEMS
 Access transparency: enables local and remote resources to be accessed
using identical operations.
 Location transparency: enables resources to be accessed without
knowledge of their physical or network location (for example, which
building or IP address).
 Concurrency transparency: enables several processes to operate
concurrently using shared resources without interference between them.
 Replication transparency: enables multiple instances of resources to be
used to increase reliability and performance without knowledge of the
replicas by users or application programmers.
 Failure transparency: enables the concealment of faults, allowing users
and application programs to complete their tasks despite the failure of
hardware or software components.
 Mobility transparency: allows the movement of resources and clients
within a system without affecting the operation of users or programs.
 Performance transparency: allows the system to be reconfigured to
improve performance as loads vary.
 Scaling transparency: allows the system and applications to expand in
scale without change to the system structure or the application algorithms.

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

MONOLITHIC MAINFRAME APPLICATION
VS DISTRIBUTED APPLICATION
 The monolithic mainframe application
architecture:
 Separate, single-function applications, such as order-
entry or billing
 Applications cannot share data or other resources
 Developers must create multiple instances of the same
functionality (service).
 The distributed application architecture:
 Integrated applications
 Applications can share resources
 A single instance of functionality (service) can be
reused.

ADVANTAGES OF DISTRIBUTED
COMPUTING
 Cost : Better price / performance as long as everyday
hardware is used for the component computers – Better
use of existing hardware
 Performance : By using the combined processing and
storage capacity of many nodes, performance levels can
be reached that are out of the scope of centralised
machines
 Scalability : Resources such as processing and storage
capacity can be increased incrementally
 Inherent distribution : Some applications like the Web
are naturally distributed
 Reliability : By having redundant components the impact
of hardware and software faults on users can be reduced

DISADVANTAGES OF DISTRIBUTED
COMPUTING
The disadvantages of distributed computing:
 Multiple Points of Failures: the failure of one or
more participating computers, or one or more
network links, can generate trouble.
 Security Concerns: In a distributed system, there
are more opportunities for unauthorized attack.
 Software: Distributed software is harder to
develop than conventional software; hence, it is
more expensive

ISSUES & CHALLANGES
 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 of
distributed system; design by different people.

 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.

 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.

 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.
 Each resource must be designed to be safe in a concurrent
environment.

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.

REFERENCES
 Andrew S. Tanenbaum and Maarten Van Steen,
Distributed Systems : Principles and Paradigms,
Pearson Prentice Hall, 2nd
Edition 2007.
 www.inderscience.com/ijcnds
 George Coulouris, Jean Dollimore, and Tim Kindberg,
Distributed Systems: Concepts and Design, Addison-
Wesley,Pearson Education 3rd
Edition 2001.

Distributed computing

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