JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M.TECH ... CSE bran… · JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M.Tech (NE URAL NETWORKS) I SEMESTER ARTIFICIAL INTELLIGENCE

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABADM.TECH (NEURAL NETWORKS)

COURSE STRUCTURE AND SYLLABUS

I YEAR I SEMESTERCode Group Subject L P Credits

Advanced Problem Solving 3 0 3Computer Systems Design 3 0 3Artificial Intelligence 3 0 3Neural Networks 3 0 3

Elective –I Pervasive ComputingMachine LearningSpeech Processing

3 0 3

Elective -II Wireless Networks and Mobile ComputingStorage Area NetworksCloud Computing

3 0 3

Lab Advance Problem Solving Lab 0 3 2Seminar - - 2

Total Credits (6 Theory + 1 Lab.) 22

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

M.Tech (NEURAL NETWORKS) I SEMESTERADVANCED PROBLEM SOLVING

Unit IOOP Using Java - Class and Objects, Variables, Operators, Expressions, Methods, Decisionstatements,Loops,Arrays,OOP concepts- Encapsulation, Inheritance, Polymorphism, Abstraction,Modularity, Exception handling, Input and Output,Java and Pointers,Interfaces,Packages, Abstractclasses,Casting in Inheritance hierarchy,Casting with Interfaces,Vectors in java.util,Data Structuresand OOP,Writing a java program-Design,coding,testing and debugging.Basic concepts(Review)- Abstact Data Types, Data structures, Algorithms- Characteristicsof Algorithms, Performance analysis- Time complexity and Space complexity,Asymptotic Analysis-Big O, Omega and Theta notations.Unit IILinear data structures- The List ADT, Array and Linked Implementations,Singly Linked Lists-Operations-Insertion,Deletion,Traversals,DoublyLinkedLists-Operations-Insertion,Deletion,SkipLists-implementation, StackADT,definitions,operations, Array and Linked implementations,applications-infix to postfix conversion, recursion implementation,tail recursion,nontail recursion,indirect recursion,QueueADT, definitions and operations ,Array and Linked Implementations,Priority Queue ADT,DequeADT,Implementation using doubly linked lists,Stacks and Queues in java.util.Unit IIINon Linear data structures-Trees-Basic Terminology, Binary tree ADT,array and linkedrepresentations,iterative traversals,threaded binary trees,Applications-Disjoint-Sets,Union and Findalgorithms,Huffman coding,General tree to binary tree conversion, Realizing a Priority Queue usingHeap.Search Trees- Binary Search Tree ADT, Implementation, Operations- Searching, Insertion andDeletion, Balanced Search trees-AVL Trees, Operations – Insertion and Searching,B-Trees, B-Tree oforder m,Operations- Insertion,Deletion and Searching,Introduction to Red-BlackTrees, Splay Trees,B*-Trees,B+-Trees(Elementary treatement), Comparison of Search Trees,Trees in java.util.Unit IVSearching- Linear Search,Binary Search, Hashing-Hash functions,Collision-Handling schemes,Hashingin java.util,Dictionary ADT,Linear list representation,Skip list representation,Hash tablerepresentation,Comparison of Searching methods.Sorting- Bubble Sort,Insertion Sort,Shell sort,Heap Sort,Radix Sort,Quick sort,Merge sort, Comparisonof Sorting methods,Sorting in java.util.Unit VGraphs–Basic Terminology, Graph Representations- Adjacency matrix,Adjacency lists,Adjacencymultilists,Graph traversals- DFS and BFS, Spanning trees-Minimum cost spanning trees,Kruskal’sAlgorithm for Minimum cost Spanning trees, Shortest paths- Single Source Shortest Path Problem,AllPairs Shortest Path Problem.Text Processing - Pattern matching algorithms- The Knuth-Morris-Pratt algorithm,The Boyer-Moorealgorithm,Tries- Standard Tries, Compressed Tries, Suffix tries.TEXT BOOKS :1. Data structures and Algorithms in Java,Adam Drozdek,Cengage Learning.2. Data structures and Algorithms in Java,Michael T.Goodrich and R.Tomassia , Wiley

India edition.3. Data structures , Algorithms and Applications in Java,S.Sahani, Universities Press.

