M.S. RAMAIAH INSTITUTE OF TECHNOLOGY
BANGALORE
(Autonomous Institute, Affiliated to VTU)
SYLLABUS
(For the Academic year 2014 – 2015)
Dept of Information Science and Engineering
M.Tech (Software Engineering)
I and II Semester
Page 1 of 29
History of the Institute:
M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, our
Founder Chairman who was a renowned visionary, philanthropist, and a pioneer in creating
several landmark infrastructure projects in India. Noticing the shortage of talented
engineering professionals required to build a modern India, Dr. M.S. Ramaiah envisioned
MSRIT as an institute of excellence imparting quality and affordable education. Part of Gokula
Education Foundation, MSRIT has grown over the years with significant contributions from
various professionals in different capacities, ably led by Dr. M.S. Ramaiah himself, whose
personal commitment has seen the institution through its formative years. Today, MSRIT
stands tall as one of India’s finest names in Engineering Education and has produced around
35,000 engineering professionals who occupy responsible positions across the globe.
About the Department:
The Department of Information Science and Engineering (ISE) was established in the year
1992 with an objective of producing high-quality professionals to meet the demands of the
emerging field of Information Science and Engineering. The department started with UG
programme with an annual sanctioned intake of 30 students and the intake was enhanced to
60 seats in the year 1991, to 90 seats in the year 2001 and then to 120 seats in the year
2008. The department started M.Tech in Software Engineering in the year 2004. The
department has been recognized as R&D center by VTU. The department has well equipped
laboratories. Some of the laboratories have also been set up in collaboration with industries
such as Intel, Apple, Honeywell, EMC2, Nokia Siemens and IBM. The department has highly
qualified and motivated faculty members. All faculty are involved in research and technical
paper publications in reputed technical journals, conferences across the world. The
department has been accredited by the NBA in 2001, 2004 & reaccredited in 2010. The
department has successfully conducted seminars & workshops for students and academicians
in the emerging technology.
Page 2 of 29
Faculty List
Sl.No.
Name of Faculty
Designation
Dr. Vijaya Kumar B P
Qualification
M.Tech (IITR), Ph.D
(IISc.)
1
2
Dr. Lingaraju G M
Ph.D
Professor
3
N Ramesh
MTech
Associate Professor
4
Rajaram M Gowda
Associate Professor
5
Dr.Mydhili K Nair
MTech. (Ph.D)
M.Tech, Ph.D (Anna
University)
6
Shashidhara H S
M.Tech. (Ph.D)
Associate Professor
7
George Philip C
M.Tech.
Associate Professor
8
T Tamilarasi
M.E (Ph.D)
Assistant Professor
9
Dr. Megha.P.Arakeri
M.Tech, Ph.D (NITK)
Associate Professor
10
Dr. Siddesh G M
M.Tech., Ph.D
Associate Professor
11
Savita K Shetty
M.Tech.
Assistant Professor
12
Myna A N
M.Sc(Engg) , (Ph.D)
Assistant Professor
13
Deepthi K
M.Tech.
Assistant Professor
14
Lincy Meera Mathews
M.Tech (Ph.D)
Assistant Professor
15
P M Krishna Raj
M.Sc(Engg), (Ph.D)
Assistant Professor
16
Rajeshwari S B
B.E (M.Tech.)
Assistant Professor
17
Prathima M N
M.E.
Assistant Professor
18
Pushpalatha M N
M.Tech (Ph.D)
Assistant Professor
19
Mohan Kumar S
M.Tech (Ph.D)
Assistant Professor
20
Sumana M
Assistant Professor
21
Prashanth Kambli
M.Tech (Ph.D)
M.Sc, (MSc(Engg.) by
Research)
22
Naresh E
M.Tech, (Ph.D)
Assistant Professor
23
Jagadeesh Sai D
M.Tech.
Assistant Professor
24
Mani Sekhar S R
M.Tech, (Ph.D)
Assistant Professor
25
Suresh Kumar K R
M.Tech, (Ph.D)
Assistant Professor
26
Sunitha R S
M.Tech.
Assistant Professor
27
Sandeep B L
M.Tech, (Ph.D)
Assistant Professor
28
Dayananda P
M.Tech, (Ph.D)
Assistant Professor
29
Koushik S
M.Tech, (Ph.D)
Assistant Professor
Professor & Head
Associate Professor
Assistant Professor
Page 3 of 29
Vision and Mission of the Institute and the Department
The Vision of MSRIT: To evolve into an autonomous institution of international standing for
imparting quality technical education
The Mission of the institute in pursuance of its Vision: MSRIT shall deliver global quality
technical education by nurturing a conducive learning environment for a better tomorrow
through continuous improvement and customization
Quality Policy
“We at M. S. Ramaiah Institute of Technology, Bangalore strive to deliver comprehensive,
continually enhanced, global quality technical and management education through an
established Quality Management system Complemented by the Synergistic interaction of the
stake holders concerned”.
The Vision of the Department: To evolve as an outstanding education and research center
of Information Technology to create high quality Engineering Professionals for the betterment
of Society
The Mission of the Department:
• To provide a conducive environment that offers well balanced Information Technology
education and research
• To provide training and practical experience in fundamentals and emerging
technologies
Program Education Objectives (PEOs)
Student will be able to
PEO1: Contribute in the area of Software Engineering development, maintenance & research
in social-technical system.
PEO2: Exhibit the Software Engineering skills for analysis, design & testing using modern tools
& technologies within or outside discipline.
PEO3: Act according to professional ethics and communicate effectively with various
stakeholders by demonstrating leadership qualities.
Program Outcomes (Pos)
Student will be able to
a: To nurture and enhance the knowledge in Software Engineering at a global perspective that
emphasizes on designing, developing and testing the software.
b: Analyze complex Software Engineering problems critically and pursue research
independently.
c: Ability to identify problems and arrive at optimal software solution within constraints.
d: Perceive & develop a suitable model by applying research skills and lifelong learning that
caters to multi disciplinary or Software Engineering domains either individually or in a group.
e: Ability to optimize the domain engineering activities with the help of modern IT tools.
f: Apply the knowledge of Software Engineering for effective project management considering
ethical and social responsibility.
g: Communicate effectively with respect to documentation and presentation with engineering
community and society.
Page 4 of 29
Board of Studies
S.
No.
Category
1
Head of the
Department
Concerned
2
Faculty members
nominated by the
Academic Council
Name of the Person with Official
Address
Dr. Vijaya Kumar B P
Head of the Department
Information Science & Engg.,
M.S. Ramaiah Institute of Technology,
MSRIT Post,
Bangalore – 560 054.
