This document provides information about a Distributed Computing II course. It includes the course code, instructor details, course objectives, outline, recommended books, learning outcomes, grading policy, and sample course content. The course aims to introduce advanced distributed computing concepts like remote method invocation, JavaBeans, XML and GUI features. It will examine the role of Java in distributed systems and web services. The grading will be based on quizzes, assignments, a midterm exam and final exam. Sample content discusses different types of computing and provides an introduction to key distributed computing concepts.

1. MCS-121
Distributed Computing II
Engr. Anila Saghir
asaghir@ssuet.edu.pk
Mobile Communication and Security
Telecommunication Engineering Department
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2. Week 01
Lecture 01
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3. Course Information Sheet
Discussion
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4. Course Information
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Session: Spring-2023
Course Title: Distributed Computing II
Course Code: MCS 308T
Credit Hours: 3
Semester: 6th
Pre-Requisites: MCS 307T Distributed Computing I
Instructor Name: Engr. Anila Saghir
Email and Contact Information: asaghir@ssuet.edu.pk
WhatsApp Group MCS-308T DC-II
Office Hours:
9:00am-5:00pm
Counselling Hours: Tuesday, 8:30am-11:30am
Mode of Teaching: Synchronous/Asynchronous/ Hybrid/Blended

5. COURSE OBJECTIVE:
• The main objective of the course is to introduce the
student with advance aspects of Distributed
Computing including Remote Method Invocation,
JavaBeans, XML and advanced GUI features study.
• The course mainly focuses on to examine the role of Java
in Distributed Systems and Web based Services
including Security issues.
• On completion of this course the student will have an
appreciation of the issues pertaining to the use of Java in
a large Distributed Enterprise Environment.
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6. COURSE OUTLINE:
Inter-process Communication,
Java Overview,
Multithreading in Java,
Distributed Computing Paradigms,
The Socket API,
The Client-Server Paradigm,
Group Communication,
Web Services: HTTP and Java, Java Servlets,
Distributed Objects,
Advanced RMI,
Internet Applications,
The Common Object Request Broker Architecture (CORBA), Java IDL and CORBA.
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7. Recommended Books:
• RECOMMENDED BOOK:
• M.L. Liu, “Distributed Computing: Principles and Applications”,
4th Edition, 2019, Pearson, ISBN: 978-0201796445
• REFERENCE BOOKS:
• David Reilly, “Java Network Programming and Distributed
Computing”, 2002, 1st Edition, Addison-Wesley, ISBN : 978-
020171037
• Raja Malleswara Rao Pattamsetti, “Distributed Computing in
Java 9”, 2017, 1st Edition, Packt Publishing , ISBN:
9781787126992
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8. Course Learning Outcomes
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CLO
No.
Course Learning Outcomes (CLOs) PLOs
Bloom’s
Taxonomy
1
Apply theoretical programming models and
modern programming tools for implementing
distributed-computing applications.
PLO_2
(Problem Analysis)
C3
(Applying)
2
Analyze the different distributed-system
middleware such as distributed objects, web
services, and peer-to-peer systems for their
performance.
PLO_2
(Problem Analysis)
C4
(Analyzing)
3
Design and develop a multi-tier server-side
application.
PLO_3
(Design/Developm
ent of Solutions)
C6
(Creating)

9. Grading Policy
Assessment Tools Percentage
Quizzes 10%
Assignments 10%
Midterm Exam 30%
Final Exam 50%
TOTAL 100%
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10. Assessment Tools and Their Weightage
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Assessment Tools CLO-1 (30) CLO-2 (40) CLO-3 (30)
Quizzes 12%(3) 9.1%(5) 10%(2)
Assignments
8%(2) 9.1%(5) 15%(3)
Midterm Exam 40%(10) 36.4%(20) -
Final Exam 40%(10) 45.4%(25) 75%(15)