REFERENCE BOOKS :

1. Data structures and algorithms in Java,Robert Lafore,Pearson Education.2. Data structures with Java,W.H.Ford and W.R.Topp,Pearson Education.3. Classic Data structures in Java,T.Budd,Pearson Education.4. Data Structures using Java,D.S. Malik and P.S.Nair, Cengage Learning,5.An Introduction to Data structures and Algorithms,J.A.Storer,Springer.6.Data structures and Java Collections Frame Work,W.J.Collins,Mc Graw Hill.7.Data structures with Java,J.R.Hubbard and A.Huray,PHI.8.Data Structures using Java,Y.Langsam,M.Augenstein,A.Tanenbaum,Pearson Education.9.Data structures with Java,J.R.Hubbard,Schaum’s Outlines,TMH.


M.Tech (NEURAL NETWORKS) I SEMESTER

COMPUTER SYSTEMS DESIGNUNIT IComputer structure – hardware, software, system software, Von-neumann architecture – casestudy. IA -32 Pentium: registers and addressing, instructions, assembly language, program flowcontrol, logic and shift/rotate instructions, multiply, divide MMX,SIMD instructions, I/Ooperations, subroutines.Input/Output organizaton, interrupts, DMA, Buses, Interface circuits, I/O interfaces, devicedrivers in windows, interrupt handlers

UNIT IIProcessing Unit: Execution of a complete instruction, multiple bus organization, hardwiredcontrol, micro programmed control.Pipelining: data hazards, instruction hazards, influence on instruction sets, data path & controlconsideration,RISC architecture introduction.

UNIT – IIIMemory: types and hierarchy, model level organization, cache memory, performanceconsiderations, mapping, virtual memory, swapping, paging, segmentation, replacementpolicies.

UNIT – IVProcesses and Threads: processes, threads, inter process communication, classical IPCproblems, Deadlocks.

UNIT – VFile system: Files, directories, Implementation, Unix file systemSecurity: Threats, intruders, accident data loss, basics of cryptography, user authentication.

TEXT BOOKS:1. Computer Organization – Car Hamacher, Zvonks Vranesic, SafeaZaky, Vth Edition,

McGraw Hill.2. Modern Operating Systems, Andrew S Tanenbaum 2nd edition Pearson/PHI

REFERENCE BOOKS:1. Computer Organization and Architecture – William Stallings Sixth Edition,

pearson/PHI2. Morris Mano -Computer System Architecture –3rd Edition-Pearson Education .3. Operating System Principles- Abraham Silberchatz, Peter B. Galvin, Greg Gagne 7th

Edition, John Wiley4. Operating Systems – Internals and Design Principles Stallings, Fifth Edition–2005,

Pearson Education/PHI



ARTIFICIAL INTELLIGENCEUNIT-IIntroduction : AI problems, foundation of AI and history of AI intelligent agents:Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problemsolving agents, problem formulation.Searching: Searching for solutions, uniformed search strategies – Breadth first search, depth first search, Depthlimited search, Iterative deepening depth first search bi-direction search - comparison. Search with partialinformation (Heuristic search) Greedy best first search, A* search, Memory bounded heuristic search, Heuristicfunctions.

UNIT-IILocal search Algorithms, Hill climbing, simulated, annealing search, local beam search, genetical algorithms.Constrain satisfaction problems: Backtracking search for CSPs local search for constraint satisfaction problems.Game Playing: Adversial search, Games, minimax, algorithm, optimal decisions in multiplayer games, Alpha-Betapruning, Evaluation functions, cutting of search.

