Distributed Computing Report

INTRODUCTION:-

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.
A distributed system consists of collection of autonomous computers, connected
through a network and distributed operating system software, which enables computers to
coordinate their activities and to share the resources of the system - hardware, software
and data, so that users perceive the system as a single, integrated computing facility.

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(Figure 1-Distributed Computing)

These networked computers may be in the same room, same campus, same
country, or in different continents. A distributed system may have a common goal, such
as solving a large computational problem. Alternatively, each computer may have its own

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user with individual needs, and the purpose of the distributed system is to coordinate the
use of shared resources or provide communication services to the users.

Rise of Distributed Computing:-
 Computer hardware prices are falling and power increasing.
 Network connectivity is increasing.
 Everyone is connected with fat pipes.
 It is easy to connect hardware together.
 Combination of cheap processors often more
 Cost-effective than one expensive fast system.
 Flexibility to add according to needs.
 Potential increase of reliability.
 Sharing of resources.

Characteristics of Distributed Computing:-
Six key characteristics are primarily responsible for the usefulness of distributed
system. They are resource sharing, openness, concurrency, scalability, fault tolerance and
transparency. It should be emphasized that they are not automatic consequences of
distribution; system must be carefully designed in order to ensure that they are achieved.

 Resource Sharing:-
Resource sharing is the ability to use any hardware, software or data anywhere in
the system. Resources in a distributed system, unlike the centralized one, are physically
encapsulated within one of the computers and can only be accessed from others by
communication. It is the resource manager to offers a communication interface enabling
the resource be accessed, manipulated and updated reliability and consistently. There are
mainly two kinds of model resource managers: client/server model and the object-based
model. Object Management Group uses the latter one in CORBA, in which any resource

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is treated as an object that encapsulates the resource by means of operations that users
can invoke.

 Openness:-
Openness is concerned with extensions and improvements of distributed systems.
New components have to be integrated with existing components so that the added
functionality becomes accessible from the distributed system as a whole. Hence, the static
and dynamic properties of services provided by components have to be published in
detailed interfaces.

 Concurrency:-
Concurrency arises naturally in distributed systems from the separate activities
of users, the independence of resources and the location of server processes in separate
computers. Components in distributed systems are executed in concurrent processes.
These processes may access the same resource concurrently. Thus the server process
must coordinate their actions to ensure system integrity and data integrity.

 Scalability:-
Scalability concerns the ease of the increasing the scale of the system (e.g. the
number of processor) so as to accommodate more users and/or to improve the
corresponding responsiveness of the system. Ideally, components should not need to be
changed when the scale of a system increases.

 Fault tolerance:-
Fault tolerance cares the reliability of the system so that in case of failure of
hardware, software or network, the system continues to operate properly, without
significantly degrading the performance of the system. It may be achieved by recovery
(software) and redundancy (both software and hardware).

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 Transparency:-
Transparency hides the complexity of the distributed systems to the users and
application programmers. They can perceive it as a whole rather than a collection of
cooperating components in order to reduce the difficulties in design and in operation.
This characteristic is orthogonal to the others. There are many aspects of transparency,
including access transparency, location transparency, concurrency transparency,
replication transparency, failure transparency, migration transparency, performance
transparency and scaling transparency.

Distributed Computing Architecture:-
Various hardware and software architectures are used for distributed computing. At
a lower level, it is necessary to interconnect multiple CPUs with some sort of network,
regardless of whether that network is printed onto a circuit board or made up of loosely-
coupled devices and cables. At a higher level, it is necessary to
interconnect processes running on those CPUs with some sort of communication system.

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.

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 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. Most web
applications are 3-Tier.

 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):-
Tightly coupled architecture refers typically to a cluster of machines that
closely work together, running a shared process in parallel. The task is subdivided
in parts that are made individually by each one and then put back together to
make the final result.

 Peer-to-peer:-
Peer-to-peer is 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 :-
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.

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Another basic aspect of distributed computing architecture is the method of
communicating and coordinating work among concurrent processes. Through various
message passing protocols, processes may communicate directly with one another,
typically in a master/slave relationship. Alternatively, a "database-centric"
architecture can enable distributed computing to be done without any form of direct inter-
process communication, by utilizing a shared database.

Distributed Computing Paradigms:-

 The Message Passing Paradigm:-

Message passing is the most fundamental paradigm for distributed applications.
A process sends a message representing a request. The message is delivered to a receiver,
which processes the request, and sends a message in response. In turn, the reply may
trigger a further request, which leads to a subsequent reply, and so forth.