11. COMPLEX COMPUTING PROBLEM:
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Included: Yes
Nature and details of Complex Computing Problem (CCP): It will be given in Assignment # 02.
CCP will be based on CLO-2 “Analyze the different distributed-system middleware such as distributed objects,
web services, and peer-to-peer systems for their performance”.
To investigate the problem, students use in-depth knowledge related to the following concepts: Java Servelets,
web services, XML, Java RMI
Attributes could be: WP1, WP3, WK5, WA3
WP1: Depth of knowledge required
WP3: Depth of analysis required
WK5: Engineering Design
WA3: Design/Development of Solution
Assessment in: Assignment # 02

12. CLO-01
Apply theoretical programming models and modern
programming tools for implementing distributed-computing
applications.
[C3 (Applying), PLO-2(Problem Analysis)]
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13. Distributed Computing, An Introduction
In this chapter, we will begin by clarifying what is meant by distributed computing. We will
do so by looking at the history of distributed computing and by comparing this type of
computing with other forms of computing. We will then understand some basic concepts in
the disciplines of operating systems and networks.
Chapter #1
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14. Introduction
• What is Computing?
Computing is any goal-oriented activity requiring,
benefiting from, or creating computing machinery.
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15. Introduction
Forms of Computing
• To understand the distributed computing, lets
understand the different types of computing's
performed by a computer.
• We can categorized computer computing roughly as :
• Monolithic Computing
• Distributed Computing
• Parallel Computing
• Cloud Computing
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16. Monolithic Computing
Early computing was performed on a
single processor.
A uniprocessor, or monolithic
computing, makes use of a single
central processing unit (CPU) to
execute one or more programs for each
application
The computer Is not connected to any
network, and thus it may use only those
resources within its immediate access.
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17. Distributed Computing
Distributed computing involves
computing performed among
multiple network-connected
computers, each of which has Its
own processors and other
resources
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18. Parallel Computing
• Parallel computing is a type of computing in which
one computer or multiple computers in a network
carry out many calculations or processes
simultaneously.
Note: Parallel computing is a particularly tightly coupled form of
distributed computing.
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19. Parallel Computing Vs Distributed
Computing
• In parallel processing, all processors have access to
shared memory for exchanging information
between them.
• On the other hand, in distributed processing, each
processor has private memory (distributed
memory). Processors use message passing to
exchange information.
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20. Parallel Computing Vs Distributed
Computing
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21. Cloud Computing
• Cloud computing is internet based computing
where virtual shared servers provide a platform,
infrastructure for application, file storage and other
resources and hosting to customers on a pay-as-
you-go basis.
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22. Cloud Computing
• Customers do not own the physical infrastructure
and no need to depend on the storage capacity of
their local computer network rather they rent the
usage from a third-party provider.
• Cloud computing significantly reduced the cost of
computation, content storage, and application
hosting.
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23. Distributed Computing Vs Cloud
Computing
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24. Distributed System
A distributed system is a
collection of independent computers,
interconnected via a network, that are capable of
collaborating on a task
Note: Computers are considered Independent if they do not share memory or
program execution space.
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25. Distributed System Vs Distributed Computing
Distributed System is the networked collection of
independent machines that can collaborate
remotely to achieve one goal
whereas,
Distributed computing is the cloud-based
technology that enables this distributed system to
operate, collaborate, and communicate.
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26. Distributed Computing
Distributed Computing is the computing
performed such as
Network services and Web Applications
in a distributed System.
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27. Distributed Computing)(Cont..)
• A network service is a service provided by a
special kind of program known as a server on a
network
• The World Wide Web, electronic email and file
transfer are some examples of such services
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28. Distributed Computing)(Cont..)
• A network application is an application that runs
on networked computers for end users.
• Network applications range from enterprise
applications such as online shopping carts and e-
marketing sites to noncommercial applications
such as chatrooms and network games etc.
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29. Distributed Computing)(Cont..)
• The distinction between network services and
network applications is not always clear-cut, and
the terms are often used Interchangeably.
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30. History of Distributed Computing
• The distributed computing system is all about
evolution from centralization to decentralization,
• We had a centralized system like mainframe in
early 1955
• But now we are probably using a decentralized
system like edge computing and containers.
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31. How does distributed computing work
• Distributed computing works by computers passing
messages to each other within the distributed
systems architecture.
• Communication protocols or rules create a
dependency between the components of the
distributed system.
