Computer Science•Chapter 9•3 min read

Cloud Computing and Distributed Systems Fundamentals

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OIYO EditorialContributor
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What Is Cloud Computing

Cloud computing delivers computing resources (servers, storage, databases, networking) on demand over the internet.

On-Premises vs Cloud:

On-Premises: buy, install, and manage your own servers

  • High upfront cost, slow to change, requires dedicated staff

Cloud: use and pay for only what you need

  • No upfront cost, elastic scaling, reduced management burden
  • Long-term cost comparison analysis still needed

Cloud Service Models

IaaS (Infrastructure as a Service):

  • Provides virtual servers, storage, and networking
  • User manages OS, middleware, and applications
  • Examples: AWS EC2, Google Compute Engine

PaaS (Platform as a Service):

  • Provides a development and deployment platform
  • User only manages application code (infrastructure is automated)
  • Examples: Google App Engine, Heroku, AWS Elastic Beanstalk

SaaS (Software as a Service):

  • Subscribe to finished software
  • Provider manages infrastructure, platform, and application
  • Examples: Gmail, Slack, Salesforce, Office 365

Key Cloud Services (AWS)

Compute:

  • EC2: Virtual servers (instances)
  • Lambda: Serverless function execution (event-driven)
  • ECS/EKS: Container management (Docker/Kubernetes)

Storage:

  • S3: Object storage (images, files, static assets)
  • EBS: Block storage attached to EC2 (virtual HDD)
  • EFS: Shared file system

Databases:

  • RDS: Managed relational DB (MySQL, PostgreSQL)
  • DynamoDB: Managed NoSQL
  • ElastiCache: Redis/Memcached cache

Networking:

  • CloudFront: CDN (global content delivery)
  • Route 53: DNS service
  • VPC: Virtual Private Cloud

Scalability

Vertical Scaling (Scale Up):

  • Upgrade server hardware (add CPU, RAM)
  • Has physical limits, single point of failure

Horizontal Scaling (Scale Out):

  • Add more servers
  • Theoretically unlimited
  • Requires a load balancer

Load Balancing:

  • Distribute traffic across multiple servers
  • Round Robin, Least Connections, IP Hash
  • Health checks automatically remove failed servers

Microservice Architecture

Monolithic:

  • All features in a single deployable unit
  • Simple but hard to scale and change

Microservices:

  • Each feature separated into an independent service
  • Each service deployed and scaled independently
  • Services communicate via APIs (REST, gRPC, message queues)

Pros: Independent deployment, technology diversity, fault isolation

Cons: Complex operations, distributed transactions, network overhead

Real world: Used by large-scale services like Netflix and Amazon

CAP Theorem

In a distributed system, you cannot guarantee all three simultaneously:

Consistency:

  • All nodes see the same data at the same time

Availability:

  • Every request receives a response (even with failed nodes)

Partition Tolerance:

  • System continues to operate despite network partitions

In practice:

  • P (Partition Tolerance) is required in distributed systems
  • CP: Consistency + Partition Tolerance (HBase, MongoDB with config)
  • AP: Availability + Partition Tolerance (Cassandra, DynamoDB)
  • Choose based on your trade-off requirements

Key Takeaways

IaaS(virtual servers) > PaaS(platform) > SaaS(finished app) — decreasing management responsibility Horizontal scaling (more servers) + load balancer = the core of cloud scalability CAP theorem: choose only 2 of Consistency, Availability, and Partition Tolerance Serverless (Lambda): deploy only code, no infrastructure management needed

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