IOSR Journal of Computer Engineering (IOSR-JCE)

ISSN : 2278-0661, ISBN : 2278-8727, PP : 53-56

www.iosrjournals.org

Real Time And Distributed Computing Systems

Ms.Kamble A.L1,Ms.Shinde.T.K2, Ms Kothiwale N3, Khot.S.S4
1
(Computer Science , Shree Datta Polytechnic, India)
2
(Computer Science, Dr.J.J.Magdum College of Engineering, India)
3
(Computer Science, Dr J.J.Magdum Polytechnic, India)
4
(Computer Science, Dr.J.J.Magdum College of Engineering, India)

ABSTRACT : General-Purpose Operating Systems (GPOSes) are being used more and more extensively to
support interactive, real-time, and distributed applications, as found in the multimedia domain. In fact, the wide
availability of supported multimedia devices and protocols, together with the wide availability of libraries and
tools for handling multimedia contents, make them an almost ideal platform for the development of this kind of
complex applications. However, contrarily to Real-Time Operating Systems, General-Purpose ones used to lack
some important functionality needed for providing proper scheduling guarantees to application processes.
Recently, the increasing use of GPOSes for multimedia applications is gradually pushing OS developers towards
enriching the kernel of a GPOS so as to provide more and more real-time functionality, thus enhancing the
performance and responsiveness of hosted time-sensitive applications. In this chapter, an overview is performed
over the efforts done in the direction of enriching GPOSes with real-time capabilities, with a particular focus on
the Linux OS. Due to its open-source nature and wide diffusion and availability, Linux is one of the most widely
used for such experimentations

Keywords – real time operating systems, distributed computational systems

I.INTRODUCTION
The word distributed in terms such as “distributed systems”, ”distributed programming” and ”Distributed
algorithm” originally referred to as computer networks where individual computers were physically distributed
within some geographical area.
A distributed system consists of multiple autonomous computers or nodes that communicate which have its
own local memory. Information is exchanged by passing messages between the processors through a computer
network. The computer interacts with each other in order to achieve a common goal. In this, a problem is
divided into many tasks, each of which is solved by one computer.
The main goal of a distributed computing system is to connect users and resources in a transparent, open and
scalable way. Ideally this arrangement is drastically more fault tolerant and more powerful than many
combinations of standalone computer systems. It is very difficult to define distributed system, but each of these
systems, do have certain properties which are a part of the systems. Although there are a number of autonomous
computational entities, each of these has their own local memory.
The different entities who are a part of the system communicate between each other by passing
messages.These are, among others, types of applications that have been undergoing in the last few years a very
steep demand on the side of end-to-end interactivity level, response time, and throughput. This is also due to the
growing availability of affordable broadband Internet connections.

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Real Time And Distributed Computing Systems
There can be different kinds of computers and network links which can be a part of the system. At the
same time also there are chances of the system changing completely or partially during the execution of a
distributed program..

II. Principle
This principle of distributed computing underlying the design of distributed systems:
1] Communication: Communication does not come for free; often communication cost dominates the cost of
local processing or storage.
2] Coordination: How can you coordinate a distributed system that it performs some task efficiently?
3] Fault-tolerance: Major advantage of a distributed system is that even in the presence of failures the system as
a whole may survive.
4] Locality: Networks keep growing. Luckily global information is not always needed to solve a task; often it is
sufficient if nodes talk to their neighbors.
5] Parallelism: How fast can we solve a task if we throw more hardware at the problem? How much parallelism
is possible for a given problem?
6] Symmetry Breaking: Sometimes some nodes need to be selected to orchestrate the others. This is done by a
technique called symmetry breaking.
7] Synchronization: How can you implement a synchronous algorithm in an asynchronous system?
8] Uncertainty: If we need to agree on a single term describing the course, it probably is “uncertainty”. As the
whole system is distributed no node knows what other nodes are doing at this exact moment.

III. Goals and Advantages

There are many different types of distributed computing systems and many challenges to overcome in
successfully designing one. The main goal of a distributed computing system is to connect users and IT
resources in a transparent, open, cost-effective, reliable and scalable way.
Ideally this arrangement is drastically more fault tolerant and more powerful than many combinations
of standalone computer systems.
There are following advantages of distributed systems.

1] Openness:
Openness is the property of distributed systems such that each subsystem is continually open to
interaction with other systems. There are some protocols and standards which enable distributed systems to be
extended and scaled open distributed are inspired to meet the following challenges:
a] Monotonicity: Once something is published in an open system, it cannot be taken back.
b] Pluralism: Different subsystems of an open distributed system include heterogeneous, overlapping and
possibly conflicting information. There is no central arbiter of truth in open distributed systems.
2] Scalability: A scalable system is one that can easily be altered to accommodate changes in the number of
users, resources and computing entities affected to it. Scalability can be measured in three different dimensions:
a] Load Scalability: A distributed system should make it easy for us to expand and contract its resource pool to
accommodate heavier or lighter loads.
b] Administrative scalability: No matter how many different organizations need to share a single distributed
system, it should still be easy to use and manage.

IV. Drawbacks / Disadvantages

If not planned properly, a distributed system can decrease the overall reliability of computations, if the
unavailability of a node can cause a disruption of the other nodes because the analysis may now require
connecting to remote nodes or inspecting communications being sent between nodes.
There are following disadvantages of distributed systems.
1] Complexity- Extra work must be done by the DBAs to ensure that the distributed nature of the system is
transparent. Extra database design work must also be done to account for the disconnected nature of the
database.
2] Economics-Increased complexity and a more extensive infrastructure mean extra labor costs.
3] Security- Remote database fragments must be secured and they are not centralized, so the remote sites must
be secured as well.

