CP7031 COMPILER OPTIMIZATION TECHNIQUES

CP7031       COMPILER OPTIMIZATION TECHNIQUES

UNIT I          INTRODUCTION

Language Processors - The Structure of a Compiler – The Evolution of Programming Languages- The Science of Building a Compiler – Applications of Compiler Technology Programming Language Basics - The Lexical Analyzer Generator -Parser Generator - Overview of Basic Blocks and Flow Graphs - Optimization of Basic Blocks - Principle Sources of Optimization.  

UNIT II          INSTRUCTION-LEVEL PARALLELISM 

Processor Architectures – Code-Scheduling Constraints – Basic-Block Scheduling –Global Code Scheduling – Software Pipelining. 

UNIT III        OPTIMIZING FOR PARALLELISM AND LOCALITY-THEORY

Basic Concepts – Matrix-Multiply: An Example - Iteration Spaces - Affine Array Indexes – Data Reuse Array data dependence Analysis.  

UNITIV        OPTIMIZING FOR PARALLELISM AND LOCALITY – APPLICATION  

Finding Synchronization - Free Parallelism – Synchronization Between Parallel Loops – Pipelining – Locality Optimizations – Other Uses of Affine Transforms. 

UNIT V         INTERPROCEDURAL ANALYSIS

Basic Concepts – Need for Interprocedural Analysis – A Logical Representation of Data Flow – A Simple Pointer-Analysis Algorithm – Context Insensitive Interprocedural Analysis - ContextSensitive Pointer-Analysis - Datalog Implementation by Binary Decision Diagrams.

REFERENCES: 

1. Alfred V. Aho, Monica S.Lam, Ravi Sethi, Jeffrey D.Ullman, “Compilers:Principles,              Techniques and Tools”, Second Edition, Pearson Education,2008. 

2.    Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern Architectures: A                Dependence-based Approach”, Morgan Kaufmann Publishers, 2002. 

3. Steven S. Muchnick, “Advanced Compiler Design and Implementation”,Morgan      Kaufmann Publishers - Elsevier Science, India, Indian Reprint 2003. 

CP7030 ROBOTICS

CP7030      ROBOTICS

UNIT I           LOCOMOTION AND KINEMATICS

Introduction to Robotics – key issues in robot locomotion – legged robots – wheeled mobile robots – aerial mobile robots – introduction to kinematics – kinematics models and constraints – robot maneuverability  

UNIT II         ROBOT PERCEPTION

Sensors for mobile robots – vision for robotics – cameras – image formation – structure from stereo – structure from motion – optical flow – color tracking – place recognition – range data  

UNIT III          MOBILE ROBOT LOCALIZATION

Introduction to localization – challenges in localization – localization and navigation – belief representation – map representation – probabilistic map-based localization – Markov localization – EKF localization – UKF localization – Grid localization – Monte Carlo localization – localization in dynamic environments  

UNIT IV        MOBILE ROBOT MAPPING

Autonomous map building – occupancy grip mapping – MAP occupancy mapping – SLAM – extended Kalman Filter SLAM – graph-based SLAM – particle filter SLAM – sparse extended information filter – fastSLAM algorithm   

UNIT V         PLANNING AND NAVIGATION  

 Introduction to planning and navigation – planning and reacting – path planning – obstacle avoidance techniques – navigation architectures – basic exploration algorithms  

REFERENCES: 

1. Roland Seigwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, “Introduction to autonomous mobile robots”, Second Edition, MIT Press, 2011. 

2. Sebastian Thrun, Wolfram Burgard, and Dieter Fox, “Probabilistic Robotics”, MIT Press, 2005. 

3. Howie Choset et al., “Principles of Robot Motion: Theory, Algorithms, and Implementations”, A Bradford Book, 2005. 

4. Gregory Dudek and Michael Jenkin, “Computational Principles of Mobile Robotics”, Second Edition, Cambridge University Press, 2010. 

5. Maja J. Mataric, “The Robotics Primer”, MIT Press, 2007.   

CP7029 INFORMATION STORAGE MANAGEMENT

CP7029         INFORMATION STORAGE MANAGEMENT

UNIT I         INTRODUCTION TO STORAGE TECHNOLOGY 

Review data creation and the amount of data being created and understand the value of data to a business, challenges in data storage and data management, Solutions available for data storage, Core elements of a data center infrastructure, role of each element in supporting business activities 

UNIT II         STORAGE SYSTEMS ARCHITECTURE

Hardware and software components of the host environment, Key protocols and concepts used by each component ,Physical and logical components of a connectivity environment ,Major physical components of a disk drive and their function, logical constructs of a physical disk, access characteristics, and performance Implications, Concept of RAID and its components, Different RAID levels 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 storage systems ,Iligh-level architecture and working of an intelligent storage system  

UNIT III      INTRODUCTION TO NETWORKED STORAGE

Evolution 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 archiving solutions and describe how CAS full fill the need, understand the appropriateness of the different networked storage options for different application environments 

UNIT IV        INFORMATION AVAILABILITY, MONITORING & MANAGING                       DATACENTER

List 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, Identify single 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 technologies and their role in ensuring information availability and business continuity, Remote replication technologies and their role in providing disaster recovery and business continuity capabilities. Identify key areas to monitor in a data center, Industry standards for data center monitoring and management, Key metrics to monitor for different components in a storage infrastructure, Key management tasks in a data center 

UNIT V         SECURING STORAGE AND STORAGE VIRTUALIZATION

Information security, Critical security attributes for information systems, Storage security domains, List and analyzes the common threats in each domain, Virtualization technologies, block-level and file-level virtualization technologies and processes 

REFERENCE BOOKS: 

1.  EMC Corporation, Information Storage and Management, Wiley, India. 

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. Additional resource material on www.emc.com/resource-library/resource-library.esp 

CP7028 ENTERPRISE APPLICATION INTEGRATION

CP7028   ENTERPRISE APPLICATION INTEGRATION

UNIT I            INTRODUCTION

Requirements for  EAI - Challenges in EAI – Integration with legacy systems – Integration with partners - Heterogeneous environment – Implementation approaches – Web services, messaging, ETL, direct data integration – Middleware requirements – Approaches to integration – services oriented and messaging. 

