Course Information
Course Code
21CS71
Credits
03
Total Hours
40 hours
Examination
Theory (3 hours)
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Course Objectives
- Introduce the rationale behind the cloud computing revolution and the business drivers.
- Introduce various models of cloud computing (IaaS, PaaS, SaaS).
- Provide an introduction on how to design cloud-native applications, the necessary tools, and the design trade-offs.
- Realize the importance of cloud virtualization, abstractions, enabling technologies, and cloud security.
Course Modules
Module 1: Introduction to Machine Learning
Topics: Cloud Computing at a Glance, Historical Developments, Building Cloud Computing Environments, Amazon Web Services (AWS), Google App Engine, Microsoft Azure.
Module 2: Data Understanding and Learning Theory
Topics: Introduction to Virtualization, Characteristics of Virtualized Environments, Pros and Cons of Virtualization, Technology Examples.
Module 3: Similarity-Based Learning and Regression
Topics: Cloud Reference Model, Types of Clouds, Economics of the Cloud, Open Challenges.
Module 4: Bayesian Learning and Neural Networks
Topics: Risks in Cloud Security, Privacy Impact Assessment, OS Security, VM Security, Security Risks posed by Shared Images.
Module 5: Clustering and Reinforcement Learning
Topics: Amazon Web Services (Compute, Storage, Communication services), Google App Engine (Architecture, Application Lifecycle, Cost Model), Cloud Applications in Science and Business.