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 The Client-Server Paradigm:-

Perhaps the best known paradigm for network applications, the client-server
model assigns asymmetric roles to two collaborating processes. One process, the server,
plays the role of a service provider which waits passively for the arrival of requests. The
other, the client, issues specific requests to the server and awaits its response. Simple in
concept, the client-server model provides an efficient abstraction for the delivery of
network services. Operations required include those for a server process to listen and to
accept requests, and for a client process to issue requests and accept responses. By
assigning asymmetric roles to the two sides, event synchronization is simplified: the
server process waits for requests, and the client in turn waits for responses. Many Internet
services are client-server applications. These services are often known by the protocol
that the application implements. Well known Internet services include HTTP, FTP, DNS,
etc.

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 The Peer-to-Peer Distributed Computing Paradigm:-

In the peer-to-peer paradigm, the participating processes play equal roles, with
equivalent capabilities and responsibilities (hence the term “peer”). Each participant may
issue a request to another participant and receive a response. The peer-to-peer paradigm
is more appropriate for applications such as instant messaging, peer-to-peer file transfers,
video conferencing, and collaborative work. It is also possible for an application to be
based on both the client-server model and the peer-to-peer model. A well-known example
of a peer-to-peer file transfer service is Napster.com or similar sites which allow files
(primarily audio files) to be transmitted among computers on the Internet. It makes use of
a server for directory in addition to the peer-to-peer computing.

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Application:-
There are many examples of commercial application of distributed system, such as
the Database Management System, distributed computing using mobile agents, local
intranet, internet (World Wide Web), JAVA RMI, etc.

 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.

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

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 JAVA RMI:-
 Communicating Entities:-
 Implementing some application for user
 Using support of distributed services
 Layers of support
 Client/server
 Embedded in language Java:-
 Object variant of remote procedure call
 Adds naming compared with RPC
 Restricted to Java environments

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 RMI Features:-
 Distributed object model:-
 Objects: normal and remote
 Idea:-
 Remote object exists on other host
 Remote object can be used as normal object
 Behavior described by interface
 Environment takes care of remote invocation
 Differences normal and remote objects:-
 Remote references can be distributed freely
 Clients only know/use interface, not actual implementation
 Passing remote objects by reference, normal objects by copying
 Failure handling more complicated since invocation itself can also fail

 RMI Architecture:-

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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.

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 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). This can be done without
disruption to the rest of the system.

 Distributed custodianship:-
The National Spatial Data Infrastructure (NSDI) calls for a system of
partnerships to produce a future national framework for data as a patchwork quilt
of information collected at different scales and produced and maintained by
different governments and agencies. NSDI will require novel arrangements for
framework management, area integration, and data distribution. This research will
examine the basic feasibility and likely effects of such distributed custodianship
in the context of distributed computing architectures, and will determine the
institutional structures that must evolve to support such custodianship.

 Data integration:-
This research will contribute to the integration of geographic information and
GISs into the mainstream of future libraries, which are likely to have full digital
capacity. The digital libraries of the future will offer services for manipulating
and processing data as well as for simple searches and retrieval.

 Missed opportunities:-

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By anticipating the impact that a rapidly advancing technology will have on
GISs, this research will allow the GIS community to take better advantage of the
opportunities that the technology offers.

Disadvantages:-
 Lack of experience in designing, and implementing a distributed system. E.g.
which platform (hardware and OS) to use, which language to use etc. But this is
changing now.
 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 is a major hazard since easy access to data means easy access to secret
data as well.

Conclusions:-
In this age of optimization everybody is trying to get optimized output from their
limited resources. The concept of distributed computing is the most efficient way to
achieve the optimization. In case of distributed computing the actual task is modularized
and is distributed among various computer system. It not only increases the efficiency of
the task but also reduce the total time required to complete the task. Now the advance
concept of this distributed computing, that is the distributed computing through mobile
agents is setting a new landmark in this technology. A mobile agent is a process that can
transport its state from one environment to another, with its data intact, and be capable of
performing appropriately in the new environment.

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References:-

 Andrews, Gregory R. (2000), Foundations of Multithreaded, Parallel, and
Distributed Programming, Addison–Wesley, ISBN 0-201-35752-6.
 Arora, Sanjeev; Barak, Boaz (2009), Computational Complexity – A Modern
Approach, Cambridge, ISBN 978-0-521-42426-4.
 Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald
L. (1990), Introduction to Algorithms (1st ed.), MIT Press, ISBN 0-262-03141-8.
 Dolev, Shlomi (2000), Self-Stabilization, MIT Press, ISBN 0-262-04178-2.

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