JntukMaterials R16 Cloud computing materials are available now! Unit 3 is available for download.You can download it from our site jntukmaterials.com. and stay tuned for updates of materials for 4-1 and share them with your friends asap!
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Jntuk R16 Cloud computing materials are available now! Unit 2 is available for download.You can download it from our site jntukmaterials.com. and stay tuned for updates of materials for 4-1 and share them with your friends asap!
Jntuk R16 Cloud computing materials are available now! You can download them from our site jntukmaterials.com. and stay tuned for updates of materials for 4-1 and share them with your friends asap!
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(Elective – 2)
OBJECTIVES:
• The student will learn about the cloud environment, building software systems and
components that scale to millions of users in modern internetcloud concepts capabilities
across the various cloud service models including Iaas, Paas, Saas, and developing cloud
based software applications on top of cloud platforms.
UNIT -I: Systems modeling, Clustering and virtualization
Scalable Computing over the Internet, Technologies for Network based systems, System models
for Distributed and Cloud Computing, Software environments for distributed systems and
clouds, Performance, Security And Energy Efficiency
UNIT- II:Virtual Machines and Virtualization of Clusters and Data Centers
Implementation Levels of Virtualization, Virtualization Structures/ Tools and mechanisms,
Virtualization of CPU, Memory and I/O Devices, Virtual Clusters and Resource Management,
Virtualization for Data Center Automation.
UNIT- III: Cloud Platform Architecture
Cloud Computing and service Models, Architectural Design of Compute and Storage Clouds,
Public Cloud Platforms, Inter Cloud Resource Management, Cloud Security and Trust
Management. Service Oriented Architecture, Message Oriented Middleware.
UNIT -IV: Cloud Programming and Software Environments
Features of Cloud and Grid Platforms, Parallel & Distributed Programming Paradigms,
Programming Support of Google App Engine, Programming on Amazon AWS and Microsoft
Azure, Emerging Cloud Software Environments.
UNIT- V: Cloud Resource Management and Scheduling
Policies and Mechanisms for Resource Management Applications of Control Theory to Task
Scheduling on a Cloud, Stability of a Two Level Resource Allocation Architecture, Feedback
Control Based on Dynamic Thresholds. Coordination of Specialized Autonomic Performance
Managers, Resource Bundling, 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.
UNIT- VI: Storage Systems
Evolution of storage technology, storage models, file systems and database, distributed file
systems, general parallel file systems. Google file system. Apache Hadoop, Big Table,
Megastore, Amazon Simple Storage Service (S3)
IV Year – I Semester
L T P C
4 0 0 3
OUTCOMES:
• Understanding the key dimensions of the challenge of Cloud Computing
• Assessment of the economics , financial, and technological implications for selecting
cloud computing for own organization
• Assessing the financial, technological, and organizational capacity of employer’s for
actively initiating and installing cloud-based applications.
• Assessment of own organizations’ needs for capacity building and training in cloud
computing-related IT areas
TEXT BOOKS:
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(Elective – 1)
OBJECTIVES:
• Optimize business decisions and create competitive advantage with Big Data analytics
• Introducing Java concepts required for developing map reduce programs
• Derive business benefit from unstructured data
• Imparting the architectural concepts of Hadoop and introducing map reduce paradigm
• To introduce programming tools PIG & HIVE in Hadoop echo system.
UNIT-I
Data structures in Java: Linked List, Stacks, Queues, Sets, Maps; Generics: Generic classes and
Type parameters, Implementing Generic Types, Generic Methods, Wrapper Classes, Concept of
Serialization
UNIT-II
Working with Big Data: Google File System, Hadoop Distributed File System (HDFS) –
Building blocks of Hadoop (Namenode, Datanode, Secondary Namenode, JobTracker,
TaskTracker), Introducing and Configuring Hadoop cluster (Local, Pseudo-distributed mode,
Fully Distributed mode), Configuring XML files.
UNIT-III
Writing MapReduce Programs: A Weather Dataset, Understanding Hadoop API for
MapReduce Framework (Old and New), Basic programs of Hadoop MapReduce: Driver code,
Mapper code, Reducer code, RecordReader, Combiner, Partitioner
UNIT-IV
Hadoop I/O: The Writable Interface, WritableComparable and comparators, Writable Classes:
Writable wrappers for Java primitives, Text, BytesWritable, NullWritable, ObjectWritable and
GenericWritable, Writable collections, Implementing a Custom Writable: Implementing a
RawComparator for speed, Custom comparators
UNIT-V
Pig: Hadoop Programming Made Easier
Admiring the Pig Architecture, Going with the Pig Latin Application Flow, Working through the
ABCs of Pig Latin, Evaluating Local and Distributed Modes of Running Pig Scripts, Checking
out the Pig Script Interfaces, Scripting with Pig Latin
UNIT-VI
Applying Structure to Hadoop Data with Hive:
Saying Hello to Hive, Seeing How the Hive is Put Together, Getting Started with Apache Hive,
Examining the Hive Clients, Working with Hive Data Types, Creating and Managing Databases
and Tables, Seeing How the Hive Data Manipulation Language Works, Querying and Analyzing
Data
IV Year – I Semester
L T P C
4 0 0 3
OUTCOMES:
• Preparing for data summarization, query, and analysis.
• Applying data modeling techniques to large data sets
• Creating applications for Big Data analytics
• Building a complete business data analytic solution
TEXT BOOKS:
SOFTWARE LINKS:
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