UNIT-IIIKnowledge Representation & Reasons logical Agents, Knowledge – Based Agents, the Wumpus world, logic,propositional logic, Resolution patterns in propos ional logic, Resolution, Forward & Backward. Chaining.First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts forwardchaining, Backward chaining, Resolution.

UNIT-IVPlanning – Classical planning problem, Language of planning problems,Expressiveness and extension, planning with state – space search, Forward states spare search, Backward statesspace search, Heuristics for stats space search. Planning search, planning with state space search, partial orderplanning Graphs.

UNIT-VLearning – Forms of learning, Induction learning, Learning Decision Tree, Statistical learning methods, learningwith complex data, learning with Hidden variables – The EM Algorithm, Instance Based learning, Neural Networks.

TEXT BOOKS:1. Artificial Intelligence – A Modern Approach. Second Edition, Stuart Russel,Peter Norvig, PHI/Pearson Education.2. Artificial Intelligence, 3rd Edition, Patrick Henry Winston., Pearson Edition,

Reference:1. Artificial Intelligence , 2nd Edition, E.Rich and K.Knight (TMH).2. Artificial Intelligence and Expert Systems – Patterson PHI3. Expert Systems: Principles and Programming- Fourth Edn, Giarrantana/ Riley, Thomson4. PROLOG Programming for Artificial Intelligence. Ivan Bratka- Third Edition– Pearson Education.


M.Tech (NEURAL NETWORKS) I SEMESTERNEURAL NETWORKS

UNIT IINTRODUCTION - what is a neural network? Human Brain, Models of a Neuron, Neuralnetworks viewed as Directed Graphs, Network Architectures, Knowledge Representation,Artificial Intelligence and Neural Networks (p. no’s 1 –49)LEARNING PROCESS 1 – Error Correction learning, Memory based learning, Hebbianlearing,(50-55)

UNIT IILEARNING PROCESS 2: Competitive, Boltzmann learning, Credit Asssignment Problem,Memory, Adaption, Statistical nature of the learning process, (p. no’s 50 –116)SINGLE LAYER PERCEPTRONS – Adaptive filtering problem, UnconstrainedOrganization Techniques, Linear least square filters, least mean square algorithm, learningcurves, Learning rate annealing techniques, perceptron –convergence theorem, Relationbetween perceptron and Bayes classifier for a Gaussian Environment (p. no’s 117 –155)

UNIT IIIMULTILAYER PERCEPTRON – Back propagation algorithm XOR problem, Heuristics,Output representation and decision rule, Comuter experiment, feature detection, (p. no’s 156 –201)BACK PROPAGATION - back propagation and differentiation, Hessian matrix,Generalization, Cross validation, Network pruning Techniques, Virtues and limitations of backpropagation learning, Accelerated convergence, supervised learning. (p. no’s 202 –234)

UNIT IVSELF ORGANIZATION MAPS – Two basic feature mapping models, Self organizationmap, SOM algorithm, properties of feature map, computer simulations, learning vectorquantization, Adaptive patter classification, Hierechel Vector quantilizer, contexmel Maps(p. no’s 443 –469, 9.1 –9.8 )

UNIT VNEURO DYNAMICS – Dynamical systems, stavility of equilibrium states, attractors,neurodynamical models , manipulation of attarctors as a recurrent network paradigm (p. no’s664 –680, 14.1 –14.6 )HOPFIELD MODELS – Hopfield models, computer experiment I (p. no’s 680-701, 14.7 –

14.8 )

TEXT BOOKS:1. Neural networks A comprehensive foundations, Simon Hhaykin, Pearson Education 2nd

Edition 2004REFERENCE BOOKS

1. Artifical neural networks - B.Vegnanarayana Prentice Halll of India P Ltd 20052. Neural networks in Computer intelligence, Li Min Fu TMH 20033. Neural networks James A Freeman David M S kapura pearson education 2004

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABADM.Tech (NEURAL NETWORKS) I SEMESTER

