Cloud Computing Essentials

Infrastructure as a Service (IaaS)

Virtualized computing resources and infrastructure components.

• Virtual machines and servers
• Storage and networking
• Load balancers and firewalls

Platform as a Service (PaaS)

Development platforms with built-in tools and services.

• Application development frameworks
• Databases and analytics
• DevOps and CI/CD tools

Lift and Shift

Direct migration of applications to cloud infrastructure.

Refactor

Optimize applications for cloud-native architectures.

Rebuild

Complete redesign using cloud-first principles.

Infrastructure Monitoring

Tracking system performance and resource utilization.

Application Performance

Monitoring application response times and error rates.

Log Management

Centralized logging and analysis for troubleshooting.

What cloud service models are?

Cloud service models define different levels of abstraction and management responsibility. Infrastructure as a Service provides virtualized computing resources like virtual machines, storage, and networking that users can configure and manage. Platform as a Service offers development platforms with built-in tools, databases, and middleware, allowing developers to focus on application code. Software as a Service delivers complete applications over the internet, requiring no installation or maintenance. Each model offers different trade-offs between control, management overhead, and ease of use. Organizations choose models based on technical requirements, development resources, and business needs. Hybrid approaches combining multiple models provide flexibility for complex architectures.

How cloud storage works?

Cloud storage provides scalable, durable data storage accessible over the internet. Object storage systems like Amazon S3 store data as objects with metadata, offering unlimited scalability and global distribution. Block storage provides high-performance storage for databases and applications requiring low-latency access. File storage offers traditional file system interfaces for shared access. Data is distributed across multiple servers and data centers for redundancy, with automatic replication ensuring durability. Access controls manage permissions, and encryption protects data at rest and in transit. Storage classes offer different performance and cost characteristics for various use cases, from frequently accessed data to long-term archival storage.

What serverless architecture is?

Serverless architecture allows developers to build applications without managing underlying servers. Functions as a Service execute code in response to events, automatically scaling based on demand. Developers focus on business logic while cloud providers handle infrastructure, scaling, and maintenance. Benefits include reduced operational overhead, automatic scaling, and pay-per-execution pricing. Applications are built as collections of functions triggered by events like API calls, database changes, or scheduled tasks. Backend services provide managed databases, authentication, and messaging. Serverless enables rapid development and deployment cycles, particularly suitable for microservices and event-driven architectures.

How hybrid clouds work?

Hybrid clouds combine public and private cloud environments for optimal flexibility. Organizations maintain private clouds for sensitive data and applications requiring strict control, while using public clouds for scalable workloads and development. Hybrid architectures enable workload portability between environments and provide disaster recovery capabilities. Key technologies include VPNs for secure connections, API gateways for service integration, and orchestration tools for workload management. Benefits include cost optimization, compliance with data sovereignty requirements, and gradual migration strategies. Multi-cloud approaches extend hybrid concepts across multiple public cloud providers to avoid vendor lock-in and leverage best-of-breed services.

What cloud migration involves?

Cloud migration moves applications and data from on-premises infrastructure to cloud environments. Assessment phase evaluates current applications, dependencies, and migration feasibility. Planning establishes migration strategy, timeline, and resource requirements. Lift and shift migrations move applications unchanged to cloud infrastructure. Refactoring optimizes applications for cloud-native features like auto-scaling and managed services. Replatforming makes minimal changes for cloud compatibility. Complete rebuilds redesign applications using cloud-first principles. Testing validates migrated applications, and optimization tunes performance and costs. Migration requires careful planning for data transfer, security, compliance, and minimal business disruption.

How to optimize cloud costs?

Cloud cost optimization requires continuous monitoring and resource management. Right-sizing matches instance types to actual workload requirements, avoiding over-provisioning. Reserved instances and savings plans provide discounts for predictable workloads. Spot instances offer significant savings for fault-tolerant applications. Auto-scaling ensures resources match demand, preventing over-provisioning during low usage. Storage lifecycle policies automatically move data to cheaper storage classes. Unused resource cleanup removes forgotten instances and volumes. Cost allocation tags enable chargeback and budget tracking. Regular cost analysis identifies optimization opportunities. Cloud cost management tools provide visibility and automated recommendations for cost reduction.

What cloud security includes?

Cloud security encompasses multiple layers of protection across shared responsibility models. Identity and access management controls who can access resources through role-based permissions and multi-factor authentication. Data encryption protects information at rest and in transit using industry-standard algorithms. Network security includes firewalls, VPNs, and DDoS protection. Compliance frameworks ensure adherence to regulations like GDPR, HIPAA, and PCI DSS. Security monitoring detects threats through logging and alerting. Backup and disaster recovery protect against data loss. Zero-trust architecture assumes no implicit trust, requiring verification for all access requests. Regular security assessments and penetration testing identify vulnerabilities.

How scalability works in cloud?

Cloud scalability enables automatic resource adjustment based on demand. Horizontal scaling adds more instances to distribute load across multiple servers. Vertical scaling increases resources of existing instances. Auto-scaling groups automatically adjust capacity based on metrics like CPU utilization or request rates. Load balancers distribute traffic across instances. Container orchestration platforms like Kubernetes manage scaling of microservices. Serverless functions scale automatically to zero or thousands of concurrent executions. Database scaling includes read replicas and sharding for high-throughput applications. Elasticity ensures resources match actual usage, optimizing costs while maintaining performance.

What cloud providers offer?

Major cloud providers offer comprehensive suites of services with different strengths. Amazon Web Services provides the broadest portfolio with enterprise-grade reliability and global infrastructure. Microsoft Azure excels in hybrid cloud capabilities and enterprise integration, particularly for Windows environments. Google Cloud Platform leads in AI/ML capabilities and big data analytics with competitive pricing. IBM Cloud focuses on enterprise solutions with strong security and compliance features. Oracle Cloud specializes in database and enterprise applications. DigitalOcean offers simplicity for developers and startups. Regional providers serve specific geographic markets. Selection depends on existing infrastructure, required services, compliance needs, and cost considerations.

What monitoring tools provide?

Cloud monitoring tools provide visibility into system performance and health. Infrastructure monitoring tracks CPU, memory, disk, and network utilization across all resources. Application performance monitoring measures response times, error rates, and throughput. Log management aggregates and analyzes logs from applications and infrastructure. Alerting systems notify teams of issues before they impact users. Dashboards provide real-time visualization of key metrics. Tracing follows requests through distributed systems. Synthetic monitoring simulates user interactions to detect issues. Analytics identify performance trends and optimization opportunities. Monitoring enables proactive problem resolution, capacity planning, and continuous improvement of cloud deployments.