Shashidhara H S
Associate Professor
Dept. of Information Science &
Engineering,
M.S. Ramaiah Institute of Technology,
MSR Post, Bangalore – 560 054.
Dr. Megha P Arakeri
Associate Professor
Dept. of Information Science &
Engineering,
M.S. Ramaiah Institute of Technology,
MSR Post, Bangalore – 560 054.
Dr. Siddesh G M
Associate Professor,
Dept. of Information Science &
Engineering,
M.S. Ramaiah Institute of Technology,
MSR Post, Bangalore – 560 054.
P. M Krishna Raj
Asst. Professor
Dept. of Information Science &
Engineering,
M.S. Ramaiah Institute of Technology,
MSR Post, Bangalore – 560 054.
3
Experts in the subject
from outside the
College, to be
nominated by the
Academic Council
Dr. Satish Babu
Professor & Head,
Dept. of Computer Science &
Engineering
SIDDAGANGA INSTITUTE OF
TECHNOLOGY
NH 206,B.H.Road, Tumkur,
Karnataka 572103.
bsbsit@gmail.com
Dr. Dilip Kumar
Professor & Head
University Visvesvaraya College of
Engineering (UVCE),
K R Circle, Dr Ambedkar Veedhi,
Bangalore
Karnataka 560001
dilipkumarsm06@yahoo.com
Status
Chair Person
Member
Member
Member
Member
Autonomous
Institute
Member
Government
University
Member
Page 5 of 29
Dr.Y.N. SRIKANT
Professor, Department of Computer
Science and Automation,
Indian Institute of Science
Bangalore 560 012.
email : srikant@csa.iisc.ernet.in
Madhu N. Belur
Department of Electrical Engineering
Indian Institute of Technology Bombay
Powai, Mumbai 400 076
India
Dr. Chetan Kumar S
Manager Software Development
CISCO System
Cessna Business Park,
Kadubeesanahalli Village,
Varthur Hobli, Sarjapur Marathalli,
Bangalore – 560 087
Shivakumar.chetan@gmail.com
Mr.Niranjan Salimath
Beaglesloft , 37/5, Ulsoor Rd, Yellappa
Chetty Layout, Sivanchetti Gardens,
Bengaluru, Karnataka 560042
Email - ranju@beaglesloft.com
VTU Member
from IISc
Special
Invitee
Expert
Member from
Industry
Alumni
Member
Page 6 of 29
Scheme of Teaching for 2015-2017 Batch
I Semester M.Tech. (Software Engineering)
Sl.
No
Subject
Code
Subject
Credits*
L
T
P
Total
1
MSWE11
Advanced Mathematics
4
1
0
05
2
MSWE12
Advanced Topics in Software Engineering
4
0
1
05
3
MSWE13
Software Architecture and Design Patterns
4
0
1
05
4
MSWE14
Seminar I
0
2
0
02
5
MSWEAX
Elective – A
4
0
1
05
6
MSWEBX
Elective – B
4
0
1
05
20
3
4
27
Total
* L : Lecture
T : Tutorial
Elective - A
P : Practical
Elective - B
MSWEA1 Cloud Computing
MSWEB1
Soft Computing
MSWEA2 Embedded Systems Design
MSWEB2
Mobile Computing
II Semester M.Tech. (Software Engineering)
Sl.
No
Subject
Code
Subject
Credits*
L
T
P
Total
1
MSWE21
Software Measurements and Metrics
4
0
1
05
2
MSWE22
Software Project Management
4
0
1
05
3
MSWE23
Software Quality Engineering
4
0
1
05
4
MSWE24
Seminar - II
0
2
0
0
5
MSWECX
Elective – C
4
0
1
05
6
MSWEDX
Elective – D
4
0
1
05
20
2
5
27
Total
* L : Lecture
Elective - C
T : Tutorial
P : Practical
Elective - D
MSWEC1
Data Science
MSWED1 Storage Area Networks
MSWEC2
Machine Learning
MSWED2 Internet of Things
Page 7 of 29
III Semester M.Tech. (Software Engineering)
Sl.
No
Subject
Code
Subject
Credits*
L
T
P
Total
1
MSWE31
Software Engineering and Society
4
1
0
05
2
MSWE32
Project Preliminaries
0
2
6
08
4
MSWE33
Seminar - III
0
2
0
02
5
MSWEEX
Elective - E
4
0
1
05
8
5
7
20
Total
* L : Lecture
T : Tutorial
P : Practical
Elective - E
MSWEE1
Applied Parallel Computing
MSWEE3 System Performance and Analysis
(4:1:0)
MSWEE2
Bioinformatics
MSWEE4 Advanced Data Mining
IV Semester M.Tech. (Software Engineering)
Sl. No
1
Subject Code
MSWE41
Subject
Credits*
Project-2
L
T
P
Total
0
0
26
26
Total
* L : Lecture
26
T : Tutorial
P : Practical
Semester wise Credit Allocation
Semester
1
2
3
4
Total
Core
15
15
05
00
30
Electives
10
10
05
00
25
Project
00
00
08
26
34
Others
02
02
02
00
11
Page 8 of 29
Total
27
27
20
26
100
ADVANCED MATHEMATICS
Course Code: MSWE11
Prerequisites: Nil
Credits
Contact Hours
: 4:1:0
: 56L + 28T
Course coordinator(s): Dr. N L Ramesh (Mathematics Department)
Course objectives:
The students will:
• Learn to solve algebraic and transcendental equations numerically.
• Understand the concepts of Vector spaces and its applications to Difference
equations and Markov chains.
• Learn the concepts of Diagonalization by finding The Eigen values and Eigen
vectors.
• Learn the concepts of Random Process, Poisson Process.
• Understand the different Queueing Models.
Course Contents:
Unit I
Numerical Methods: Errors, Different types of errors, Fixed Point Iteration method,
2
Aitken’s Δ
Process method, Newton-Raphson method for a system of two
simultaneous equations, Horner’s method, Birje-Vieta method.
Introduction to Linear Algebra, Consistency, Gauss Jordan method.
Unit II
Vector Spaces: Vector spaces and Subspaces, Null Spaces, Column Spaces and Linear
Transformations, Linearly Independent Sets, Basis, Coordinate Systems, The Dimension
of Vector Space, Rank, Change of Basis, Applications to Difference Equations.