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32. How does distributed computing work
• This interdependence is called coupling, and there
are two main types of coupling.
• Loose Coupling
• Tight Coupling
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33. How does distributed computing work
Loose coupling
In loose coupling, components are weakly
connected so that changes to one component do not
affect the other.
• For example, client and server computers can be loosely coupled
by time. Messages from the client are added to a server queue, and
the client can continue to perform other functions until the server
responds to its message.
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34. How does distributed computing work
Tight coupling
A tightly coupled system is a system architecture and
computing method in which all hardware and
software components are linked together so that
every component is dependent on the others.
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35. How does distributed computing work
• Tightly coupled system architecture encourages
application and code interdependence.
• High-performing distributed systems often use
tight coupling.
• Fast local area networks typically connect several
computers, which creates a cluster.
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36. Distributed Computing Architectures
• Various hardware and software architectures are
used for distributed computing.
• Distributed programming typically falls into one of
several basic architectures:
• client–server
• three-tier
• n-tier
• peer-to-peer
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37. Client–Server Architecture
• Client-server is the most common method of
software organization on a distributed system.
• The functions are separated into two categories:
clients and servers.
• Clients
• Servers
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38. Client–Server Architecture:
Clients
• Clients
• Clients have limited information and processing
ability. Instead, they make requests to the servers,
which manage most of the data and other
resources.
• You can make requests to the client, and it
communicates with the server on your behalf.
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39. Client–Server Architecture:
Servers
• Servers
• Server computers synchronize and manage access to
resources.
• They respond to client requests with data or status
information.
• Typically, one server can handle requests from several
machines.
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40. Client-Server Architecture:
Benefits and limitations
• Client-server architecture gives the benefits of
security and ease of ongoing management.
• focus is only on securing the server computers.
• Similarly, any changes to the database systems
require changes to the server only.
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41. Benefits and limitations
Client-Server Architecture
• The limitation of client-server architecture is that
servers can cause communication bottlenecks,
especially when several machines make requests
simultaneously.
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42. Three-tier architecture
• In three-tier distributed systems, client machines
remain as the first tier you access.
• Server machines, on the other hand, are further
divided into two categories:
• Application Servers
• Database Servers
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43. Three-tier architecture
• In three-tier distributed systems, client machines
remain as the first tier you access.
• Server machines, on the other hand, are further
divided into two categories:
• Application Servers
• Database Servers
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44. Three-tier architecture
• Application servers
• Application servers act as the middle tier for
communication.
• They contain the application logic or the core
functions that you design the distributed system for.
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45. Three-tier architecture
• Database servers
• Database servers act as the third tier to store and
manage the data.
• They are responsible for data retrieval and data
consistency.
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46. Three-tier architecture
• By dividing server responsibility, three-tier
distributed systems reduce communication
bottlenecks and improve distributed computing
performance.
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47. N-tier architecture
• N-tier models include several different client-server
systems communicating with each other to solve
the same problem.
• Most modern distributed systems use an n-tier
architecture with different enterprise applications
working together as one system behind the scenes.
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48. Peer-to-peer architecture
• Peer-to-peer distributed systems assign equal
responsibilities to all networked computers.
• There is no separation between client and server
computers, and any computer can perform all
responsibilities.
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49. Peer-to-peer architecture
• Peer-to-peer architecture has become popular for
content sharing, file streaming, and block-chain
networks.
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50. The Strengths and Weaknesses of
Distributed Computing
• Machines, able to work remotely on the same task,
improve the performance efficiency of distributed
systems.
• The fault-tolerance, agility, cost convenience, and
resource sharing make distributed computing a
powerful technology.
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51. The Strengths and Weaknesses of
Distributed Computing
• There are a number of reasons for the popularity of
distributed computing:
• The affordability of computers and availability of
network access.
• Resource sharing
• Scalability
• Fault tolerance
• Multiple points of failure
• Security Concerns
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52. Applications of distributed computing
and newer challenges
• Mobile systems
• Sensor networks
• Ubiquitous or pervasive computing
• Peer-to-peer computing
• Publish-subscribe, content distribution, and multimedia
• Distributed agents
• Distributed data mining
• Grid computing
• Security in distributed systems
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