PERVASIVE COMPUTING

ELECTIVE – I

Unit I:Pervasive Computing Application - Pervasive Computing devices and Interfaces -Device technology trends, Connecting issues and protocols

Unit II:Pervasive Computing and web based Applications - XML and its role in PervasiveComputing - Wireless Application Protocol (WAP) Architecture and Security - WirelessMark-Up language (WML) – Introduction

Unit III:

Voice Enabling Pervasive Computing - Voice Standards - Speech Applications inPervasive Computing and security

Unit IV:PDA in Pervasive Computing – Introduction - PDA software Components, Standards,emerging trends - PDA Device characteristics - PDA Based Access Architecture

Unit V:User Interface Issues in Pervasive Computing, Architecture - Smart Card- basedAuthentication Mechanisms - Wearable computing Architecture

Text Books:

1. Jochen Burkhardt, Horst Henn, Stefan Hepper, Thomas Schaec & Klaus Rindtorff. ---Pervasive Computing Technology and Architecture of Mobile Internet Applications, AddisionWesley, Reading, 2002.

2. Uwe Ha nsman, Lothat Merk, Martin S Nicklous & Thomas Stober: Principles of MobileComputing, Second Edition, Springer- Verlag, New Delhi, 2003.

Reference Books:

1. Rahul Banerjee: Internetworking Technologies: An Engineering Perspective,Prentice –Hall of India, New Delhi, 2003. (ISBN 81-203-2185-5)

2. Rahul Banerjee: Lecture Notes in Pervasive Computing, Outline Notes,BITS-Pilani, 2003.



MACHINE LEARNINGELECTIVE – I

UNIT IINTRODUCTION - Well-posed learning problems, Designing a learning system, Perspectives andissues in machine learning

Concept learning and the general to specific ordering – Introduction, A concept learning task,Concept learning as search, Find-S: finding a maximally specific hypothesis, Version spaces and thecandidate elimination algorithm, Remarks on version spaces and candidate elimination, Inductive bias

UNIT IIDecision Tree learning – Introduction, Decision tree representation, Appropriate problems for decisiontree learning, The basic decision tree learning algorithm, Hypothesis space search in decision treelearning, Inductive bias in decision tree learning, Issues in decision tree learningArtificial Neural Networks – Introduction, Neural network representation, Appropriate problems forneural network learning, Perceptions, Multilayer networks and the back propagation algorithm,Remarks on the back propagation algorithm, An illustrative example face recognitionAdvanced topics in artificial neural networksEvaluation Hypotheses – Motivation, Estimation hypothesis accuracy, Basics of sampling theory, Ageneral approach for deriving confidence intervals, Difference in error of two hypotheses, Comparinglearning algorithms

UNIT IIIBayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximumlikelihood and least squared error hypotheses, Maximum likelihood hypotheses for predictingprobabilities, Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïvebayes classifier, An example learning to classify text, Bayesian belief networks The EM algorithmComputational learning theory – Introduction, Probability learning an approximately correcthypothesis, Sample complexity for Finite Hypothesis Space, Sample Complexity for infinite HypothesisSpaces, The mistake bound model of learning - Instance-Based Learning- Introduction, k -NearestNeighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning,Remarks on Lazy and Eager LearningGenetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Hypothesis SpaceSearch, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms

UNIT IVLearning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets:Summary, Learning First Order Rules, Learning Sets of First Order Rules: FOIL, Induction as InvertedDeduction, Inverting ResolutionAnalytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks onExplanation-Based Learning, Explanation-Based Learning of Search Control Knowledge

UNIT VCombining Inductive and Analytical Learning – Motivation, Inductive-Analytical Approaches toLearning, Using Prior Knowledge to Initialize the Hypothesis, Using Prior Knowledge to Alter theSearch Objective, Using Prior Knowledge to Augment Search Operators,Reinforcement Learning – Introduction, The Learning Task, Q Learning, Non-Deterministic, Rewardsand Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to DynamicProgramming