UNIT II          INTEGRATION PATTERNS

Introduction to integration patterns – Architecture for application integration – Integration patterns – Point to point, broker, message bus, publish/subscribe, Challenges in performance, security, reliability - Case studies 

UNIT III         SERVICE ORIENTED INTEGRATION

Business process integration - Composite applications-services – Web services – Service choreography and orchestration - Business process modeling - BPMN, Business process execution - BPEL – Middleware infrastructure - Case studies

UNIT IV          MESSAGING BASED INTEGRATION

 Messaging – Synchronous and asynchronous – Message structure – Message oriented middleware – Reliability mechanisms – Challenges – Messaging infrastructure – Java Messaging Services – Case studies  

UNIT V           ENTERPRISE SERVICE BUS

Enterprise Service Bus – routing, scalable connectivity, protocol and message transformations, data enrichment, distribution, correlation, monitoring – Deployment configurations – Global ESB, Directly connected, Federated, brokered ESBs – Application server based – Messaging system based – Hardware based ESBs – Support to SOA, message based and event based integrations - Case studies.

REFERENCES: 

1. George Mentzas and Andreas Frezen (Eds), "Semantic Enterprise Application Integration for Business Processes: Service-oriented Frameworks", Business Science Reference, 2009 

2. Waseem Roshen, "SOA Based Enterprise Integration", Tata McGrawHill, 2009. 

3. G Hohpe and B Woolf, "Enterprise Integration Patterns:Designing, Building, and Deploying Messaging Solutions",AddisonWesley Professional, 2003 

4. D Linthicum, "Next Generation Application Integration: From Simple Information to WebServices",AddisonWesley, 2003 

5. Martin Fowler, "Patterns of Enterprise Application Architecture", Addison- Wesley, 2003 

6. Kapil Pant and Matiaz Juric, "Business Process Driven SOA using BPMN and BPEL: From Business Process Modeling to Orchestration and Service Oriented Architecture", Packt Publishing, 2008  

 

CP7027 MULTI OBJECTIVE OPTIMIZATION TECHNIQUES

CP7027      MULTI OBJECTIVE OPTIMIZATION TECHNIQUES

UNIT I             INTRODUCTION AND CLASSICAL APPROACHES

Multiobjective optimization: Introduction - Multiobjective optimization problem-principles – Difference between single and multiobjective optimization – Dominance and Pareto Optimality , Classical Methods – Weighted Sum -   Constraint method – Weighted Metric methods – Benson’s method -  Value Function -  Goal Programming methods – Interactive Methods

  

UNIT II           MOP EVOLUTIONARY ALGORITHMS

Generic MOEA - Various MOEAs: MOGA, NSGA-II, NPGA, PAES, SPEA2, MOMGA, micro GA - Constrained MOEAs: Penalty Function approach - Constrained Tournament – Ray – Tai –Seow’s Method.  

UNIT III         THEORETICAL ISSUES

Fitness Landscapes - Fitness Functions - Pareto Ranking  - Pareto Niching and Fitness Sharing - Recombination Operators -  Mating Restriction - Solution Stability and Robustness -  MOEA Complexity - MOEA Scalability - Running Time Analysis - MOEA Computational Cost - No Free Lunch Theorem.

   

UNIT IV      MOEA TESTING, ANALYSIS, AND PARALLELIZATION

MOEA Experimental Measurements – MOEA Statistical Testing Approaches – MOEA Test Suites - MOEA Parallelization: Background – Paradigms – Issues - MOEA Local Search Techniques.  

UNIT V         APPLICATIONS AND ALTERNATIVE METAHEURISTICS

Scientific Applications: Computer Science and Computer Engineering - Alternative Metaheuristics: Simulated Annealing – Tabu Search and Scatter Search – Ant System – Distributed Reinforcement Learning – Particle Swarm Optimization – Differential Evolution – Artificial Immune Systems - Other Heuristics.

REFERENCES: 

1. Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen,   “Evolutionary Algorithms for Solving Multi-objective Problems”, Second Edition,  Springer, 2007. 

2. Kalyanmoy Deb, “ Multi-Objective Optimization Using Evolutionary Algorithms”, John Wiley, 2002. 

3. Aimin Zhoua, Bo-Yang Qub, Hui Li c, Shi-Zheng Zhaob, Ponnuthurai Nagaratnam Suganthan b, Qingfu Zhangd, “Multiobjective evolutionary algorithms: A survey of the state of the art”, Swarm and Evolutionary Computation  (2011) 32–49. 

4. E Alba, M Tomassini, “Parallel and evolutionary algorithms”, Evolutionary Computation, IEEE Transactions on 6 (5), 443-462. 

5. Crina Grosan, Ajith Abraham, “Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews”, Studies in Computational Intelligence, Vol. 75, Springer, 2007. 

6. Christian Blum and Andrea Roli. 2003. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35, 3 (September 2003), 268308. 