Unit III
Linear Transformations: Introduction to Linear Transformations, The Matrix of a Linear
Transformation, Rank-nullity theorem, Algebra of Linear Transformations.
Eigen
values
and Eigenvectors:
Characteristic
equation,
Diagonalization,
eigenvectors, System of ODEs as matrix differential equations, and linear
transformation.
Unit IV
Probability: Introduction to the theory of Probability, Random variables, Binomial,
Poisson’s Distribution, and Normal distribution, Joint Distributions, Stochastic process
and Markov chains.
Markov Process: Markov Process, Poisson Process, Pure Birth and Pure Death Process,
Birth and Death Process.
Unit V
Introduction to Queueing theory and Applications: Single server with infinite
system capacity, Queueing Models (M/M/1):( ∞ /FIFO), (M/M/1):(k/FIFO),(M/M/s):( ∞
/FIFO), (M/M/s):(k/FIFO),M/G/1 Queueing system characteristics, Case Studies.
Tutorials:
2
1. Examples on Aitken’s Δ Process method and Newton-Raphson method.
2.
3.
4.
5.
6.
Problems on Horner’s method and Birje-Vieta method.
Examples on consistency and Gauss Jordan method.
Problems on Vector spaces and Subspaces.
Problems on Basis and Change of Basis.
Application to Difference Equations and Markov chains.
Page 9 of 29
7.
8.
9.
10.
11.
12.
Problems on Linear Transformations.
Examples on Eigen values and Eigen vectors.
Problems on Random variables and distributions.
Problems on Markov and Poisson Process
Problems on M/M/1 Queueing system.
Problems on M/G/1 Queueing system.
References:
1. M.K. Jain, S. R. K. Iyengar, R. K. Jain, Numerical Methods for Scientific and
Engineering Computation, 5e, New Age International Publishers, 2007.
2. David C. Lay, Linear Algebra and its Applications, 3e, Pearson, 2011.
3. T. Veerarajan, Probability, Statistics and Random Processes, 3e, Tata McGraw Hill,
2008.
4. Kishore S. Trivedi, Probability and Statistics with Reliability, Queuing and Computer
Science Applications, 2e, John Wiley & Sons, 2008.
Course outcomes:
Students will be able to:
• Solve the problems of algebraic and transcendental equations using numerical
methods. (PO a, PO c)
•
Apply Vector spaces to solve Difference equations and problems arising in Markov
chains. (PO a, PO c)
•
Diagonalize the Matrix by finding the Eigen values and Eigen vectors. (PO a, PO c)
•
Apply the concept of Random process to discuss Poisson process, Birth and
Death process, Pure Birth and Pure Death process. (PO a, PO c)
•
Study the system characteristics in analyzing Queuing Models. (PO a, PO c)
Page 10 of 29
Course Code
ADVANCED TOPICS IN SOFTWARE ENGINEERING
: MSWE12
Credits
Prerequisites: Nil
Contact Hours
: 4:0:1
: 56L + 28P
Course coordinator(s): Krishna Raj P M
Course objectives:
• Analyze the various software development processes and methodologies
• Understand the process of software design
• Implement software modules in distributed fashion
• Appreciate the software quality measures beyond testing
• Study alternate models of software development including FOSS
Course Contents:
Unit I
Process Models and their evolution – NATO 1968, Waterfall Model, Spiral Model, Agile
Manifesto, Agile Process and Principles, Extreme programming, Scrum, Rational Unified
Process, CMM, CMM-i, PCMM, ISO 12207, Critical Analysis of Process Models.
Unit II
Software Design – Design principles, Software architecture, Design Patterns, User
Interface Design, Object Oriented Design with UML, Universal design applied to software
engineering, Design for Reuse.
Unit III
Programming Paradigms – Imperative programming, Functional programming, Logical
programming, Object oriented programming, Global software development – tools and
practices, Coding standards, Aspect oriented software engineering.
Unit IV
Software Testing and Quality Assurance – Testing processes, Testing tools, ISO Quality
Models – ISO 9001 and ISO 9126, Usability Testing, Test driven software development,
Object Oriented Testing with C&K metrics, Software Configuration Management.
Unit V
Research Issues in Software Engineering, Free and Open Source Software Engineering:
history, motivation, quality, development methodology and applications.
Laboratory:
The following tools are to be used for the specific exercises given by the faculty
1. Planning tools - task juggler, planner
2. Designing tools - star uml, dia, acceleo (in eclipse)
3. Programming tools - emacs, gcc, gdb, eclipse, netbeans, code::blocks
4. Testing tools - cxxtest, junit, selenium
5. SCM tools - make, git, apache ant, github, sourceforge
References:
1. Roger S Pressman, Software Engineering, 7th edition, TMH publication
2. Ian Sommerville, Software Engineering, 9th edition, Pearson Education
3. Rumbaugh, Object –Oriented Modeling and Design, Pearson Education
4. Jeff Tain, Software Quality Engineering, IEEE publication
5. Research Papers
Course outcomes:
Students will be able to:
• Identify various software development methods (PO a, PO d, PO e)
• Design using appropriate techniques (PO a, PO b, PO e)
• Implement distributed software using modern CASE tools (PO b, PO c, PO d)
• Verify the fitness of software (PO a, PO c, PO d, PO e)
• Understand the alternate software development methodologies (PO a, PO b, PO c)
Page 11 of 29
SOFTWARE ARCHITECTURE AND DESIGN PATTERNS
Course Code
: MSWE13
Prerequisites: NIL
Credits
Contact Hours
: 4:0:1
: 56L+28P
Course coordinator(s): Mohan Kumar S
Course objectives:
• To enable students to understand the challenges of advanced software design and
the issues associated with large-scale software architectures, frameworks, patterns and components.
• To develop the students' understanding of the tools and techniques that may be
used for the automatic analysis and evaluation of software.
Course Contents:
UNIT I
What is software architecture? : What software architecture is and what it is not,
Architectural Structures and Views, Architectural Patterns, Why is Software Architecture
Important?, Inhibiting or Enabling a System’s Quality Attributes, reasoning About and
Managing Change, Predicting system Qualities, Enhancing Communication among
Stakeholders, Carrying Early Design Decisions, Defining Constraints on an
Implementation, Influencing the Organizational Structure, Enabling Evolutionary
Prototyping, Improving Cost and Schedule Estimates, Supplying a Transferable, Reusable
Model, Allowing Incorporation of Independently Developed Components, Restricting the
Vocabulary of Design Alternatives, Providing a Basis for training, Summary, Discussion
Questions
UNIT II
Patterns: What is a Pattern? What Makes a Pattern? Pattern Categories, Relationship
between Patterns, Pattern Description, Patterns and Software Architecture, Summary
Patterns and Software Architecture: Introduction, Patterns in Software Architecture,
Enabling Techniques for Software Architecture, Non-functional properties of software
architecture
UNIT III
Architectural Patterns: Introduction, From Mud to Structure: Layers, Pipes and Filters,
Black Board, Distributed Systems: Broker, Interactive Systems: Model-View Controller,
Presentation-Abstraction Control.