TEXT BOOKS:1. Machine Learning – Tom M. Mitchell, - MGH2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor &

Francis(CRC)



SPEECH PROCESSING

ELECTIVE-I

UNIT IINTRODUCTION Production of speech,sound perception, speech Analysis, speech coding,speech Enhancement, speech Synthesis, speech and speaker Recognition.Signals and Linear Systems: Simple signal, Filtering and convolution, Frequency Analysis :Fourier Transform, spectra and Correlation, Laplace Transform: Poles and Zeros, Discrete –Time Signal and Systems: Sampling, Frequency Transforms of Discrete-Time Signals,Decimation and Interpolation Filter: Band pass Filter, Digital Filters, Difference Equations andInterpolationSPEECH ANALYSISIntroduction, Short-Time speech Analysis: Windowing, Spectra of Windows: Wide-andNarrow –Band Spectrograms, Time-domain Parameters: Signal Analysis in the Time Domain,Short –Time Average Energy and Magnitude, Short –Time Average Zero-Crossing Rate (ZCR), short-Time Autocorrelation Function , Frequency–Domain (Spectral) Parameters:Filter–Bank Analysis, Short-Time Fourier Transform Analysis, Spectral Displays, FormantEstimation and Tracking .

UNIT IISPEECH PRODUCTION AND ACOUSTIC PHONETICS :Anatomy and Physiology of the speech Organs: the Lungs and the Thorax, Larynx and VocalFolds(cords), Vocal Tract, Articulatory phonetics: Manner of Atriculatory, Structure of theSyllable, Voicing, Place of the Articulation, Phonemes in Other Language, ArticulatoryModels, Acoustic Phonetics : Spectrograms, Vowels, Diphthongs, glides and Liquids, Nasals,Fricatives, stops (Plosives), Variants of Normal Speech.

UNIT IIILINEAR PREDICTIVE CODING (LPC) ANALYSISBasic Principles of LPC, Least –Squares Autocorrelation Method, Least –Squares CovarianceMethod, Computation Considerations, Spectral Estimation Via LPC, Updating the LPC ModelSample by Sample, Window Considerations.Cepstral Analysis: Mathematical details of Cepstral analysis, Applications for the spectrum,Mel-Scale Cepstrum, F0 Pitch estimation:Time domain F0 estimation methods, short-timeSpectral methods

UNIT IVSpeech synthesis: Introduction, Principles of speech synthsis: Types of strored speech units toconcatenate, Memory size, Synthesis method, Limited text voice response system, unrestricted-text TTS systems. Synthesizer methods: Articulatory synthesis, Formant synthesis, LPCsynthesis.

UNIT VIntroduction: VaN CCriability in speech signals, segmenting speech into smaller units,Performance evaluation, Database for speech recognition,pattern recognition methods,pre=processing, parametric representation: parameters used in speech recognition, featureextraction, Evaluation of similarity of speech patterns: frame-based distance measures, MakingASR decisions, HMMsSpeaker recognition: Introduction, Verification Vs. Recognition, Recognition techniques:Model evaluation, text dependence, statical Vs. dynamic features, stochastic models, vectorquantization, similarity and distance measures, cepstral analysis, Features that distinguish thespeakers: measures of the effectiveness of features, techniques to choose features, spectralfeatures, prosodic features

Text Books:1. Speech Communication Douglas O’ Shaughnessy, Universities Press

Reference Books:

1. Fundamentals of Speech Recognition, Lawrence Rabiner, Biing-Hwang Juang, PearsonEducation

2. Speech and Language processing, Daniel Jurafsky, James H. Martin, Pearson Education



WIRELESS NETWORKS AND MOBILE COMPUTING

ELECTIVE –IIUNIT I : INTRODUCTION TO MOBILE AND WIRELESS LANDSCAPEDefinition of Mobile and Wireless, Components of Wireless Environment, ChallengesOverview of Wireless Networks, Categories of Wireless NetworksWireless LAN : Infra red Vs radio transmission, Infrastructure and Ad-hoc Network, IEEE 802.11,HIPERLAN, BluetoothGLOBAL SYSTEM FOR MOBILE COMMUNICATIONS(GSM)GSM Architecture, GSM Entities, Call Routing in GSM, PLMN Interfaces, GSM Addresses andIdentifiers, Network Aspects in GSM, GSM Frequency Allocation, Authentication and SecurityUNIT II: MOBILE NETWORK LAYERMobile IP (Goals, assumptions, entities and terminology, IP packet delivery, agent advertisement anddiscovery, registration, tunneling and encapsulation, optimizations), Dynamic Host ConfigurationProtocol (DHCP), Mobile Ad-hoc networks : Routing, destination Sequence Distance Vector, DynamicSource Routing.MOBILE TRANSPORT LAYERTraditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, Fast retransmit/fast recovery,Transmission /time-out freezing, Selective retransmission, Transaction oriented TCP.UNIT III: BROADCAST SYSTEMSOverview, Cyclical repetition of data, Digital audio broadcasting: Multimedia object transfer protocol,Digital video broadcasting: DVB data broadcasting, DVB for high-speed internet access, Convergenceof broadcasting and mobile communications.UNIT IV : PROTOCOLS AND TOOLS:Wireless Application Protocol-WAP. (Introduction, protocol architecture, and treatment of protocols ofall layers), Bluetooth (User scenarios, physical layer, MAC layer, networking, security, linkmanagement) and J2ME.WIRELESS LANGUAGE AND CONTENT – GENERATION TECHNOLOGIESWireless Content Types, Markup Languages: HDML, WML, HTML, cHTML, XHTML, VoiceXML.Content- Generation Technologies: CGI with Perl, Java Servlets, Java Server Pages, Active ServerPages, XML with XSL Stylesheets, XML Document, XSL StylesheetUNIT V: MOBILE AND WIRELESS SECURITYCreating a Secure Environment, Security Threats, Security Technologies, Other Security Measures,WAP Security, Smart Client Security

TEXT BOOKS:1. Jochen Schiller, “Mobile Communications”, Pearson Education, Second Edition, 2008.2. Martyn Mallick, “Mobile and Wireless Design Essentials”, Wiley, 2008.3. Asoke K Talukder, et al, “Mobile Computing”, Tata McGraw Hill, 2008.

REFERENCE BOOKS:1.Mobile Computing,Raj Kamal,Oxford University Press.2.William Stallings, “ Wireless Communications & Networks”, Person, Second Edition, 2007.3.Frank Adelstein et al, “Fundamentals of Mobile and Pervasive Computing”, TMH, 2005.4.Jim Geier, “Wireless Networks first-step”, Pearson, 2005.5.Sumit Kasera et al, “2.5G Mobile Networks: GPRS and EDGE”, TMH, 2008.6.Matthew S.Gast, “802.11 Wireless Networks”, O’Reilly, Second Edition, 2006.7.Ivan Stojmenovic , “Handbook of Wireless Networks and Mobile Computing”, Wiley, 2007.


STORAGE AREA NETWORKS

ELECTIVE II

UNIT I: Introduction to Storage TechnologyReview data creation and the amount of data being created and understand the value of data to abusiness, challenges in data storage and data management, Solutions available for data storage, Coreelements of a data center infrastructure, role of each element in supporting business activities

UNIT II: Storage Systems ArchitectureHardware and software components of the host environment, Key protocols and concepts used by eachcomponent ,Physical and logical components of a connectivity environment ,Major physicalcomponents of a disk drive and their function, logical constructs of a physical disk, accesscharacteristics, and performance Implications, Concept of RAID and its components , Different RAIDlevels and their suitability for different application environments: RAID 0, RAID 1, RAID 3, RAID 4,RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and contrast integrated and modular storagesystems ,High-level architecture and working of an intelligent storage system