V. Difficult to maintain integrity-

In a distributed database, enforcing integrity over a network may require too much of the network’s resources to
be feasible

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Real Time And Distributed Computing Systems
VI. Inexperience- Distributed databases are difficult to work with and as a young field there is not much
readily available experience on proper practice.

VII. Lack Of standards – There are no tools or methodologies yet to help users convert a centralized DBMS
into a distributed DBMS.

VIII. Types of Distributed Systems

1] Clusters: A computer cluster is a group of linked computers, working together closely, forming a single
computer. The components of a cluster are commonly connected to each other through fast local area networks.
Clusters are usually deployed to improve performance and availability over that of a single computer.
2] Grids: Clusters can be combined to form a grid, a system of massive collective computing power which is
designed to be easily used by “plugging in” to it.
3] Peer-to-Peer: A system where by individual users or nodes can communicate with each other by themselves.

IX. Applications
1) Telecommunication networks
Distributed system can be used in various Telephone networks, cellular networks, computer networks such as
the Internet, wireless sensor networks and Routing algorithms.
2) Network applications
Distributed systems has a wide scope in World wide web and peer-to-peer networks, Massively multiplayer
online games and Distributed information processing systems such as banking systems and airline reservation
systems.
3) Real-time process control
Distributed systems are used in Aircraft control systems and Industrial control systems.
4) Parallel computation These distributed systems can also be used in Scientific computing, including cluster
computing and grid computing and various volunteer computing projects and distributed rendering in computer
graphics.

X. Architectural Models Related To Distributed Computing

Various hardware and software architectural models 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,
 Tier architecture-tier architecture,
 Tightly coupled architecture,
 Peer-to-peer architecture,

1] Client-Server model
The client server model of computing is distributed application structure that partitions tasks of workloads
between the providers of a resource or service called servers and service requesters called clients. Often clients
and servers communicate over a computer network on a separate hardware but both client and server may reside
in the same system.

A server machine is a host that is running one or more client does not share any of its resources, but
requests a server’s contents or service function. Clients therefore initiate communication sessions with servers
which a wait incoming requests.

2] 3-tier Architectural Model

Three – tier architecture is an extension to the client-server architecture and is defined by the following
three component layers:
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Real Time And Distributed Computing Systems
1) Presentation Tier
Also known as the client or front end, deals with the interaction with the user. Usually, there can be any
number of clients which can all access the server at the same time. Clients process user input, send requests to
the server, and show the results of these requests to the user. Client implements the presentation logic.
2) Application Tier
Also known as the server or the back-end or middleware, it processes the requests of all clients. It is the actual
web application that performs all functionality specific to the web application. The business logic is
implemented on an application server(s).Whenever it needs data; it contacts the database server(s).

3] N-tier architecture model

N-tier architecture is really 3 tier architectures in which the middle tier or application tier is split up into new
tiers. A tier is one of two or more rows, levels or ranks arranged one above another. N-tier refers typically to
web applications which further forward their requests to other enterprise services. This type of application is one
of the most responsible for the success of application servers’. N-tier applications have the advantage that any
one tier can run on an appropriate processor or operating system platform and can be updated independently of
the other tiers.

4] Tightly coupled model :

Tightly coupled Distributed computing model actually means two entities are closely associated. It typically refers to a
cluster of highly integrated 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.
The components of a cluster are commonly but not always connected to each other through fast
networks. All clusters share common main memory and are usually deployed to improve performance and
availability over that of a single computer while typically being much more cost effective than single computers
of comparable speed or availability.
5] Peer-to-peer model:
Peer means a host computer. When many hosts are connected through to share files, computing capabilities,
network bandwidth and storage, it calls peer to peer networking system. In peer-to-peer computing, each
computer can serve the function of both client and server. Any computer in the network can initiate questions
(like a client), receive questions and transmit data (like server).
In some cases, peer-to peer computing is implemented by actually giving each computer on the
network both server and client capabilities. Peer-to-peer encompasses two distinct types of technology:1) The
sharing of different computer processing and storage capabilities.
2) The sharing of digital files and data between two computers.

XI. Conclusion
Distributed computing encompasses many of the entities occurring in today’s' world. No one entity
could ever control the Internet.
It’s just too distributed, in every sense.
The Internet will continue to spread it's already global reach for our foreseeable future, routinely
touching every aspect of our daily lives.
As we continue our exploration of a truly unknowable future, we must try to keep in mind that in all the
important ways. We must help to define the future of computing. While there are both physical and non-physical
elements that make up this thing, we call the Internet.
The Internet and the World Wide Web ignore borders and instead allow people the freedom to view
the World as a continuum and to see them as a part of the whole picture.
We are all part of a rapidly evolving complex system that is constantly changing and adapting, both as
a whole and as individual points in a dynamic matrix of objects, processes and events. With each point in the
matrix playing an integral part, we will continue to collectively define, create and explore the fantastic future of
computing.

References
[1] A Note on Distributed Computing
[2] Algorithms and Distributed
[3] Computing Distributed Computing: Introduction
[4] Distributed Computing Utilities, Grids & Clouds
[5] www.google.com
[6] Distributed Object Systems
[7] The Clouds Project: Designing and
[8] Implementing a Fault Tolerant
[9] Distributed Operating System.
[10] Proceedings: Distributed Computing (Fall 1980).
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