UNIT IV
Architectural Patterns: Adaptable Systems: Micro-kernel, Reflection Design Patterns:
Introduction, Structural Decomposition: Whole-Part, Organization of Work: Master-Slave,
Access Control: Proxy
UNIT V
Design Patterns: Management: Command – Processor, View Handler, And And
Communication: Forwarder-Receiver, Client-Dispatcher-Server, Publisher-Subscriber
Pattern Systems: What is a Pattern System? Pattern Classification, Pattern Selection,
Pattern Systems as Implementation Guidelines.
Laboratory:
Exercises and Mini-projects based on concepts & tools.
References:
1. Len Bass, Paul Clement, Rick Kazman, “Software Architectures in Practice”, 3rd
Edition, Pearson, 2013.
2. Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal,
“Pattern Oriented Software Architecture: A System of Patterns”, John Wiley and
Sons, Volume 1, Reprinted February 2001.
3. Alan Shalloway, James R Trott, Design Patterns Explained, A New Perspective on
Object Oriented Design, 2nd Edition, Addison Wesley
Page 12 of 29
4. Mary Shaw and David Garlan: Software Architecture-Perspectives on an Emerging
Discipline, PHI Learning, 2007.
5. James W Cooper, Java Design Patterns, A Tutorial, Addison Wesley
6. Eric Freeman, Elisabeth Freeman, Head First Design Patterns, O’reilly Publications
Course outcomes:
Students will be able to:
• Classify some of the challenging design issues that software engineers face and
the trade-offs associated with the solutions to these. (PO a, PO b, PO c, PO e)
•
Describe the principles behind software patterns and be able to apply a number of
the fundamental patterns (PO b, PO c, PO e)
•
Summarize the need for software architecture and the principles of the classic
architectural styles (PO a, PO b, PO c, PO e)
•
Outline the major approaches to automated software analysis achievable through
static and dynamic analysis (PO a, PO b, PO c, PO e)
•
Demonstrate practical competence in the application and construction of tools to
support automated software analysis (PO b, PO c, PO e)
Page 13 of 29
CLOUD COMPUTING
Course Code : MSWEA1
Prerequisites: NIL
Credits
: 4:0:1
Contact Hours
: 56L+28P
Course coordinator(s): Dr. Siddesh G M
Course objectives:
• Introduce different aspects of cloud computing like; service models, challenges &
infrastructure.
•
•
•
Explore the various cloud computing applications & paradigms.
Analyze different cloud virtualization, resource management, data storage and
security issues with case studies.
Study the literature and exercise a project.
•
Apply features of cloud platforms in programming and software environments.
Course Contents:
UNIT I
Introduction: Network centric computing and network centric content, Peer-to-peer
systems, Cloud Computing: an old idea whose time has come, Cloud Computing delivery
models
&
Services,
Ethical
issues,
Cloud
vulnerabilities,
Challenges,
Cloud Infrastructure: Amazon, Google, Azure & online services, open source private
clouds.
UNIT II
Storage diversity and vendor lock-in, intercloud, Energy use & ecological impact of data
centers, service level and compliance level agreement, Responsibility sharing, user
experience, Software licensing. Cloud Computing: Applications & Paradigms,
Challenges, existing and new application opportunities, Architectural styles of cloud
applications, Workflows coordination of multiple activities, Coordination based on a state
machine model -the Zoo Keeper, The Map Reduce programming model, Apache Hadoop,
A case study: the GrepTheWeb application, Clouds for science and engineering, High
performance computing on a cloud, Social computing, digital content, and cloud
computing.
UNIT III
Cloud Resource Virtualization: Layering and virtualization, Virtual machine monitors,
Virtual machines Performance and security isolation, Full virtualization and
paravirtualization, Hardware support for virtualization Case study: Xen -a VMM based on
paravirtualization, Optimization of network virtualization in Xen 2.0, vBlades
-paravirtualization targeting a x86-64 Itanium processor, A performance comparison of
virtual machines, Virtual machine security, The darker side of virtualization, Software
fault isolation.
UNIT IV
Cloud Resource Management and Scheduling: Policies and mechanisms for resource
management, Stability of a two-level resource allocation architecture, Resource
bundling; combinatorial auctions for cloud Scheduling algorithms for computing clouds,
fair queuing, Start time fair queuing, borrowed virtual time, Cloud scheduling subject to
deadlines, Scheduling mapreduce applications subject to deadlines, Resource
management and application scaling.
UNIT V
Storage systems: Evolution, Storage models, file systems, databases, DFS, General
parallel File system, GFS, Hadoop, Locks & Chubby, Bigtable, Mega store. Cloud
security: Risks,VM Security. Cloud Application Development: AWS, Hadoop, trust
management & adaptive data streaming service, Cloud based FPGA synthesis.
Laboratory
Students, in teams of 3, should set a cloud environment and execute a project on cloud
platform.
Page 14 of 29
References:
1. Dan Marinescu, Cloud Computing: Theory and Practice, 1st edition, MK Publishers,
2013.
2. Kai Hwang, Jack Dongarra, Geoffrey Fox. Distributed and Cloud Computing, From
Parallel Processing to the Internet of Things, MK Publishers.
3. Anthony T. Velte, Toby J. Velte, Robert Elsenpeter, Cloud Computing: A Practical
Approach, McGraw Hill, 2010.
Course outcomes:
Students will be able to:
• Explain different aspects of cloud computing like; service models, challenges &
infrastructure. (PO c)
•
•
•
•
Explore the various cloud computing applications & paradigms. (PO c)
Analyze different cloud virtualization, resource management, data storage and
security issues with case studies. (PO c)
Study the literature and exercise a project. (PO c)
Apply features of cloud platforms in programming and software environments.
