Thursday, April 2, 2026
Monday, March 30, 2026
Advanced Cloud Computing
Assignment 1: Foundations and Recent Trends in Cloud
Computing
Objective: To understand the fundamental concepts,
evolution, and recent trends in computing paradigms leading to cloud computing.
Tasks:
·
Define and compare Distributed Computing, Grid
Computing, Cluster Computing, and Cloud Computing. Highlight their differences,
advantages, and disadvantages.
·
Discuss the evolution of cloud computing,
including its history and the role of open standards.
·
Explain the business drivers for adopting cloud
computing, such as cost efficiency, scalability, and flexibility.
·
Analyze the recent trends in computing,
including the emergence of Grid, Cluster, and Cloud Computing, and their impact
on modern IT infrastructure.
·
Describe the pros and cons of cloud computing,
emphasizing its benefits like scalability and drawbacks such as security
concerns.
Assignment 2: Cloud Computing Architecture and Service
Models
Objective: To explore the architecture, service models, and
deployment options of cloud computing.
Tasks:
·
Illustrate the cloud computing stack and compare
it with traditional client/server architecture.
·
Describe the services provided at various levels
of cloud computing and how it works, including the role of Web services.
·
Explain the three main service models: IaaS,
PaaS, and SaaS with examples.
·
Discuss the different deployment models: Public
cloud, Private cloud, Hybrid cloud, and Community cloud, including their
advantages and use cases.
·
Provide real-world examples of each service and
deployment model.
Assignment 3: Infrastructure, Platform, and Software as a
Service (IaaS, PaaS, SaaS)
Objective: To delve into the specifics of each cloud service
model, virtualization, and resource management.
Tasks:
·
Explain IaaS with emphasis on virtualization,
hypervisors, machine images, and virtual machines.
·
Describe resource virtualization concepts
including server, storage, and network virtualization.
·
Discuss examples of IaaS providers such as
Amazon EC2 and Eucalyptus, covering resource provisioning, pricing, and
management.
·
Explain PaaS, its architecture, and how it
supports Service-Oriented Architecture (SOA). Provide examples like Google App
Engine and Microsoft Azure.
·
Describe SaaS, its features, and how it differs
from traditional software deployment. Include a case study to illustrate SaaS
implementation.
·
Discuss service management aspects such as SLAs,
billing, data scalability, and large-scale data processing.
Assignment 4: Cloud Security, Data Management, and Legal
Considerations
Objective: To understand the security challenges, data
management issues, and legal considerations in cloud computing.
Tasks:
·
Discuss various security levels in cloud
computing: Network, Host, Application, and Data security.
·
Explain data security and privacy issues,
including jurisdictional challenges related to data location.
·
Describe Identity & Access Management (IAM),
Access Control, and authentication mechanisms in cloud environments.
·
Analyze trust, reputation, and risk management
in cloud services.
·
Elaborate on cloud contracting models and the
importance of SLAs.
· Discuss legal and ethical considerations, including data sovereignty, compliance, and the impact of jurisdictional laws on data security and privacy.
Monday, March 16, 2026
MDM Quiz
Quiz 1 : https://forms.gle/jwKBXadoYN9N8URZ9
Quiz 2 : https://forms.gle/xJr5NiBxrG5JyBP18
Quiz 3 : https://forms.gle/cdN5K2ADrcekFTLg7
Quiz 4 : https://forms.gle/coQCXKUEjz8BG6bB8
Wednesday, February 18, 2026
MDM
- PPT and Notes
- Syllabus
- Books
Unit 1 PPT
Unit 1 Notes
Unit 2 PPT
Unit 2 Notes
Unit 3 PPT
Unit 3 Notes
Tuesday, February 3, 2026
MDM Assignments
On Unit 1 and 2
1. Explain the concept, importance, and lifecycle of predictive analytics with real-world examples.
2. Describe key applications of predictive analytics in healthcare, finance, e-commerce, and social media.
3. Discuss how recommender systems and targeted marketing utilize predictive analytics in online retail.
4. Analyze the role, benefits, and ethical challenges of personalization and predictive modeling in retail strategies.
On Unit 3 and 4
1. Explain the different data types used in predictive analytics and their significance.Include examples of structured, unstructured, and semi-structured data, and discuss how recognizing data types influences the choice of analysis techniques.
2. Describe the complexities involved in raw data analysis.Discuss issues like missing data, noise, high dimensionality, and data variability. Include strategies for exploring and cleaning raw data to prepare it for modeling.
3. Discuss how understanding data complexities can improve predictive modeling outcomes.Include real-world examples where addressing data issues led to better model accuracy and insights.
On Unit 5 and 6
1. Select a real-world case study (e.g., Google Flu Trends, Twitter earthquake prediction).Summarize the problem, data used, modeling approach, and results.
2. Explain how clustering techniques like K-means or nearest neighbors help in identifying patterns in data.Use a practical example, such as customer segmentation or document classification.
3. Analyze the importance of feature extraction and similarity measures in building effective predictive models.Discuss how these techniques improve model performance and insights.
On Unit 7 and 8
1. Describe how classification algorithms like decision trees and support vector machines are used to predict future outcomes.Provide examples in domains like healthcare or finance.
2. Develop a step-by-step plan to build a predictive model, including data preparation, model selection, testing, and deployment.Use a hypothetical or real case example.
3. Discuss the role of ensemble methods, neural networks, and regression in improving prediction accuracy.Include benefits, limitations, and suitable scenarios for each method.