  

CP7026 SOFTWARE QUALITY ASSURANCE

CP7026   SOFTWARE QUALITY ASSURANCE

UNIT I             INTRODUCTION

Introduction – Views on quality – Cost of quality - Quality models – Quality frameworks – Verification and Validation – Defect taxonomy – Defect management – Statistics and measurements – IEEE standards – Quality assurance and control processes  

UNIT II             VERIFICATION

Introduction – Verification techniques – Inspections, reviews, walk-throughs – Case studies

  

UNIT III           TEST GENERATION

Software testing- Validation – Test plan  – Test cases - Test Generation – Equivalence partitioning – Boundary value analysis – Category partition method – Combinatorial generation - Decision tables – Examples and Case studies  

UNIT IV         STRUCTURAL TESTING

Introduction – Test adequacy criteria – Control flow graph – Coverages: block, conditions, multiple conditions, MC/DC, path – Data flow graph – Definition and use coverages – C-use, P-use, Defclear, Def-use – Finite state machines – Transition coverage – Fault based testing – Mutation analysis – Case studies  

UNIT V           FUNCTIONAL TESTING

Introduction – Test adequacy criteria - Test cases from use cases – Exploratory testing - Integration, system, acceptance, regression testing – Testing for specific attributes: Performance, load and stress testing – Usability testing – Security testing - Test automation – Test oracles  

REFERENCES: 

1. Boriz Beizer, "Software Testing Techniques", 2nd Edition, DreamTech, 2009. 

2. Aditya P. Mathur, "Foundations of Software Testing", Pearson, 2008 

3. Mauro Pezze and Michal Young,  "Software Testing and Analysis. Process, Principles, and Techniques", John Wiley 2008 

4. Stephen H. Kan, "Metrics and Models in Software Quality Engineering", 2nd Edition, Pearson, 2003 

5. Kshirasagar Naik and Priyadarshi Tripathy (Eds), "Software Testing and Quality Assurance: Theory and Practice", John Wiley, 2008 

6. "Combinatorial Methods in Software Testing", ttp://csrc.nist.gov/groups/SNS/acts/index.html 

IF7002 BIO INFORMATICS

IF7002        BIO INFORMATICS

UNIT I   INTRODUCTION

Need for Bioinformatics technologies – Overview of Bioinformatics technologies – Structural bioinformatics – Data format and processing – secondary resources- Applications – Role of Structural bioinformatics - Biological Data Integration System.  

UNIT II           DATAWAREHOUSING AND DATAMINING IN BIOINFORMATICS

Bioinformatics data – Data ware housing architecture – data quality – Biomedical data analysis – DNA data analysis – Protein data analysis – Machine learning – Neural network architecture- Applications in bioinformatics  

UNIT III           MODELING FOR BIOINFORMATICS

Hidden markov modeling for biological data analysis – Sequence identification – Sequence classification – multiple alignment generation – Comparative modeling – Protein modeling – genomic modeling – Probabilistic modeling – Bayesian networks – Boolean networks - Molecular modeling – Computer programs for molecular modeling  

UNIT IV          PATTERN MATCHING AND VISUALIZATION

Gene regulation – motif recognition and  motif detection – strategies for motif detection – Visualization – Fractal analysis – DNA walk models – one dimension – two dimension – higher dimension – Game representation of Biological sequences – DNA, Protein, Amino acid sequences 

UNIT V MICROARRAY ANALYSIS

Microarray technology for genome expression study – image analysis for data extraction – preprocessing – segmentation – gridding , spot extraction , normalization, filtering – cluster analysis – gene network analysis – Compared Evaluation of Scientific Data Management Systems – Cost Matrix – Evaluation model ,Benchmark , Tradeoffs      

REFERENCES: 

1. Yi-Ping Phoebe Chen (Ed), “Bio Informatics Technologies”, First Indian Reprint, Springer Verlag, 2007. 

2. N.J. Chikhale and Virendra Gomase, "Bioinformatics- Theory and Practice", Himalaya Publication House, India, 2007 

3. Zoe lacroix and Terence Critchlow, “Bio Informatics – Managing Scientific data”, First Indian Reprint, Elsevier, 2004 

4. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003. 

5. Arthur M Lesk, “Introduction to Bioinformatics”, Second Edition, Oxford University Press, 2005 6. Burton. E. Tropp, “Molecular Biology: Genes to Proteins “, 4th edition, Jones and Bartlett Publishers, 2011 

7. Dan Gusfield, “Algorithms on Strings Trees and Sequences”, Cambridge University Press, 1997. 

8. P. Baldi, S Brunak , Bioinformatics, “A Machine Learning Approach “, MIT Press, 1998. 

CP7025 DATA MINING TECHNIQUES

CP7025      DATA MINING TECHNIQUES 

UNIT I            INTRODUCTION TO DATA MINING

Introduction to Data Mining – Data Mining Tasks – Components of Data Mining Algorithms – Data Mining supporting Techniques – Major Issues in Data Mining – Measurement and Data – Data Preprocessing – Data sets  

UNIT II         OVERVIEW OF DATA MINING ALGORITHMS

Overview of Data Mining Algorithms – Models and Patterns – Introduction – The Reductionist viewpoint on Data Mining Algorithms – Score function for Data Mining Algorithms- Introduction – Fundamentals of Modeling – Model Structures for Prediction – Models for probability Distributions and Density functions – The Curve of Dimensionality – Models for Structured Data – Scoring Patterns – Predictive versus Descriptive score functions – Scoring Models with Different Complexities – Evaluation of Models and Patterns – Robust Methods. 

