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cloud computing

delivery of computing services (servers, databases, networking, software) over the internet

  • service: a software that provides functionality and performs a task or set of tasks for your system

    • examples:
      • apache
      • nginx
      • postgresql
      • auth0
      • prometheus

  • infrastructure: the physical or virtual resources that support the operation of a system

    • e.g. servers, storage, network, databases, etc

  • serverless: cloud provider handles all the underlying infrastructure, maintenance, updates and operational tasks

    • no visibility in the machines
    • no server management
      • automatic scaling
    • pay-as-you-go model: only pay for what you use
    • e.g. AWS Lambda, DynamoDB

  • fully managed service: cloud provider manages the service's infrastructure and resources

    • the user focuses only on using the service
    • pay for machine runtime
    • e.g. AWS RDS, Google Cloud SQL

  • private cloud: cloud infrastructure that is operated solely for a single organization

    • IBM cloud private

  • public cloud: cloud infrastructure that is available to the general public

    • AWS, Azure, GCP

  • Compute: CPU

  • Memory: RAM

  • Storage: Data

  • Database: Storing data in a structured way

  • Network: Routers, switches, DNS servers

  • advantages of cloud computing

    • on-demand resources can be provisioned without human interaction
    • resources are available over the network, and can be accessed by diverse client platforms
    • quickly and easily scale based on demand (stop guessing capacity)
    • measured payment for resources (pay only what you use)
    • benefits from massive economies of scale
    • trade CAPEX (CAPital EXpense) for OPEX (OPerational EXpense)
    • easily access global infrastructure

  • problems solved by the cloud

    • flexibility
    • cost-effectiveness
    • scalability
    • elasticity
    • high-availability
    • fault-tolerance
    • agility

  • scalability vs elasticity vs agility

    • scalability: ability to accommodate larger load by adding resources
      • scaling up: increasing resources of a single instance
      • scaling out: adding more instances
    • elasticity: ability to automatically scale resources based on demand
    • agility: how quickly resources get provisioned to developers

  • vertical scalability: increasing/decreasing the size of an instance

  • horizontal scalability: increasing/decreasing the number of instances/systems for your application

  • users

    • multiple people can belong to a group
    • a person can belong to multiple groups simultaneously

  • load balancing: distributing incoming network traffic across multiple servers

    • improves responsiveness and availability of applications
    • prevents a single server from becoming a single point of failure

  • on-site: infrastructure is physically located at your site

  • IaaS (Infrastructure as a Service): provides virtualized computing resources over the internet

    • e.g. AWS EC2, Google Compute Engine

  • PaaS (Platform as a Service): provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure

    • e.g. AWS Elastic Beanstalk

  • SaaS (Software as a Service): software that is centrally hosted and licensed on a subscription basis

    • e.g. Google Drive
On-site IaaS PaaS SaaS
Applications You You You Service provider
Data You You You Service provider
Runtime You You Service provider Service provider
Middleware You You Service provider Service provider
O/S You You Service provider Service provider
Virtualization You Service provider Service provider Service provider
Servers You Service provider Service provider Service provider
Storage You Service provider Service provider Service provider
Networking You Service provider Service provider Service provider

  • CaaS (Communication as a Service): provides communication services over the internet

    • e.g. Skype

  • Database as a Service (DBaaS): provides database services over the internet

    • e.g. AWS RDS, Google Cloud SQL

  • workloads: computing resources required to run an application or service in a cloud computing environment

    • static workload: consistent demand for resources
    • periodic workload: demand for resources is predictable and occurs at regular intervals
    • once-in-a-lifetime workload: demand for resources is unpredictable and occurs once
    • unpredictable workload: demand for resources is unpredictable
    • continously changing workload: demand for resources changes frequently

  • migration strategies: moving applications to the cloud

    • lift-and-shift: move applications to the cloud without making any changes
    • improve-and-move: move applications with some optimizations for the cloud
    • rip-and-replace: replace applications with cloud-native applications that are built from scratch