(PO d, PO e, PO g)
Page 15 of 29
Course Code
EMBEDDED SYSTEMS DESIGN
: MSWEA2
Prerequisites: NIL
Credits
Contact Hours
: 4:0:1
: 56L + 28P
Course coordinator(s): George Philip C
Course Objectives:
• Discuss the Embedded System design process and the formalisms for system design
•
•
•
•
Discuss the function of the components that make up a modern Embedded
Computing System
Explain the buses and protocols for embedded applications
Identify real-time embedded system requirements and its implications in the design
of RTOS
Discuss Embedded System Development process, and example embedded systems
Course Contents:
UNIT I
Embedded Computing:– Introduction, Complex Systems and Microprocessors, The
Embedded System Design Process (Case Study: GPS Moving Map), Formalisms for
System Design. (Task: UML documentation for the selected project)
Hardware Software Co-Design, Embedded Product Development Lifecycle Management,
Testing.
UNIT II
Model of an Embedded System, Microprocessor vs. Microcontroller, Example: A simple
temperature monitor, Classification of MCUs, Current Trends.
MCU:- The processor, The Harvard Architecture, GPIO, Power on Reset, Brown Out
Reset, Watch Dog Timer, Real Time Clock, Memory Types, Low Power Design.
Elementary ideas of Sensors, ADCs, Actuators.
UNIT III
Buses and Protocols:- Elementary ideas of Parallel, I2C, SPI, USB, IEEE 1394, RS-232,
RS-422/RS-485, Ethernet, CAN, WLAN, ZigBee, Bluetooth.
UNIT IV
Processes and Operating Systems:- Multiple Tasks and Processes, Multirate Systems,
Preemptive RTOS, Priority-Based Scheduling – Rate Monotonic Scheduling, Earliest
Deadline First Scheduling, Evaluating OS Performance, Power Optimization Strategies for
Processes.
UNIT V
Embedded Program Development, Integrated Development Environment, Compiler,
Assembler, Builder, Disassembly, Linker, Simulator, Downloading the Hex file, Hardware
Simulator.
Embedded System Examples:- Mobile Phone, Automotive Electronics, RFID, WISENET,
Robotics, Biomedical Applications, BMI.
Laboratory:
1. Using the Intel Atom Processor Board running the Linux OS:• Write and execute C programs.
• Test the functions of the following interfaces: RFID, GSM/GPRS, GPS, LED array, DC Motor, Switches, 4×4 Keypad, 16×2
LCD Serial Display, Accelerometer, Touch Screen, Potentiometer, Temp.
sensor, and Bluetooth
Page 16 of 29
• Capture images using the Web Camera.
2. A group project work has to be completed.
References:
1. Marilyn Wolf; Computers as Components: Principles of Embedded Computing System
Design, 3e, Morgan Kaufmann, 2012, ISBN: 9780123884367. (Unit I, IV)
2. Lyla B Das; Embedded Systems: An Integrated Approach, Pearson Education, 2013,
ISBN: 9788131787663. (Unit I, II, III, V)
3. Shibu K V; Introduction To Embedded Systems, MGH, 2009, ISBN: 9780070145894.
Course Outcomes:
Students will be able to:
• Describe the Embedded System design process and the formalisms for system
design (PO - a,c,e)
• Interpret the functions of the components that make up a modern Embedded
Computing System (PO - a)
• Identify the buses and protocols for embedded applications (PO - a)
• Identify real-time embedded system requirements and its implications in the
design of RTOS (PO - a, c)
• Explain the Embedded System Development process and identify example
embedded systems (PO - a,e)
Page 17 of 29
SOFT COMPUTING
Course Code
: MSWEB1
Prerequisites: Nil
Credits
Contact Hours
: 4:0:1
: 56L+28P
Course coordinator(s): Myna A N
Course objectives:
• To Introduce the soft computing methods to learn from experimental data and
transfer knowledge into analytical models
•
To design the various neural network structures
•
To Embed existing structured human knowledge into workable mathematics by
fuzzy logic models
To develop mathematical models using neuro fuzzy approaches
To highlight the effect of Genetic Algorithms in problem solving
•
•
Course Contents:
UNIT I
Neural Networks: History, overview of biological Neuro-system, Mathematical Models of
Neurons, ANN architecture, Learning rules, Learning Paradigms-Supervised,
Unsupervised and reinforcement Learning.
UNIT II
ANN training Algorithms perceptions, Training rules, Delta, Back Propagation Algorithm,
Multilayer Perceptron Model, Hopfield Networks, Associative Memories, Applications of
Artificial Neural Networks.
UNIT III
Introduction to Fuzzy Logic, Classical and Fuzzy Sets: Overview of Classical Sets,
Membership Function, Fuzzy rule generation. Operations on Fuzzy Sets, Fuzzy
Arithmetic, Fuzzy Logic, Uncertainty based Information: Compliment, Intersections,
Unions, Combinations of Operations, Aggregation Operations. Fuzzy Numbers, Linguistic
Variables, Arithmetic Operations on Intervals & Numbers, Lattice of Fuzzy Numbers,
Fuzzy Equations. Classical Logic, Multivalued Logics, Fuzzy Propositions, Fuzzy Qualifiers,
Linguistic Hedges. Information & Uncertainty, Nonspecificity of Fuzzy & Crisp Sets,
Fuzziness of Fuzzy Sets.
UNIT IV
Fuzzy Neural Networks: Integration of fuzzy logic and neural networks, Fuzzy neurons,
Hybrid neural nets, Computation of fuzzy logic inferences by hybrid neural net, Trainable
neural nets for fuzzy IF-THEN rules, Implementation of fuzzy rules by regular FNN of
Type2, Implementation of fuzzy rules by regular FNN of Type 3, Tuning fuzzy control
parameters by neural nets, Fuzzy rule extraction from numerical data, Neuro-fuzzy
classifiers, FULLINS, Applications of fuzzy neural systems.
UNIT V
Genetic Algorithms: An Overview: A Brief History Of Evolutionary Computation, The
Appeal Of Evolution, Biological Terminology, Search Spaces And Fitness Landscapes,
Elements Of Genetic Algorithms, A Simple Genetic Algorithm, Genetic Algorithms And
Traditional Search Methods, GA in problem solving: Evolving Computer Programs, Data
Analysis And Prediction, Evolving Neural Networks.
Laboratory:
Exercises and Mini-projects based on concepts & tools.