 UNIT III        CLASSIFICATIONS

Classifications – Basic Concepts – Decision Tree induction – Bayes Classification Methods – Rule Based Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Classification: Advanced concepts – Bayesian Belief Networks- Classification by Back Propagation – Support Vector Machine – Classification using frequent patterns.    

UNIT IV           CLUSTER ANALYSIS

Cluster Analysis: Basic concepts and Methods – Cluster Analysis – Partitioning methods – Hierarchical methods – Density Based Methods – Grid Based Methods – Evaluation of Clustering – Advanced Cluster Analysis: Probabilistic model based clustering – Clustering High – Dimensional Data – Clustering Graph and Network Data – Clustering with Constraints.   

UNIT V       ASSOCIATION RULE MINING AND VISUALIZATION

Association Rule Mining – Introduction – Large Item sets – Basic Algorithms – Parallel and Distributed Algorithms – Comparing Approaches – Incremental Rules – Advanced Association Rule Techniques – Measuring the Quality of Rules – Visualization of Multidimensional Data – Diagrams for Multidimensional visualization – Visual Data Mining – Data Mining Applications – Case Study: 

REFERENCE S: 

1. Jiawei Han, Micheline Kamber , Jian Pei, “Data Mining: Concepts and Techniques”, Third Edition (The Morgan Kaufmann Series in Data Management Systems), 2012. 

2. David J. Hand, Heikki Mannila and Padhraic Smyth “Principles of Data Mining” (Adaptive Computation and Machine Learning), 2005 

3. Margaret H Dunham, “Data Mining: Introductory and Advanced Topics”, 2003 

4. Soman, K. P., Diwakar Shyam and Ajay V. “Insight Into Data Mining: Theory And Practice”, PHI, 2009. 

CP7024 INFORMATION RETRIEVAL TECHNIQUES

CP7024      INFORMATION RETRIEVAL TECHNIQUES 

UNIT I        INTRODUCTION

Motivation – Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web Characteristics–The impact of the web on IR  ––IR Versus Web Search–Components of a Search engine                 

UNIT II         MODELING

Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing                   

UNIT III      INDEXING

Static and Dynamic Inverted Indices – Index Construction and Index Compression Searching - Sequential Searching and Pattern Matching.  Query Operations -Query Languages–Query Processing - Relevance Feedback and Query Expansion - Automatic Local and Global Analysis – Measuring Effectiveness and Efficiency.    

UNIT IV         CLASSIFICATION AND CLUSTERING

Text Classification and Naïve Bayes – Vector Space Classification – Support vector machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –Matrix decompositions and latent semantic indexing – Fusion and Meta learning  

UNIT V          SEARCHING AND RANKING

Searching the Web –Structure of the Web –IR and web search – Static and Dynamic Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR: Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital Libraries                                                      

REFERENCES: 

1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The concepts and Technology behind Search (ACM Press Books), Second Edition 2011 

2. Christopher D. Manning, PrabhakarRaghavan, HinrichSchutze, Introduction to Information Retrieval, Cambridge University Press, First South Asian Edition 2012 

3. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval Implementing and Evaluating Search Engines, The MIT Press, Cambridge, Massachusetts London, England, 2010

 

IF7013 ENERGY AWARE COMPUTING

IF7013   ENERGY AWARE COMPUTING

UNIT I   INTRODUCTION   

Energy efficient network on chip architecture for multi core system-Energy efficient MIPS CPU core with fine grained run time power gating – Low power design of Emerging memory technologies.  

UNIT II  ENERGY EFFICIENT STORAGE 

Disk Energy Management-Power efficient strategies for storage system-Dynamic thermal management for high performance storage systems-Energy saving technique for Disk storage systems  

UNIT III  ENERGY EFFICIENT ALGORITHMS

Scheduling of Parallel Tasks – Task level Dynamic voltage scaling – Speed Scaling – Processor optimization- Memetic Algorithms – Online job scheduling Algorithms.   

UNIT IV  REAL TIME SYSTEMS 

Multi processor system – Real Time tasks- Energy Minimization – Energy aware scheduling- Dynamic Reconfiguration- Adaptive power management-Energy Harvesting Embedded system   

UNIT V           ENERGY AWARE APPLICATIONS                                   9 On chip network – Video codec Design – Surveillance camera- Low power mobile storage.   

REFERENCES: 

1. Ishfaq Ah mad, Sanjay Ranka, Handbook of Energy Aware and Green Computing, Chapman and Hall/CRC, 2012 

2. Chong-Min Kyung,  Sungioo  yoo, Energy Aware system design Algorithms and Architecture, Springer, 2011. 

3. Bob steiger wald ,Chris:Luero, Energy Aware computing, Intel Press,2012. 

CP7023 RECONFIGURABLE COMPUTING

CP7023    RECONFIGURABLE COMPUTING

UNIT I          DEVICE ARCHITECTURE

 General Purpose Computing Vs Reconfigurable Computing – Simple Programmable Logic Devices – Complex Programmable Logic Devices – FPGAs – Device Architecture - Case Studies. 

UNIT II        RECONFIGURABLE COMPUTING ARCHITECTURES AND SYSTEMS

Reconfigurable Processing Fabric Architectures – RPF Integration into Traditional Computing Systems – Reconfigurable Computing Systems – Case Studies – Reconfiguration Management. 

UNIT III       PROGRAMMING RECONFIGURABLE SYSTEMS

Compute Models - Programming FPGA Applications in HDL – Compiling C for Spatial Computing – Operating System Support for Reconfigurable Computing.  

