Distributed Computing Paradigm

A model where software components are shared across multiple computers for efficiency and performance.

A group of tightly coupled computers working as a single system via high-speed LANs.

A distributed network of loosely coupled computers used for large-scale computational tasks.

On-demand delivery of computing resources and services over the internet.

Enhanced performance by handling larger and more complex computations.

Cost efficiency through resource sharing.

By using redundancy and fault tolerance mechanisms.

It can easily expand to meet increasing demand without major changes.

It supports a wide range of applications from research to commerce.

Simulations, data analysis, and modeling in physics, biology, and climate.

Processing large datasets using distributed platforms.

Providing computing resources on demand.

Distributing web content globally for fast access.

Real-time trading, risk analysis, and fraud detection.

Medical data analysis and telemedicine.

Online shopping platforms and payment processing.

A distributed network where nodes act as both clients and servers without central control.

Processing data near the source of generation to reduce latency and bandwidth usage.

Extending cloud computing closer to the edge of the network for distributed infrastructure.

A machine or device that participates in computation, storage, and communication.

The medium for communication between nodes, such as LAN, WAN, or Internet.

The layout or arrangement of nodes in a network.

All nodes connected to a single communication line.

Nodes connected to a central hub.

Each node connected to two others in a closed loop.

Nodes are interconnected, allowing multiple data paths.

A combination of two or more network topologies.

Reliable data transmission in distributed systems.

Low-latency communication without guaranteed delivery.

Remote Procedure Call allowing execution on remote nodes.

Message Passing Interface for communication in parallel computing.

Protocols for communication in web services.

Clients request services from centralized servers.

Divides applications into presentation, logic, and data layers.

An architecture where components provide reusable services over a network.

Applications structured as independent, loosely coupled services.

A model where components communicate by producing and consuming events.

A system managing and storing files across multiple servers for reliability and access.

A distributed file system designed for high throughput access to large datasets.

A scalable distributed file system optimized for large data streaming.

Storing copies of data on multiple nodes for availability and fault tolerance.

Data copies updated immediately across nodes.

Data copies updated later, allowing delays.

Dividing a database into smaller pieces to improve scalability and performance.

Data divided into ranges of values.

Data divided using a hash function.

Data divided according to a predefined list of values.

A decentralized ledger that records transactions across many computers.

A process ensuring agreement on blockchain transactions.

Cryptocurrency and smart contracts.

Enhanced security and transparency.

Scalability and energy consumption.

Integration of connected devices with distributed systems.

By processing data near IoT devices to reduce latency.

By extending cloud services closer to IoT devices.

Smart cities and industrial automation.

Interoperability and data security.

Training machine learning models across decentralized devices without centralizing data.

Neural networks trained across multiple nodes in a distributed system.

Improved real-time decision making and scalability.

Data privacy and resource management.