WAF (Well-Architected Framework) and CAF (Cloud Adoption Framework)

  • WAF: a set of best practices for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud
  • CAF: a set of best practices for adopting cloud technologies and services in a structured and organized way

5 pillars of cloud computing architecture

  1. operational excellence: automation, monitoring, and management
  2. security: protect data, systems, and assets
  3. reliability: ability to recover from failures and maintain availability
  4. performance efficiency: use resources efficiently
  5. cost optimization: avoid unnecessary costs

storage services

  • file storage: data is stored in a hierarchical file system (directories and files, like your computer)
    • e.g. AWS EFS
  • block storage: data is stored in fixed-size blocks
    • e.g. AWS EBS
    • ideal for databases
    • block: a sequence of bytes or bits having a fixed length
      • each block has a unique id, no additional metadata is stored
  • object storage: data is stored as objects
    • e.g. AWS S3
    • object: a collection of data that contains both data and metadata
    • ideal for non-structured data

Compute

Azure AWS GCP
Azure Virtual Machines AWS EC2 (Elastic Compute Cloud) Google Compute Engine
Azure Functions AWS Lambda Google Cloud Functions
Azure App Service AWS Elastic Beanstalk Google App Engine

Storage

Azure AWS GCP
Azure Blob Storage AWS S3 (Simple Storage Service) Google Cloud Storage
Azure Disk Storage AWS EBS (Elastic Block Store) Google Persistent Disk
Azure File Storage AWS EFS (Elastic File System) Google Cloud Filestore

Database

Azure AWS GCP
Azure SQL Database AWS RDS (Relational Database Service) Google Cloud SQL
Azure Cosmos DB AWS DynamoDB Google Cloud Firestore
Azure Cache for Redis AWS ElastiCache Google Cloud Memorystore

Content Delivery and Network

Azure AWS GCP
Azure Virtual Network Azure DNS AWS VPC (Virtual Private Cloud) Google Cloud Virtual Network (VPC)
Azure Content AWS Route 53 Google Cloud DNS
Delivery Network (CDN) AWS CloudFront Google Cloud CDN

Security and Identity Management

Azure AWS GCP
Azure Active Directory AWS IAM (Identity and Access Management) Google Cloud Identity and Access Management (IAM)
Azure Key Vault AWS Cognito Google Cloud Identity Platform
--- AWS Key Management Service (KMS) Google Cloud Key Management Service (KMS)

Applications and Services

Azure AWS GCP
Azure API Management AWS API Gateway Google Cloud API Gateway
Azure Service Bus AWS SQS (Simple Queue Service) Google Cloud Pub/Sub
Azure Logic Apps AWS Step Functions Google Cloud Composer

Development, CI/CD and DevOps

Azure AWS GCP
Azure DevOps Services AWS CodeCommit & AWS CodePipeline Google Cloud Build
Azure Pipelines AWS CodeBuild Google Cloud Source Repositories
Azure Repos AWS CodeDeploy Google Cloud Deployment Manager

Container Orchestration

Azure AWS GCP
Azure Kubernetes Service (AKS) AWS ECS (Elastic Container Service) Google Kubernetes Engine (GKE)
Azure Container Instances (ACI) AWS ECR (Elastic Container Registry) Google Cloud Run
Azure Container Registry AWS EKS (Elastic Kubernetes Service) Google Cloud Container Registry

Data Analysis and BigData

Azure AWS GCP
Azure Synapse Analytics AWS Redshift Google BigQuery
Azure Data Lake Storage AWS Athena Google Cloud Dataproc
Azure HDInsight AWS EMR (Elastic MapReduce) Google Cloud Dataflow
Azure Stream Analytics AWS Kinesis Google Pub/Sub
Azure Databricks
  • cloud-native: building apps specifically designed to run in cloud environment
    • fully leverages the benefits of cloud computing
  • edge computing: computing model where data processing happens close to where the data is generated, at the edge of the network
  • hybrid service: service that can operate both in the cloud and on-premises