UNIT IV       MAPPING DESIGNS TO RECONFIGURABLE PLATFORMS

The Design Flow - Technology Mapping – FPGA Placement and Routing – Configuration Bitstream Generation – Case Studies with Appropriate Tools.   

UNIT V       APPLICATION DEVELOPMENT WITH FPGAS

Case Studies of FPGA Applications – System on a Programmable Chip (SoPC) Designs.         

REFERENCES: 

1. Maya B. Gokhale and Paul S. Graham, “Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays”, Springer, 2005. 

2. Scott Hauck and Andre Dehon (Eds.), “Reconfigurable Computing – The Theory and Practice of FPGA-Based Computation”, Elsevier / Morgan Kaufmann, 2008. 

3. Christophe Bobda, “Introduction to Reconfigurable Computing – Architectures, Algorithms and Applications”, Springer, 2010.   

CP7022 SOFTWARE DESIGN

CP7022   SOFTWARE DESIGN

UNIT I           SOFTWARE DESIGN PRINCIPLES 

Introduction – Design process – Managing complexity – Software modeling and notations – Abstraction – Modularity – Hierarchy – Coupling - Cohesion – Design guidelines and checklists – Refactoring  

UNIT II          OO DESIGN

Object model – Classes and objects  – Object oriented analysis – Key abstractions and  mechanisms – Object oriented design – Identifying design elements – Detailed design – Case studies.   

UNIT III        DESIGN PATTERNS

Introduction to patterns – Design context – Reusable solutions – Documenting reusable solutions – Standard patterns from GOF book.

  

UNIT IV        FUNCTION AND SERVICE ORIENTED DESIGNS

Structural decomposition – Detailed Design – Function oriented design Case study – Services – Service identification – Service design – Service composition – choreography and orchestration – Service oriented design Case study   

UNIT V         USER CENTERED DESIGN AND DESIGN REVIEW

Introduction to user centered design – Use in context – Interface and interaction – User centered design principles – Task analysis – Evaluation – Introduction to design review– Testing the design – Walk throughs – Review against check lists.  

REFERENCES:  

1. Grady Booch et al., "Object Oriented Analysis and Design with Applications", 3rd Edition, Pearson, 2010. 

2. Carlos Otero, "Software Engineering Design: Theory and Practice", CRC Press, 2012 

3. David Budgen, "Software Design", 2nd Edtion, Addison Wesley, 2003 

4. Alan Shalloway and James R Trott, "Design Patterns Explained: A New Perspective on Object-Oriented Design", 2nd Edition, Addison-Wesley Professional, 2004 

5. Hassan Gomaa, "Software Modeling and Design", Cambridge University Press, 2011 

6. Eric Gamma et al., "Design Patterns: Elements of Reusable Object-Oriented Software", Addison-Wesley Professional, 1994 

7. Ian Sommerville, "Software Engineering", 9th Edition, Addison-Wesley, 2010 8. M B Rosson and J M Carroll, "Usability Engineering: Scenario-Based Development of Human-Computer Interaction", Morgan Kaufmann, 2002   

CP7021 MEDICAL IMAGE PROCESSING

CP7021     MEDICAL IMAGE PROCESSING

UNIT I  INTRODUCTION

Introduction to medical imaging technology, systems, and modalities. Brief history; importance; applications; trends; challenges. Medical Image Formation Principles: X-Ray physics; X-Ray generation, attenuation, scattering; dose Basic principles of CT; reconstruction methods; artifacts; CT hardware. 

UNIT II            STORAGE AND PROCESSING

 Medical Image Storage, Archiving and Communication Systems and Formats Picture archiving and communication system (PACS); Formats: DICOM Radiology Information Systems (RIS) and Hospital Information Systems (HIS). Medical Image Processing, Enhancement, Filtering Basic image processing algorithms Thresholding; contrast enhancement; SNR characteristics; filtering; histogram modeling.    

UNIT III VISUALIZATION

Medical Image Visualization Fundamentals of visualization; surface and volume rendering/visualization; animation; interaction. Magnetic Resonance Imaging (MRI) Mathematics of MR; spin physics; NMR spectroscopy; imaging principles and hardware; image artifacts.

      

UNIT IV           SEGMENTATION AND CLASSIFICATION

Medical Image Segmentation - Histogram-based methods; Region growing and watersheds; Markov Random Field models; active contours; model-based segmentation. Multi-scale segmentation; semi-automated methods; clustering-based methods; classification-based methods; atlas-guided approaches; multi-model segmentation. Medical Image Registration Intensity-based methods; cost functions; optimization techniques.

       

UNIT V  NUCLEAR IMAGING

PET and SPECT Ultrasound Imaging methods; mathematical principles; resolution; noise effect; 3D imaging; positron emission tomography; single photon emission tomography; ultrasound imaging; applications. Medical Image Search and Retrieval Current technology in medical image search, content-based image retrieval, new trends: ontologies. Applications. Other Applications of Medical Imaging Validation, Image Guided Surgery, Image Guided Therapy, Computer Aided Diagnosis/Diagnostic Support Systems.       

  

REFERENCES: 

1. Paul Suetens, "Fundamentals of Medical Imaging", Second Edition, Cambridge University Press, 2009.  

2. J. Michael Fitzpatrick and Milan Sonka, "Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis", SPIE Publications, 2009.  

3. Kayvan Najarian and Robert Splinter, "Biomedical Signal and Image Processing", Second Edition, CRC Press, 2005.  

4. Geoff Dougherty, "Digital Image Processing for Medical Applications", First Edition, Cambridge University Press, 2009.  