UNIT III: Introduction to Networked StorageEvolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS, and IP-SAN , Benefits of the different networked storage options, Understand the need for long-term archivingsolutions and describe how CAS fulfills the need , Understand the appropriateness of the differentnetworked storage options for different application environments

UNIT IV: Information Availability & Monitoring & Managing DatacenterList reasons for planned/unplanned outages and the impact of downtime, Impact of downtime,Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO and RPO, Identifysingle points of failure in a storage infrastructure and list solutions to mitigate these failures ,Architecture of backup/recovery and the different backup/recovery topologies , replication technologiesand their role in ensuring information availability and business continuity, Remote replicationtechnologies and their role in providing disaster recovery and business continuity capabilitiesIdentify key areas to monitor in a data center, Industry standards for data center monitoring andmanagement, Key metrics to monitor for different components in a storage infrastructure, Keymanagement tasks in a data center

UNIT V: Securing Storage and Storage VirtualizationInformation security, Critical security attributes for information systems, Storage security domains, Listand analyzes the common threats in each domain, Virtualization technologies, block-level and file-levelvirtualization technologies and processesCase StudiesThe technologies described in the course are reinforced with EMC examples of actual solutions.Realistic case studies enable the participant to design the most appropriate solution for given sets ofcriteria.TEXT BOOKS :1. EMC Corporation, Information Storage and Management, Wiley.2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill , Osborne,

2003.3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001.4. Meeta Gupta, Storage Area Network Fundamentals, Pearson Education Limited, 2002.

Reference Books:1. EMC Corporation, Information Storage and Management, Wiley,

2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill , Osborne,2003.

3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001.

4. Meeta Gupta, Storage Area Network Fundamentals, Pearson Education Limited, 2002.


CLOUD COMPUTING

ELECTIVE - II

UNIT – IIntroduction to virtualization and virtual machine, Virtualization in cluster/grid contextVirtual network, Information model & data model for virtual machine, Software as a Service(SaaS), SOA, On Demand Computing.

UNIT – IICloud computing: Introduction, What it is and What it isn’t, from Collaborations to Cloud,

Cloud application architectures, Value of cloud computing, Cloud Infrastructure models,Scaling a Cloud Infrastructure, Capacity Planning, Cloud Scale.

UNIT – IIIData Center to Cloud: Move into the Cloud, Know Your Software Licenses, The Shift to aCloud Cost Model, Service Levels for Cloud ApplicationsSecurity: Disaster Recovery, Web Application Design, Machine Image Design, PrivacyDesign,Database Management, Data Security, Network Security, Host Security, CompromiseResponse

UNIT – IVDefining Clouds for the Enterprise- Storage-as-a-Service, Database-as-a-Service, Information-as-a-Service, Process-as-a-Service, Application-as-a-Service, Platform-as-a-Service,Integration-as-a-Service, Security-as-a-Service, Management/Governance-as-a-Service,Testing-as-a-Service, Infrastructure-as-a-Service

UNIT – VDisaster Recovery, Disaster Recovery, Planning, Cloud Disaster ManagementCase study: Types of Clouds, Cloudcentres in detail, Comparing approaches, XenOpenNEbula , Eucalyptus, Amazon, Nimbus

Text Books:1. Cloud Computing – Web Based Applications That Change the way you Work and

Collaborate Online – Michael Miller, Pearson Education.2. Cloud Application Architectures, 1st Edition by George Reese O'Reilly Media.

Reference Book:1. Cloud Computing and SOA Convergence in Your Enterprise: A Step-by-Step Guide

David S. Linthicum Addison-Wesley Professional.2. Enterprise Web 2.0 Fundamentals by Krishna Sankar; Susan A. Bouchard, Cisco

Press

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M.TECH ... CSE bran… · JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD M.Tech (NE URAL NETWORKS) I SEMESTER ARTIFICIAL INTELLIGENCE