Page 18 of 29
References:
1. Anderson J.A., An Introduction to Neural Networks, PHI, 1999.
2. G.J. Klir & B. Yuan: Fuzzy Sets & Fuzzy Logic, PHI, 1995.
3. Robert Fuller, Lecture Notes on Neural Fuzzy Systems, 1995.
4. Melanie Mitchell: An Introduction to Genetic Algorithm, PHI, 1998.
5. Vojislav Kecman, “Learning and Soft Computing,” Pearson Education (Asia) Pte. Ltd.
2004
Course outcomes:
Students will be able to:
• Identify, formulate and develop solutions for soft computing problems using
neural networks (PO a, PO d, PO e)
•
Analyze the various neural network algorithms (PO a, PO d, PO e)
•
Formulate mathematical models, Design and develop solutions for soft computing
problems using fuzzy logic (PO a, PO d, PO e)
•
Design solutions using fuzzy neural network approach (PO a, PO d, PO e)
•
Implement genetic algorithms for solving problems (PO a, PO d, PO e)
Page 19 of 29
MOBILE COMPUTING
Course Code
: MSWEB2
Prerequisites:
NIL
Credits
Contact Hours
: 4:0:1
: 56L+28P
Course coordinator(s): Dr. B P Vijaya Kumar
Course objectives:
To introduce networking concepts of Wireless transactions, spread spectrum, medium
access and access network.
To learn Network and transport layer for mobile networks and the various concepts
used in telecommunication systems.
Exploring different techniques to handle databases and data dissemination for
mobile computing
To learn Data Synchronization in Mobile Computing along with mobile agents,
services, file systems and security.
Exploring various aspects of mobility support, and various mobile operating systems.
Course Contents:
UNIT I
Introduction: Challenges in mobile computing, coping with uncertainities, resource
poorness, banwidth, etc. Cellular architecture, co-channel interference, frequency reuse,
capacity increase by cell splitting. Evolution of mobile system: CDMA, FDMA, TDMA,
GSM. Wireless LAN: IEEE 802.11, HiperLAN, Blue tooth, Wi Max.
UNIT II
Mobililty Management: Cellular architecture, Co-channel interference, Mobility: handoff,
types of handoffs; location management, HLR-VLR scheme, hierarchical scheme,
predictive location management schemes. Mobile IP, Dynamic host configuration
protocol, Mobile transport layer - Traditional and classical TCP.
UNIT III
Publishing & Accessing Data in Air: Databases: Database Hoarding Techniques, Data
Caching, Transactional Models, Query Processing.
Data Dissemination and Broadcasting Systems: Communication Asymmetry,
Classification of Data-Delivery Mechanisms, Data Dissemination Broadcast Models,
Selective Tuning and Indexing Techniques.
UNIT IV
Data Synchronization in Mobile Computing Systems: Synchronization, Synchronization
software for mobile devices, Synchronization protocols, SyncML - Synchronization
language for mobile computing, Sync4J (Funambol), Synchronized Multimedia Markup
Language (SMIL).
Mobile Devices: Server and Management: Mobile agent, Application server, Gateways,
Portals, Service Discovery, Device management, Mobile file systems, security.
UNIT V
Support Mobility- File Systems, Mobile Security, Mobile operating systems: Windows,
Symbian OS, Android, iOS, Linux for Mobile devices.
Laboratory:
1. Study of network simulators: Qualnet
2. Use Telnet to execute HTTP commands directly against a Web Server
3. Learn to setup remote desk top connection between a client and a server, via web.
4. Implement web caching
a) Configuring windows as DHCP server
b) Capture and analyze DHCP traffic generation.
Page 20 of 29
5.
6.
Study and practice programming languages for different Mobile operating system
Application development using different Mobile OS over the emulator/mobile devices
References:
1.
2.
3.
4.
Mobile Computing, RajKamal, Oxford University Press, 2nd Edition, 2012
Jochen Schiller, Mobile Communications, 2nd Edition, Pearson 2003
Reza B, Mobile Computing Principles, Cambridge University press 2005
Balancing Push and Pull for Data Broadcast, S.Acharya, M. Franklin and S. Zdonik.
Proceedings of the ACM SIGMOD, Tuscon, AZ, May 1997.
5. Disseminating Updates on Broadcast Disks, S.Acharya, M. Franklin and S. Zdonik. Proceedings of the 22nd VLDB Conference, Mumbai (Bombay), India, Sept 1996.
Course outcomes:
Students will have the ability to:
CO1: Discuss the challenges and issues in mobile computing, and describe the basic
principles and techniques, and protocol standards in wireless networks – PO a, PO b
CO2: Describe the concept of network and transport layer for mobile networks in respect
to mobility management. PO a, PO d
CO3: Analyze the database handling, data dissemination, Synchronization in respect to
Mobile data base and computing. PO a, PO b, PO c, PO e
CO4: Describe and illustrate the mobile services, agents and mobility support using
different file systems and platforms. PO a, PO b, PO c,
CO5: Develop mobile applications by analyzing their characteristics and requirements,
selecting the appropriate computing models and software architectures with hands-on
training. PO c, PO e
Page 21 of 29
SOFTWARE MEASUREMENTS AND METRICS
Course Code : MSWE21
Prerequisites: NIL
Credits: 4:0:1
Contact Hours : 56L+28P
Course coordinator(s): Krishna Raj P M
Course objectives:
• Introduce concepts of Software measurements and the necessary metrics to
measure the software
•
Carry out empirical investigation and analyze the software measurement data
•
Define and measure the internal and external attributes of software
•
•
•
Measure the resource required and make process predictions
Plan and Develop a measurement program
Learn about the measurement in practice and the ongoing research in software
measurement and metrics
Course Contents:
UNIT I
Fundamentals of measurements, Measurements- what is it and why do it? Measurement in everyday life, in Software Engineering, Scope of software metrics, The
basics of measurements - Representational theory of measurement, measurement and
models, measurement scale and scale types, meaningfulness in measurement, A goal
framework for software measurement – Classifying software measures, determining
what to measure, applying the framework, software measurement validation and in
practice.
UNIT II
Empirical investigation – Four principals of investigation, planning formal experiments,
planning case studies, Software metrics data collection – What is good data, how to
define the data, how and when to collect data, how to store and extract data, Analyzing
software measurement data – Analyzing the results of experiments, examples of simple
analysis techniques, more advanced methods, overview of statistical tests.
UNIT III
Measuring internal software attributes: size – Aspects of software size, length, reuse,
functionality, complexity, Measuring internal software attributes: structure – Types of
structural measures, control-flow structure, modularity and information flow attributes,
object-oriented metrics, data structure, difficulties with general complexities measures,
Measuring external software attributes – Modeling software quality, measuring aspects
of quality.
