Centralized vs Distributed System Architecture


Fig-01: Centralized vs Distributed System Architecture

Centralized System Architecture

A centralized system architecture refers to a computing architecture where all or most of the processing, data storage, and management are performed by a single central server or a mainframe. This central entity is responsible for handling all requests and serving all client devices connected to it. Here are the main characteristics and components of a centralized system architecture:

Characteristics

Components

  1. Central Server: Handles major processing and data storage tasks.
  2. Client Devices: Connect to the central server for processing and data access.
  3. Network Infrastructure: Typically involves local area networks (LAN) connecting clients to the central server.
  4. Data Storage: Centralized storage managed by the central server.

Examples



Distributed System Architecture

A distributed system architecture refers to multiple independent components (or nodes) work together to appear as a single coherent system to the end-users. These components are spread across different physical or virtual machines.

Characteristics

Components

  1. Nodes/Servers: Multiple independent servers or nodes handle processing and data storage.
  2. Network Infrastructure: Involves wide area networks (WAN) and internet connectivity for communication between nodes.
  3. Distributed Data Storage: Data is distributed across multiple nodes, often with redundancy to ensure availability and fault tolerance.
  4. Middleware: Software layer that manages communication, data consistency, and coordination between distributed components.

Examples



Comparison

Feature Centralized System Distributed System
—————————————————————–——————————————
Control Single central server Multiple independent nodes
Integration Tight integration, often local Loose integration, often geographically dispersed
Management Simplified management Complex management
Scalability Vertical scaling (hardware upgrades) Horizontal scaling (adding nodes)
Reliability Single point of failure Fault-tolerant, no single point of failure
Performance Low latency in small-scale operations High performance for large-scale operations; may face network latency
Examples Mainframes, client-server systems Internet services, peer-to-peer networks, cloud computing

In summary, centralized systems are simpler to manage and more straightforward in design but face challenges in scalability and reliability. Distributed systems offer better scalability and fault tolerance but are more complex to design, implement, and manage.