Cloud Computing and Distributed Systems

Study Notes

Cloud Security

Infrastructure Security: Protection of hardware, software, and networks associated with cloud services.
Data Security and Storage: Safeguarding data from unauthorized access and ensuring secure storage solutions.
Identity and Access Management: Managing user identities and regulating access to resources based on authentication and authorization.
Access Control, Trust, Reputation, and Risk: Mechanisms to ensure that users gain appropriate access, and risk assessment processes to maintain cloud security.

Case Study and Simulations

Open Source and Commercial Clouds: Examination of the differences, benefits, and limitations of open-source versus commercial cloud solutions.
Cloud Simulator: A tool for testing and evaluating cloud environments and service delivery models.

Research Trends

Cloud Computing: Ongoing advancements and applications in cloud technology.
Fog Computing: Extending cloud capabilities to the edge of the network closer to data sources.
Distributed Computing: Leveraging multiple computers to process data simultaneously across a network.
Grid Computing: Coordinating resources across networks of computers to work on common problems.
Cluster Computing: Grouping computers to work together while presenting a single system to users.
Utility Computing: Offering computing resources as a utility service, similar to water or electricity.

Centralized vs. Distributed Computing

Uni-Processor Computing: Centralized computing utilizing a single processor for operations.
Distributed Computing Definition: A field of computer science focused on solving problems using distributed systems composed of multiple autonomous entities with local memory.

Characteristics of Distributed Systems

Communication: Processes communicate via message passing over various communication lines (e.g., high-speed buses, telephone lines).
Applications: Examples include Facebook and the Large Hadron Collider (LHC) for data processing.

Robustness

Single Point of Failure (SPOF): Distributed systems are designed to avoid SPOF; other nodes can take over tasks from failed nodes.

Performance Factors

Nature of Applications: Distributed computing supports both computing-intensive and data-intensive applications.
Computation of Pi: Example of heavy computational tasks, such as Monte Carlo simulations that benefit from distributed processing.

Distributed Applications

Application Structure: Consist of processes distributed across machines, working collaboratively to solve common problems.
Client-Server Model: Historically centralized resource management, where clients interact with individual servers.
Peer-to-Peer Computing: Represents a shift towards more decentralized applications where each node can act as both client and server.

Description

Test your knowledge on various aspects of cloud computing, including cloud security, data storage, and access management, as well as distributed computing, grid computing, and cluster computing. This quiz covers both theoretical and practical concepts, including case studies and research trends.