5. Jerry L. Prince and Jonathan Links, "Medical Imaging Signals and Systems", First Edition, Prentice Hall, 2005. 

6. John L. Semmlow, "Biosignal and Medical Image Processing", Second Edition, CRC Press, 2008.

   

CP7020 BIO-INSPIRED COMPUTING

CP7020       BIO-INSPIRED COMPUTING

UNIT I         EVOLUTIONARY AND CELLULAR SYSTEMS

Foundations of evolutionary theory – Genotype – artificial evolution – genetic representations – initial population – fitness functions – selection and reproduction – genetic operators – evolutionary measures – evolutionary algorithms – evolutionary electronics – evolutionary algorithm case study Cellular systems – cellular automata – modeling with cellular systems – other cellular systems – computation with cellular systems – artificial life – analysis and synthesis of cellular systems 

UNIT II           NEURAL SYSTEMS

Biological nervous systems – artificial neural networks – neuron models – architecture – signal encoding – synaptic plasticity – unsupervised learning – supervised learning – reinforcement learning – evolution of neural networks – hybrid neural systems – case study 

UNIT III         DEVELOPMENTAL AND IMMUNE SYSTEMS

Rewriting systems – synthesis of developmental systems – evolutionary rewriting systems – evolutionary developmental programs Biological immune systems – lessons for artificial immune systems – algorithms and applications – shape space – negative selection algorithm – clonal selection algorithm - examples  

UNIT IV          BEHAVIORAL SYSTEMS

Behavior is cognitive science – behavior in AI – behavior based robotics – biological inspiration for robots – robots as biological models – robot learning – evolution of behavioral systems – learning in behavioral systems – co-evolution of body and control – towards self reproduction – simulation and reality  

UNIT V        COLLECTIVE SYSTEMS 

Biological self-organization – Particle Swarm Optimization (PSO) – ant colony optimization (ACO) – swarm robotics – co-evolutionary dynamics – artificial evolution of competing systems – artificial evolution of cooperation – case study TOTAL: 45 PERIODS   OUTCOMES: Upon completion of the course, the students will be able to   Implement and apply evolutionary algorithms  Explain cellular automata and artificial life  Implement and apply neural systems  Explain developmental and artificial immune systems  Explain behavioral systems  Implement and apply collective intelligence systems    

REFERENCES:  

1. D. Floreano and C. Mattiussi, "Bio-Inspired Artificial Intelligence", MIT Press, 2008. 

2. F. Neumann and C. Witt, “Bioinspired Computation in combinatorial optimization: Algorithms and their computational complexity”, Springer, 2010.  

3. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010. 

4. D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”, AddisonWesley, 1989.   

5. Simon O. Haykin, “Neural Networks and Learning Machines”, Third Edition, Prentice Hall, 2008. 6. M. Dorigo and T. Stutzle, “Ant Colony Optimization”, A Bradford Book, 2004. 

7. R. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001. 

NE7011 MOBILE APPLICATION DEVELOPMENT

NE7011      MOBILE APPLICATION DEVELOPMENT

UNIT I             INTRODUCTION

Introduction to mobile applications – Embedded systems -  Market and business drivers for mobile applications – Publishing and delivery of mobile applications – Requirements gathering and validation for mobile applications   

UNIT II            BASIC DESIGN  

Introduction – Basics of embedded systems design – Embedded OS - Design constraints for mobile applications, both hardware and software related  – Architecting mobile applications – User interfaces for mobile applications – touch events and gestures – Achieving quality constraints – performance, usability, security, availability and modifiability.

   

UNIT III           ADVANCED DESIGN

Designing applications with multimedia and web access capabilities – Integration with GPS and social media networking applications – Accessing applications hosted in a cloud computing environment – Design patterns for mobile applications.   

UNIT IV          TECHNOLOGY I - ANDROID

Introduction – Establishing the development environment – Android architecture  – Activities and views – Interacting with UI – Persisting data using SQLite – Packaging and deployment – Interaction with server side applications – Using Google Maps, GPS and Wifi – Integration with social media applications.

  

UNIT V          TECHNOLOGY II - IOS

Introduction to Objective C – iOS features – UI implementation – Touch frameworks – Data persistence using Core Data and SQLite – Location aware applications using Core Location and Map Kit – Integrating calendar and address book with social media application – Using Wifi - iPhone marketplace.  T

REFERENCES: 

1. http://developer.android.com/develop/index.html 

2. Jeff McWherter and Scott Gowell, "Professional Mobile Application Development",  Wrox, 2012 3. Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”, DreamTech, 2012 

4. James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2012 

5. David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 

6 Development: Exploring the iOS SDK”, Apress, 2013. 

CP7019 MANAGING BIG DATA

CP7019         MANAGING BIG DATA

UNIT I            UNDERSTANDING BIG DATA

What is big data – why big data – convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data – big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics 

  

UNIT II         NOSQL DATA MANAGEMENT

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships – graph databases – schemaless databases – materialized views – distribution models – sharding – master-slave replication – peer-peer replication – sharding and replication – consistency – relaxing consistency – version stamps – map-reduce – partitioning and combining – composing map-reduce calculations   

UNIT III         BASICS OF HADOOP

Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures   

UNIT IV       MAPREDUCE APPLICATIONS 

 MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input formats – output formats 

  

UNIT V        HADOOP RELATED TOOLS

Hbase – data model and implementations – Hbase clients – Hbase examples – praxis.Cassandra – cassandra data model – cassandra examples – cassandra clients – Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries. 

