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Computing Types

Modern software systems use different computing approaches depending on where computation happens, how systems are structured, and when decisions are made.

There is no single “best” computing model. Each type exists to solve a specific class of problems related to scale, latency, reliability, cost, and complexity.

As systems evolved from single machines to globally distributed platforms and IoT systems, computing models also evolved:

From centralized to distributed
From monolithic to microservices
From cloud-only to edge and fog
From reactive to proactive

Understanding these computing types helps you:

Choose the right architecture for a problem
Understand why IoT systems cannot rely on cloud alone
See how modern data and IoT platforms fit together

Centralized Computing

Single computer or location handles all processing and storage. All resources and decisions are managed from one central point.

Characteristics

Single point of control
Centralized decision making
Consistent data
Simpler security
Easier maintenance

Examples

Traditional banking systems
Library systems
School management systems

Typical setup

Central server or mainframe
All branches connect to HQ
Single database
Centralized processing
One place for updates

Major drawback

Single point of failure

Distributed Computing

Multiple computers work together as one logical system. Processing, storage, and management are spread across multiple machines or locations.

Characteristics

Shared resources
Fault tolerance
High availability
Horizontal scalability
Load balancing

Example

Google Search
- Multiple data centers
- Distributed query processing
- Replication and redundancy

Monolithic

Single application where all functionality is packaged into one codebase.

Characteristics

One deployment unit
Shared database
Tightly coupled components
Single technology stack
All-or-nothing scaling

Advantages

Simple to build
Easy to deploy
Good performance
Lower initial cost

Disadvantages

Hard to scale selectively
Technology lock-in

Examples

WordPress
Early-stage applications (many start monolithic)

Microservices

Application built as independent, small services that communicate via APIs.

Characteristics

Independent services
Separate databases (often)
Loosely coupled
Different tech stacks possible
Individual scaling

Advantages

Scale only what is needed
Team autonomy
Technology flexibility

Disadvantages

Operational overhead
Higher complexity
Latency and distributed failures
Tooling sprawl if unmanaged

Cloud Computing

Cloud computing provides compute resources (servers, storage, databases, networking, software) over the internet with pay-as-you-go pricing.

Benefits

Cost savings
- No upfront infrastructure
- Pay for usage
- Reduced maintenance
Scalability
- Scale up or down on demand
- Handle traffic spikes
Accessibility
- Access from anywhere
- Global reach
Reliability
- Backups and disaster recovery
- Multi-region options
Automatic updates
- Security patches
- Managed services reduce ops work

Examples

Cloud storage
OTT streaming platforms

Service Models

SaaS (Software as a Service)
- Ready-to-use apps
- Examples: Gmail, Dropbox, Slack
PaaS (Platform as a Service)
- App runtime and developer platforms
- Examples: Heroku, Google App Engine
IaaS (Infrastructure as a Service)
- Compute, network, storage building blocks
- Examples: AWS EC2, Azure VMs

Edge Computing

Edge computing moves computation and storage closer to where data is generated, near or on IoT devices.

Benefits

Lower latency
Works with limited internet
Reduces bandwidth cost
Better privacy (data stays local)

Simple examples

Smart camera doing motion detection locally
Smart thermostat adjusting temperature locally
Factory robot making real-time decisions from sensors

Examples

Smart Home Security
- Local video processing
- Only sends alerts or clips to cloud
Tesla cars
- Local sensor fusion and obstacle detection
- Split-second decisions on device

Fog Computing

What it does

Aggregates data from multiple edge devices
Provides more compute than individual devices
Filters and enriches data before sending to cloud
Keeps latency lower than cloud-only systems

Examples

Smart building local server processing many sensors
Factory gateway analyzing multiple machines
Farm gateway coordinating multiple sensors and controllers

Cloud vs Edge vs Fog

Aspect	Cloud	Edge	Fog
Location	Central data centers	On/near device	Local network
Latency	High	Very low	Medium
Compute	Very high	Low	Medium
Storage	High	Very limited	Limited
Internet dependency	Required	Optional	Local network required
Data scope	Global	Single device	Multiple local devices
Typical use	Analytics, long-term storage	Real-time decisions	Aggregation, coordination
Example	AWS	Smart camera	Factory gateway