UNIT IV
Resource measurement: productivity, teams and tools – Meaning of productivity,
productivity of what? Measuring productivity, Teams, tools and methods, Making process
predictions – Good estimates, cost estimation: problems and approaches, models of
effort and cost, problems with existing modeling methods, dealing with problems of
current estimation methods, implications for process prediction.
UNIT V
Planning a measurement program – What is a metrics plan, Why and what: developing
goals, questions, and metrics, Where and when: mapping measures to activities, How:
measurement tools, Who: measurers, analysts and audience, revising the plan,
Measurement in practice – success criteria, measurement in the small and in the large,
lessons learned, Empirical research in software engineering – problems with empirical
research, Investigating products, resources, processes, Measurement today and
tomorrow.
Laboratory:
The students are expected to take a project and apply all suitable metrics to it
individually.
Page 22 of 29
References:
1. Norman E. Fenton and Shari Lawrence Pfleeger, Software Metrics: A Rigorous
Approach, PWS; 2nd edition, 1998
2. Stefhan H Kan, Metrics and Models in Software Quality Engineering, Pearson
Education , 2003
Course outcomes:
Students will be able to:
• Identify the basics of measurement theory and its application to software (PO a)
•
Design the experiment for emperical investigation of software (PO d)
•
Measure the internal and external attributes of software (PO b)
•
Estimate time, cost and personnel required to develop given software (PO a)
•
Devise a measurement plan for a software project and implement it (PO a)
Page 23 of 29
Course Code
SOFTWARE PROJECT MANAGEMENT
: MSWE22
Prerequisites: NIL
Credits
Contact Hours
: 4:0:1
: 56L + 28P
Course coordinator(s): Dr.Mydhili K Nair
Course objectives:
• Classify software projects. Evaluate projects. Manage Programmes
• Select project approach. Estimate effort. Activity planning
• Manage risks. Allocate resources. Project monitoring and control.
• Manage people. Work in teams. Manage software quality.
Course Contents:
UNIT I
Introduction, Contract & Technical project Management, Activities, Plans, Methods,
Methodologies, objectives, business case, Success, failure, Management control,
Traditional vs Modern project management, Project portfolio management, Project
evaluation, Cost-benefit evaluation Techniques, Risk Evaluation, Resource allocation,
Strategic management, Benefits, Step Wise Project Planning.
UNIT II
Build/Buy? Methodologies, software processes, process models, prototyping, Incremental
delivery, Atern, RAD, Agile methods, XP, Scrum, Selection of process model. Basis for
software effort estimation, models, Expert judgment, Estimation by analogy, Albrecht
FPA, FP Mark II, COSMIC FFP, COCOMO II, Cost estimation, Staffing pattern, Schedule
compression, Capers Jones rules, When activity planning? Project schedules & activities,
Sequencing & scheduling activities, Network Model, Time, Forward & Backward Pass,
Critical path, Activity float, Shorten project duration, Critical activities, Activity on Arrow
networks.
UNIT III
Categories of risk, deal with risk, Risk identification, assessment, planning, Management.
Evaluation of risks to the schedule, PERT, Monte Carlo, Critical chain. Nature of
Resources, resource requirements, Scheduling resources, creating critical paths,
Counting the cost, Publish resource schedule, cost schedules, scheduling sequence.
UNIT IV
Monitoring and control Framework, Collect data, Review, Project termination, progress,
cost monitoring, Earned Value Analysis, Prioritizing Monitoring, Get project back to
target, Change control, Software Configuration Management, Managing contracts,
Stages, terms of contract, contract management, Acceptance.
UNIT V
Managing people, Understanding behavior, Organizational behavior, Selecting the right
person, Best methods, Motivation, The Oldham-Hackman model, Stress, Ethical
concerns. Becoming a team, Decisions, Organizational and Team structures,
Coordination, Dispersed and Virtual teams, Communication genres, Communication
plans, Leadership. Place and importance of quality, ISO 9126, Product and Process
metrics, Product vs Process quality, Quality management systems, CMM, Enhance
quality, Testing, Reliability, Quality plans.
Laboratory:
Students, in teams of 3, should implement a project according to any one of the
following standards - Team Software Process, Capability Maturity Model, PRINCE2, ISO
10006:2003
Page 24 of 29
References:
1. Bob Hughes, Mike Cotterell, Software Project Management, 4th Edition, Tata McGraw
Hill Publications, 2006
2. Kathy Schwalbe, Information Technology Project Management, 5th Ed, Thompson,
2006
3. Watts S. Humphrey, Managing the Software Process, Addison-Wesley, 1989
4. Selected papers from current literature
Course outcomes:
Students will be able to:
• Classify and evaluate projects (PO a, PO c, PO g)
•
Select a project approach, estimate effort and plan activities (PO a, PO b, PO d,
PO f)
•
Manage risks and Allocate resources (PO a, PO e, PO g)
•
•
Monitor and control projects and Manage contracts (PO a, PO e, PO g)
Manage people, work in teams and Manage quality (PO a, PO g)
Page 25 of 29
SOFTWARE QUALITY ENGINEERING
Course Code: MSWE23
Prerequisites: NIL
Credits
: 4:0:1
Contact Hours
: 56L + 28P
Course coordinator(s): Naresh E
Course objectives:
• Understand the basic definitions/concepts of Quality engineering and software testing.
• Analyze the concepts like verification and its techniques like Reviews, Walkthroughs,
and Inspections in the development of software.
• Apply the concepts like validation and its techniques like black box testing and white
box testing.
• Understand the different types of System testing like Performance testing, Security
testing, Load testing, Reliability testing, etc.
• Design and Execution of Test Scenarios and Test Cases with the reports to track and
monitor the defects.
Course Contents:
UNIT I
Introduction: Software Quality - Perspective and Expectations, Historical perspective of
Quality, Quality frameworks, Quality Assurance as dealing with defects, Defect
prevention detection and Containment strategies. QA Process and Quality
Engineering: QA Activates in Software Processes, Verification and Validation
Perspectives, Reconciling the Two Views Quality Engineering: Activities and Process
Quality Planning: Goal Setting and Strategy Formation Quality Assessment and
Improvement Quality Engineering in Software Processes.
UNIT II
A Perspective on Testing: Basic definitions, Test Scenarios, Test cases, Insights from
a Venn diagram, Identifying test cases, Error, fault and Failure taxonomies, testing
throughout the SDLC, Levels of testing, Activities of Test engineer, Test/Debug life cycle,
Testing principles, The cost of bugs, What makes a good software tester?, Testing
Axioms. Examples: The triangle problem, The NextDate function, the commission
problem, The SATM (Simple Automatic Teller Machine) problem, the currency converter.