REFERENCES: 

1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013. 

2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence", Addison-Wesley Professional, 2012. 

3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012. 

4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012. 

5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012. 

6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011. 

7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010. 8. Alan Gates, "Programming Pig", O'Reilley, 2011.

NE7012 SOCIAL NETWORK ANALYSIS

NE7012        SOCIAL NETWORK  ANALYSIS

UNIT I            INTRODUCTION

Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks - Blogs and online communities - Web-based networks  

UNIT II            MODELING AND VISUALIZATION

Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation - Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce - Ontological representation of social individuals and relationships.  

UNIT III           MINING COMMUNITIES

Aggregating and reasoning with social network data, Advanced Representations - Extracting evolution of Web Community from a Series of Web Archive - Detecting Communities in Social Networks - Evaluating Communities – Core Methods for Community Detection & Mining - Applications of Community Mining Algorithms - Node Classification in Social Networks.   

UNIT IV        EVOLUTION

Evolution in Social Networks – Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for Social Influence Analysis - Influence Related Statistics - Social Similarity and Influence - Influence Maximization in Viral Marketing - Algorithms and Systems for Expert Location in Social Networks - Expert Location without Graph Constraints - with Score Propagation – Expert Team Formation - Link Prediction in Social Networks - Feature based Link Prediction - Bayesian Probabilistic Models - Probabilistic Relational Models  

UNIT V        TEXT AND OPINION MINING

Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering - Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis - Product review mining – Review Classification – Tracking sentiments towards topics over time  

REFERENCES: 

1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011   

2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007. 

3. Borko Furht, “Handbook of Social Network Technologies and Applications”, Springer, 1st edition, 2010. 

4. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and applications”, Springer, 1st edition, 2011. 

5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”, Springer, 2010. 

6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social Network Analysis: Trends, Tools and Research Advances”, Springer, 2009. 

7. Toby Segaran, “Programming Collective Intelligence”, O’Reilly, 2012 

  

CP7018 LANGUAGE TECHNOLOGIES

CP7018              LANGUAGE TECHNOLOGIES

UNIT I          INTRODUCTION

Natural Language Processing – Mathematical Foundations – Elementary Probability Theory – Essential information Theory - Linguistics Essentials  - Parts of Speech and Morphology – Phrase Structure – Semantics – Corpus Based Work.   

UNIT II         WORDS

Collocations – Statistical Inference – n-gram Models – Word Sense Disambiguation – Lexical Acquisition.   

  

UNIT III        GRAMMAR

Markov Models – Part-of-Speech Tagging – Probabilistic Context Free Grammars - Parsing.   

UNIT IV           INFORMATION RETRIEVAL

Information Retrieval Architecture – Indexing - Storage – Compression Techniques – Retrieval Approaches – Evaluation - Search Engines - Commercial Search Engine Features – Comparison - Performance Measures – Document Processing - NLP based Information Retrieval – Information Extraction.  

UNIT V          TEXT MINING

Categorization – Extraction Based Categorization – Clustering - Hierarchical Clustering - Document Classification and Routing - Finding and Organizing Answers from Text Search –  Text Categorization and Efficient Summarization using Lexical Chains – Machine Translation - Transfer Metaphor - Interlingual and Statistical Approaches.        

REFERENCES: 

1. Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural Language Processing “, MIT Press, 1999. 

2. Daniel Jurafsky and James H. Martin, “ Speech and Language Processing” , Pearson, 2008. 

3. Ron Cole, J.Mariani, et.al “Survey of the State of the Art in Human Language Technology”, Cambridge University Press, 1997. 

4. Michael W. Berry, “ Survey of Text Mining: Clustering, Classification and Retrieval”, Springer Verlag, 2003. 

  

NE7005 PROTOCOLS AND ARCHITECTURE FOR WIRELESS SENSOR NETWORKS

NE7005     PROTOCOLS AND ARCHITECTURE FOR WIRELESS   SENSOR NETWORKS  

UNIT I     INTRODUCTION AND OVERVIEW OF WIRELESS SENSOR NETWORKS

Background of Sensor Network Technology, Application of Sensor Networks, Challenges for Wireless Sensor Networks, Mobile Adhoc NETworks (MANETs) and Wireless Sensor Networks, Enabling Technologies For Wireless Sensor Networks.  

UNIT II         ARCHITECTURES 

Single-node Architecture, Hardware Components & Design Constraints, Operating Systems and Execution Environments, Introduction to TinyOS and nesC, Network Architecture, Sensor Network Scenarios, Optimization Goals and Figures of Merit, Design Principles for WSNs, Service Interfaces of WSNs, Gateway Concepts.  

UNIT III        DEPLOYMENT AND CONFIGURATION

Localization and Positioning, Coverage and Connectivity, Single-hop and Multi-hop Localization, Self Configuring Localization Systems, Sensor Management Network Protocols: Issues in Designing MAC Protocol for WSNs, Classification of MAC Protocols, S-MAC Protocol, B-MAC Protocol, IEEE 802.15.4 Standard and Zig Bee, Dissemination Protocol for Large Sensor Network.  

UNIT IV       ROUTING PROTOCOLS AND DATA MANIPULATION

Issues in Designing Routing Protocols, Classification of Routing Protocols, Energy-Efficient Routing, Unicast, Broadcast and Multicast, Geographic Routing.  

Data Centric and Content based Routing, Storage and Retrieval in Network, Compression Technologies for WSN, Data Aggregation Technique.  

