One of the most influential paradigms covered in the book is , a programming model for processing large datasets in parallel across clusters. Originating from Google, MapReduce simplifies distributed computing by hiding complexities such as parallelization, fault tolerance, and data distribution.
: Service Level Agreements (SLAs) dictate QoS (Quality of Service) parameters, defining penalties and performance metrics critical to market trust.
: Combines public and private clouds, bound together by standardized technology that allows data and applications to be shared between them. This grants businesses greater flexibility and optimized deployment options. One of the most influential paradigms covered in
MapReduce exemplifies how cloud computing enables applications by leveraging large pools of commodity servers.
The book distinguishes four primary cloud deployment models: : Combines public and private clouds, bound together
Resources are geographically distributed but logically centralized through software management layers. This ensures fault tolerance and low latency by serving users from the closest edge data centre. Hyper-Virtualization
: Moving from desktop-bound computing to service-oriented platforms using server clusters and data centers. Essential Characteristics (NIST Model) : On-demand self-service. Broad network access. Resource pooling. Rapid elasticity. Measured service. Slide 2: Roots & Evolution of Computing Paradigms The book distinguishes four primary cloud deployment models:
Whether you are preparing a Presentation (PPT) or studying modern distributed systems, this guide breaks down the core concepts, architectures, and deployment paradigms outlined in this seminal framework. 1. Core Principles of Cloud Computing