There are three main cloud deployment models:

• Public Cloud: Services are available to the general public (e.g., AWS, Azure, Google Cloud)
• Private Cloud: Used by a single organization for enhanced control and security
• Hybrid Cloud: A combination of public and private clouds for flexibility and scalability

The three main service models are:

• IaaS (Infrastructure as a Service): Provides virtualized computing resources (e.g., AWS EC2, Azure VM)
• PaaS (Platform as a Service): Offers a platform for developing and deploying applications (e.g., Google App Engine)
• SaaS (Software as a Service): Delivers software applications over the internet (e.g., Gmail, Salesforce)

• Cost efficiency – Pay-as-you-go model
• Scalability – Easily increase or decrease resources
• Flexibility – Access from anywhere, anytime
• Reliability – High uptime and data backup
• Security – Advanced encryption and access control features

Virtualization is the technology that allows multiple virtual machines (VMs) to run on a
single physical server. It enables efficient resource utilization, scalability, and isolation —
forming the foundation of cloud computing infrastructure.

The most widely used cloud platforms are:

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)
• IBM Cloud
• Oracle Cloud

Auto-scaling automatically adjusts the number of computing resources (like instances) based
on current demand. It helps maintain performance while optimizing cost by scaling up during
peak times and scaling down during low usage.

Data security in the cloud is achieved through:

• Encryption (both in-transit and at rest)
• Identity and Access Management (IAM)
• Firewalls and security groups
• Regular audits and compliance checks

• Data privacy and security concerns
• Internet dependency
• Compliance with regulations
• Cost management for large-scale operations
• Vendor lock-in issues

• Scalability refers to the system’s ability to handle increased workload by adding resources (usually planned)
• Elasticity allows resources to be automatically added or removed based on real-time demand (dynamic adjustment)

Cloud providers use multiple layers of security such as encryption (in transit and at rest),
firewalls, multi-factor authentication, and identity & access management (IAM). They
also comply with international standards like ISO 27001, GDPR, and SOC 2, ensuring
strong data protection measures.

Serverless computing allows developers to run code without managing servers. The cloud
provider handles all infrastructure, scaling, and maintenance. Unlike IaaS or PaaS, you only
pay for actual execution time, not idle resources.
 Example: AWS Lambda, Azure Functions, Google Cloud Functions.

Cloud bursting is a hybrid cloud strategy where an application runs in a private cloud but
“bursts” into a public cloud when demand spikes.

It’s commonly used in e-commerce, banking, and media streaming during peak traffic
periods to maintain performance.

A VPC is an isolated section of a public cloud where you can launch and manage resources

securely. It offers complete control over network configuration, IP ranges, subnets, and
security groups, functioning like a private data center within a public cloud.

Load balancing distributes incoming traffic across multiple servers to ensure no single server
is overloaded. It improves availability, fault tolerance, and response time.
Cloud providers like AWS ELB and Azure Load Balancer automatically scale and redirect
traffic based on performance metrics.

• Cloud Storage: Stores unstructured data like files, images, and videos (e.g., AWS S3, Google Cloud Storage)
• Cloud Database: Stores structured data and supports querying (e.g., AWS RDS, Azure SQL Database).

Both serve different use cases—storage is for data backup and retrieval, while
databases handle transactional workloads.

Containers package applications with all dependencies, ensuring they run consistently across
environments. They’re lightweight and ideal for cloud deployments.
Tools like Docker and Kubernetes enable container orchestration, making microservices-
based architectures scalable and efficient in the cloud.

• Use auto-scaling and right-sizing instances
• Choose spot instances or reserved instances
• Implement cost monitoring tools (like AWS Cost Explorer)
• Delete unused resources (idle volumes, IPs)
• Schedule non-production environments to shut down after hours

Edge computing processes data closer to the source (like IoT devices) instead of relying on a
centralized cloud. It reduces latency and bandwidth usage, complementing cloud computing
by handling time-sensitive tasks locally while offloading heavy computation to the cloud.

Multi-cloud is the use of services from multiple cloud providers (like AWS + Azure + GCP).
Organizations adopt it for:

• Avoiding vendor lock-in
• Improved reliability and redundancy
• Optimizing costs
• Leveraging best features from each provider

Infrastructure as Code (IaC) is the practice of managing and provisioning cloud resources
through code instead of manual setup. Tools like Terraform, AWS CloudFormation, and
Azure Resource Manager help automate deployments, ensuring consistency and faster
scaling.
Example: You can write a Terraform script to launch an EC2 instance with predefined
settings in seconds.

• Elasticity means automatically adjusting resources based on real-time demand
  (scaling up/down dynamically).
• Scalability means designing a system that can handle growth (adding resources to
  meet future demand).
• ⏱️ Elasticity = Short-term automatic adjustment
• 📊 Scalability = Long-term system design capacity

• Region: A geographical area (e.g., AWS Asia Pacific – Mumbai)
• Availability Zone (AZ): A separate data center within a region. Using multiple AZs ensures high availability, fault tolerance, and disaster recovery
  if one zone goes down.

The Shared Responsibility Model defines who is responsible for what:

• Cloud Provider: Security of the cloud (hardware, infrastructure, global network)
• User/Customer: Security in the cloud (data, access management, applications). This model ensures both parties maintain proper security controls.

Cloud monitoring involves tracking performance, uptime, and resource usage of cloud
services using tools like AWS CloudWatch, Azure Monitor, or Google Stackdriver.
It helps detect issues early, improve reliability, and optimize costs through performance
analytics.