UNIT V       SENSOR NETWORK PLATFORMS AND TOOLS

Sensor Node Hardware – Berkeley Motes, Programming Challenges, Node-level Software Platforms, Node-level Simulators, State-centric Programming. 

REFERENCES: 

1. Holger Karl & Andreas Willig, “Protocols And Architectures for Wireless Sensor Networks", John Wiley, 2005. 

2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information Processing Approach", Elsevier, 2007.      

3. Raghavendra, Cauligi S, Sivalingam, Krishna M., Zanti Taieb, “Wireless Sensor Network”, Springer 1st Ed. 2004 (ISBN: 978-4020-7883-5). 

4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks- Technology, Protocols, and Applications”, John Wiley, 2007. 

5. N. P. Mahalik, “Sensor Networks and Configuration: Fundamentals, Standards, Platforms, and Applications” Springer Verlag. 

6. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.

CP7017 DATA VISUALIZATION TECHNIQUES

CP7017        DATA VISUALIZATION TECHNIQUES

UNIT I           CORE SKILLS FOR VISUAL ANALYSIS 

Information visualization – effective data analysis – traits of meaningful data – visual perception – making abstract data visible – building blocks of information visualization – analytical interaction – analytical navigation – optimal quantitative scales – reference lines and regions – trellises and crosstabs – multiple concurrent views – focus and context – details on demand – over-plotting reduction – analytical patterns – pattern examples  

UNIT II            TIME-SERIES, RANKING, AND DEVIATION ANALYSIS 

Time-series analysis – time-series patterns – time-series displays – time-series best practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – best practices – deviation analysis – deviation analysis displays – deviation analysis best practices  

UNIT III         DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS 

Distribution analysis – describing distributions – distribution patterns – distribution displays – distribution analysis best practices – correlation analysis – describing correlations – correlation patterns – correlation displays – correlation analysis techniques and best practices – multivariate analysis – multivariate patterns – multivariate displays – multivariate analysis techniques and best practices  

UNIT IV        INFORMATION DASHBOARD DESIGN I 

Information dashboard – categorizing dashboards – typical dashboard data – dashboard design issues and best practices – visual perception – limits of short-term memory – visually encoding data – Gestalt principles – principles of visual perception for dashboard design  

UNIT V        INFORMATION DASHBOARD DESIGN II 

Characteristics of dashboards – key goals in visual design process – dashboard display media – designing dashboards for usability – meaningful organization – maintaining consistency – aesthetics of dashboards – testing for usability – case studies: sales dashboard, CIO dashboard, Telesales dashboard, marketing analysis dashboard  

REFERENCES: 

1. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis", Analytics Press, 2009.  

2. Stephen Few, "Information dashboard design: The effective visual communication of data", O'Reilly, 2006. 

3. Edward R. Tufte, "The visual display of quantitative information", Second Edition, Graphics Press, 2001. 

4. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013. 

5. Ben Fry, "Visualizing data: Exploring and explaining data with the processing environment", O'Reilly, 2008.  

6. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking business intelligence beyond reporting", Wiley, 2010. 

7. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley, 2011.

 

IF7202 CLOUD COMPUTING

IF7202       CLOUD COMPUTING

UNIT I   CLOUD ARCHITECTURE AND MODEL

Technologies for Network-Based System – System Models for Distributed and Cloud Computing – NIST Cloud Computing Reference Architecture. Cloud Models:- Characteristics – Cloud Services – Cloud models (IaaS, PaaS, SaaS) – Public vs Private Cloud –Cloud Solutions - Cloud ecosystem – Service management – Computing on demand.  

UNIT II  VIRTUALIZATION

Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization - Virtualization Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O Devices  - Virtual Clusters and Resource management – Virtualization for Data-center Automation.  

UNIT III CLOUD INFRASTRUCTURE

Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture Development – Design Challenges - Inter Cloud Resource Management – Resource Provisioning and Platform Deployment – Global Exchange of Cloud Resources.  

UNIT IV PROGRAMMING MODEL

Parallel and Distributed Programming Paradigms – MapReduce , Twister and Iterative MapReduce – Hadoop Library from Apache – Mapping Applications - Programming Support - Google App Engine, Amazon AWS -  Cloud Software Environments -Eucalyptus, Open Nebula, OpenStack, Aneka, CloudSim   

UNIT V SECURITY IN THE CLOUD

Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security – Security Governance – Risk Management – Security Monitoring – Security Architecture Design – Data Security – Application Security – Virtual Machine Security - Identity Management and Access Control – Autonomic Security.

REFERENCES: 

1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012. 

2. John W.Rittinghouse and James F.Ransome, “Cloud Computing: Implementation, Management, and Security”, CRC Press, 2010. 

3. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical Approach”, TMH, 2009. 

4. Kumar Saurabh, “ Cloud Computing – insights into New-Era Infrastructure”, Wiley India,2011. 

5. George Reese, “Cloud Application Architectures: Building Applications and Infrastructure in the Cloud” O'Reilly 

6. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and Processes”, Elsevier/Morgan Kaufmann, 2005. 

7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, “Grid and Cloud Computing – A Business Perspective on Technology and Applications”, Springer. 

8. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A comprehensive Guide to Secure Cloud Computing”, Wiley – India, 2010. 

9. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi, ‘Mastering Cloud Computing”, TMGH,2013. 

10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011 

11. Michael Miller, Cloud Computing, Que Publishing,2008 

12. Nick Antonopoulos, Cloud computing, Springer Publications, 2010 

Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From Parallel Processing to the Internet of Things