History of Computing Milestones

Cloud Computing & IoT

Course 1: Introduction to Cloud Computing
Instructor: Pr Meriem Belguidoum
Semester: 2024/2025, Semester 3
Topic: Cloud Computing
Subtopics: History, Definition, Why use cloud computing, Cloud Deployment Models, Cloud Service Models, Comparison between Cloud Providers, Cloud Computing & IoT
Course Outline:
- Some History
- What is Cloud Computing?
- Why using cloud computing?
- The features of Cloud Computing
- Cloud deployment models
- Cloud service models
- Comparison between some Cloud providers
- Cloud computing & IoT
History
- 1960s: Time-sharing on mainframes: Early shared computing, maximizing expensive resource use.
- 1970s: Virtual Machines (VMs): Foundation for multiple isolated environments on a single physical machine.
- 1990s: Rise of the Internet & ASPs (Application Service Providers): Hosted software applications offered over the web, a precursor to SaaS.
- Late 1990s - Early 2000s: Dot-Com Boom & Bust: Highlighted the limitations of traditional IT and the need for flexible, scalable solutions.
- 2006: Amazon launches AWS: Marks the beginning of modern cloud computing with on-demand services for storage and computing power.
- 2008-2010: Google App Engine & Microsoft Azure Launch: Major players enter the market, increasing competition and driving innovation.
- 2010s: Mobile & Big Data Explosion: Further accelerates cloud adoption due to the need for scalability, cost-effectiveness, and data processing capabilities.
- Present: Serverless, AI/ML, Edge computing emerge: The cloud continues to evolve, enabling new possibilities and transforming industries.

Real-world Use Cases

E-commerce: Website and platform hosting, handling millions of online transactions with dynamic resource scaling to accommodate traffic surges.
Education: Learning Management Systems (LMS) like Moodle, Canvas, Blackboard, utilizing the cloud for online learning platforms. Students receive anytime, anywhere access.
Entertainment: Video and music streaming services like Netflix, Spotify, Disney+ relying on the cloud to stream multimedia content globally to ensure quality and minimal buffering.
Healthcare: Medical data storage and analysis, enabling secure storage and analysis of large volumes of medical data, facilitating personalized medicine, and research.
Finance: Online banking and algorithm trading, financial institutions leveraging the cloud to power secure and reliable online banking platforms.

What is Cloud Computing?

Definition (NIST): Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

Why Using Cloud Computing?

Traditional Approach Limitations: High costs, significant upfront investments in hardware, software, maintenance, high complexity requiring specialized technical skills, lack of flexibility to adapt to changing needs and peak demands, underutilized resources (servers often idle, wasting energy).
Real-world use case challenges: Handling website traffic surges during peak seasons, enabling seamless collaboration for remote teams across different locations and time zones.
Cloud Solutions: Elasticity, cloud resources scale up or down automatically, adjusting to changing traffic patterns. Cloud-based collaboration Platforms offer tools like Google Workspace and Microsoft 365.
Benefits: Cost-efficiency, improved performance, enhanced productivity, streamlined workflows, access to work anywhere, anytime.

Cloud Deployment Models

Public Cloud: Massive available space, easy scalability, recommended for software development and collaborative projects (e.g., AWS, Azure, Google Cloud).
Private Cloud: Resides behind a firewall, used by a single organization, recommended for businesses with stringent regulatory requirements.
Hybrid Cloud: Combines public and private clouds for seamless interaction; balancing big data analytics with strict data privacy regulations.
Community Cloud: Collaborative, multi-tenant platform shared by organizations with common interests, like research institutions, government agencies, or industry groups.

Cloud Service Models

IaaS (Infrastructure as a Service): The user has complete control over the physical and virtual environment, manages operating systems, applications, and data. (Examples: Amazon Web Services (AWS) - EC2, Microsoft Azure - Virtual Machines)
PaaS (Platform as a Service): Provides a complete platform for developing, testing, deploying, and managing applications without worrying about the underlying infrastructure. (Examples: Amazon Web Services (AWS) - Elastic Beanstalk, Microsoft Azure - App Service)
SaaS (Software as a Service): Software applications delivered over the internet. The user doesn't manage the underlying infrastructure, applications, or operating systems. (Examples: Google Workspace, Microsoft 365)

Comparison between Cloud Providers

AWS, Azure, GCP: Strengths, weaknesses, market position, strengths of each service for compute, networking, database, data analytics, machine learning, and other services.

Cloud Computing & IoT

Cloud as Engine for IoT: Stores and analyzes IoT devices' data safely and securely to offer insights in real-time.
Cloud Platforms for IoT Solutions: Offers tools and platforms for IoT solutions, such as connecting, monitoring, managing, and analyzing (e.g., AWS IoT, Google Cloud IoT Core, Azure IoT Hub).
IoT Data Analysis and Prediction: Utilizing machine learning services and APIs to find patterns in IoT data; automating actions for predictive, more efficient processes, especially related to equipment failures