WEEK 05 — DIGITAL TRANSFORMATION
Cloud, Fog, and Edge Computing
Understanding modern computing infrastructures and service models
CHAPTER 01
Introduction to Cloud Computing
The foundational infrastructure delivering on-demand computing services over the internet.
CASE STUDY
The Growing Pains of FreshBake Bakery
FreshBake Bakery is a rapidly expanding business with 10 physical stores. They host their website, inventory system, and loyalty program on an aging on-premise server, leading to crashes during peak holiday seasons and delayed inventory updates.
BUSINESS CHALLENGE
CORE PRINCIPLE
What is Cloud Computing?
Cloud computing delivers on-demand computing services—including storage, software, and processing power—over the internet. Instead of owning physical servers, companies rent resources from providers like AWS, Microsoft Azure, or Google Cloud.
Scalability
Resources can be scaled up or down based on demand, enabling e-commerce platforms to handle traffic spikes during sales easily.
Cost-Effectiveness
Reduces capital expenses by eliminating in-house hardware. Operates on a pay-as-you-go model where you only pay for what you use.
Accessibility & Global Reach
Services are accessible from anywhere. Cloud data centers allow companies to operate globally with low latency and enhanced collaboration.
Flexibility & Innovation
Supports rapid deployment of applications and services, fostering continuous innovation and adaptability to market changes.
How has Netflix leveraged cloud computing to transform its global streaming service in terms of scalability and customer experience?
Class Discussion
CHAPTER 02
Cloud Service Models
Exploring the three primary models of cloud services: IaaS, PaaS, and SaaS.
THEORETICAL FRAMEWORK
IaaS, PaaS, and SaaS
Cloud computing is generally categorized into three service models, each offering varying levels of control, flexibility, and management for businesses.
Infrastructure as a Service (IaaS) provides virtualized computing resources, Platform as a Service (PaaS) offers tools for developers, and Software as a Service (SaaS) delivers fully functional applications.
IaaS (Infrastructure as a Service)
Provides virtual servers and storage. Example: AWS EC2 allows businesses like Airbnb to scale server capacity during peak travel seasons and reduce costs.
PaaS (Platform as a Service)
Enables developers to build and deploy without managing infrastructure. Example: Google App Engine allowed Snapchat to deploy quickly and focus on app development.
SaaS (Software as a Service)
Delivers complete applications over the web. Example: Salesforce CRM helps Coca-Cola manage sales globally with real-time access and collaboration.
CRITICAL ANALYSIS
Risks and Challenges
While cloud computing offers immense benefits, it also introduces significant challenges such as security concerns involving data breaches, and the potential for operations disruption due to provider outages.
KEY COMPONENT
CHAPTER 03
Edge Computing
Processing data closer to the source to reduce latency and bandwidth usage.
CASE STUDY
The Real-Time Dilemma at HealthGuard
HealthGuard Hospital uses IoT wearables to monitor patient vital signs. Sending all data to a centralized cloud server causes critical alert delays during emergencies, network congestion, sky-high bandwidth costs, and security concerns.
They need a solution that improves response times, reduces costs, and ensures reliable care without compromising patient privacy or relying entirely on a distant cloud server.
CORE PRINCIPLE
Bringing Computation to the Edge
Edge computing processes data close to its source, such as on IoT devices or local servers, rather than sending everything to centralized data centers. This localized approach is critical for time-sensitive applications.
WALMART USE CASE
Low Latency
Critical for real-time operations like autonomous vehicles and industrial automation where milliseconds matter.
Bandwidth Optimization
Sending less data to the cloud saves significant transmission costs and reduces internet congestion.
Reliability
Systems can continue to function and make decisions even with limited or complete loss of internet connectivity.
ARCHITECTURE
Cloud vs Edge vs Fog
CHAPTER 04
Fog Computing
The decentralized intermediate layer bridging the gap between Edge and Cloud.
SCENARIO
Running a Modern Factory
Imagine a factory with dozens of machines operating simultaneously. Each machine produces continuous sensor data about its speed, components, and output.
This massive volume of data needs to be analyzed in real time to derive key business insights without overwhelming external networks or relying purely on isolated device-level processing.
CORE PRINCIPLE
The Intermediate Layer
Fog computing is a decentralized model that extends cloud computing by bringing data processing closer to the network edge. It acts as a bridge, enabling efficient and scalable data processing across distributed systems.
SMART CITIES (CISCO)
Intermediate Layer
Sits between edge devices and the cloud, handling data locally on routers and gateways while connecting to the cloud for heavy analysis.
Distributed Processing
Data is processed across multiple fog nodes within the local network rather than a centralized location.
Scalable Real-Time Support
Handles massive volumes of IoT data without overwhelming the cloud, perfect for smart cities and industrial IoT applications.
CONCLUSION
Summary
Modern digital transformation requires choosing the right computing infrastructure. Cloud computing offers scalable, cost-effective centralized resources through IaaS, PaaS, and SaaS models. Edge computing brings processing directly to the data source for zero-latency, real-time decision making. Fog computing serves as the crucial decentralized bridge, distributing processing across local networks to balance load and capabilities.
KEY TAKEAWAYS
- Cloud computing (IaaS, PaaS, SaaS) trades control for scalable, on-demand resources.
- Edge computing reduces latency and bandwidth usage by processing data locally.
- Fog computing provides an intermediate, decentralized processing layer.
- Selecting the right model depends on the organization's need for real-time processing versus centralized analytics.