UNIT III
Functional Testing: Boundary value analysis, Robustness testing, Worst-case testing,
Special value testing, Examples, Random testing, Equivalence classes, Equivalence test
cases for the triangle problem, Decision tables, Test cases for the triangle problem.
Compatibility testing, Usability testing, website testing, Testing the documentation. Case
studies.
UNIT IV
Coverage-based Testing: Statement coverage testing, Condition coverage testing,
Path coverage, computing cyclomatic complexity, exploratory testing. Static Testing:
Reviews, Types of reviews, Inspections, Inspection process, Inspection roles, benefits of
inspection, Checklists. Nonfunctional testing with case studies.
UNIT V
Test Management and Automation: Introduction, Test Planning, Test Management,
Test Process, Test Reporting, Best Practices, Test Plan template. What is Test
Automation? Terms used in Automation, Skills needed for automation, what to
automate, scope of automation, design and Architecture for automation, Process model
for automation, Defect management and reporting. Test Metrics and Measurements:
What are metrics and measurements? Why metrics in Testing? Types of metrics: Project
metrics – Effort variance, Schedule variance, Effort distribution across phases.
Page 26 of 29
Laboratory:
1. Practicing static/verification techniques.
2. Practicing code-coverage testing techniques.
3. Discuss Manual versus Automated testing.
4. Application of Black-box testing techniques and White-box testing techniques with
case studies (Triangle problem, NextDate Function, SATM, Currency Convertor problem).
5. Practicing Functional/System testing tools like selenium, QTP, etc (Discuss merits and
demerits, solutions for demerits).
6. Practicing test/defect management activities using the bugzilla tool.
References:
1. Jeff Tian, “Software Quality Engineering: Testing, Quality Assurance, and Quantifiable
Improvement”, - John Wiley and
Sons Inc., and IEEE Computer Society Press,
February 2005
2. Edwar.Dkit. “Software testing in the Real World”, Pearson Education 2003.
3. Paul C. Jorgensen: Software Testing, A Craftsman’s Approach, 3 Edition, Auerbach
Publications, 2008.
4. Srinivas Sesikan and Ramesh Gopalswamy, “Principles of Software Testing”, Pearson
Education.
5. William E Perry. “Effective Methods for Software Testing”, Second Edition, John Wiley
and Sons
6. Stephan H. Kan, “Metrics and Models in Software Quality Engineering”, Second
Edition, Pearson Education.
7. Dustin, “Effective Software Testing: 50 Specific Ways to Improve Your Testing”,
Pearson Education.
Course outcomes:
Students will be able to:
• Gain the knowledge of the basic definitions/concepts of Quality engineering and
software testing. (PO a)
•
Analyze the concepts like verification and its techniques like
Walkthroughs, and Inspections in the development of software. (PO a)
•
Apply the concepts like validation and its techniques like black box testing and
white box testing. (PO e)
•
Gain the knowledge, understand the different types of System testing like
Performance testing, Security testing, Load testing, Reliability testing, etc. (PO g)
•
Design and Execute Test Scenarios and Test Cases with the reports to track and
monitor the defects. (PO d, PO g)
Reviews,
Page 27 of 29
STORAGE AREA NETWORKS
Course Code: MSWED1
Prerequisites: NIL
Credits: 4:0:1
Contact Hours: 56L+28P
Course coordinator(s): George Philip C
Course objectives:
• Provide a comprehensive view of storage architectures, and the logical and
physical components of storage infrastructure including storage subsystems
• Discuss RAID and intelligent storage systems
• Discuss storage networking technologies such as FC SAN, IP SAN, and FCoE
• Discuss NAS, object-based and unified storage
• Discuss business continuity, storage security, storage monitoring and
management activities
Course Contents:
UNIT I
Introduction: Information Storage, Evolution of Storage Architecture, Data Centre
Infrastructure, Virtualization and Cloud Computing.
Data Centre Environment: Application, DBMS, Host, Connectivity, Storage, Disk Drive
Components, Disk Drive Performance, Host Access to Data, Direct-Attached Storage,
Storage Design Based on Application, Disk Native Command Queuing, Introduction to
Flash Drives.
UNIT II
Data Protection: RAID Implementation Methods, Array Components, Techniques, Levels,
Impact on Disk Performance, Comparison, Hot Spares.
Intelligent Storage System: Components, Storage Provisioning, Types.
UNIT III
Fibre Channel Storage Area Networks: FC Overview, Evolution, Components, FC
Connectivity, Ports, FC Architecture, Fabric Services, Login Types, Zoning, FC
Topologies, Virtualization in SAN.
IP SAN and FCoE: iSCSI, FCIP, FCoE.
UNIT IV
Network-Attached Storage: Benefits, Components, NAS I/O Operation, Implementations,
File Sharing Protocols, Factors Affecting NAS Performance, File-Level Virtualization.
Object Based and Unified Storage: Object Based Storage Devices, Content Addressed
Storage, CAS Use Cases, Unified Storage.
UNIT V
Business Continuity: Information Availability, Terminology, Planning Lifecycle, Failure
Analysis, Impact Analysis, Solutions.
Cloud Computing Infrastructure.
Securing the Storage Infrastructure: Framework, Risk Triad, Domains. Security
Implementations in Storage Networking, Virtualized and Cloud Environments.
Managing the Storage Infrastructure: Monitoring, Management Activities, Management
Challenges, Information Lifecycle Management, Storage Tiering.
Page 28 of 29
Laboratory:
Exercises and Mini-project using open source tools.
References:
1. Somasundaram G., Alok Shrivastava, (EMC Education Services); “Information
Storage and Management”; 2e, Wiley India, 2012, ISBN 9788126537501.
2. Robert Spalding; “Storage Networks: The Complete Reference”; Tata McGraw Hill,
2003.
Course outcomes:
The students will have
CO1: The ability to describe storage architectures, and the logical and physical components
of storage infrastructure including storage subsystems. (PO - a,c,d,e,g)
CO2: The ability to describe RAID and intelligent storage systems. (PO - a,c)
CO3: The ability to illustrate storage networking technologies such as FC SAN, IP SAN, and
FCoE. (PO - a)
CO4: The ability to describe NAS, object-based and unified storage. (PO - a)
CO5: The ability to explain business continuity, storage security, storage monitoring and
management activities. (PO - a,d,e)
Page 29 of 29