Dear Anna Klimenko,

in very general terms, Grid Computing is the use of widely Distributed Computing Resources to achieve a common goal.

https://en.wikipedia.org/wiki/Grid_computing

The goals and idea of Grid Computing are explained in the following article:

https://de.scribd.com/document/159134946/Challenges-in-Grid-computing

Fog Computing refers to Computing for the use of widely distributed intelligent Sensors and Actuators at the Edge of the Internet of Things (IoT). An immense amount of widely distributed Sensors and Actuators at the Edge of the IoT can be imagined as Fog (a kind of Mimi Clouds) on the IoT.

https://www.researchgate.net/publication/344015774_Intelligent_IoT_IIoT_related_Illustrations

Best regards

Anatol Badach

Dear Anna Klimenko,

You may want to review some helpful information presented below:

Fog computing, edge computing, sensor networks, and grid computing are all computing paradigms that involve the distribution of computing resources. While they share some similarities, they have distinct characteristics and are designed to address different challenges. Here's a brief overview of each:

Key Differences:

• Fog and edge computing are more focused on bringing computing resources closer to the data source to address issues of latency and bandwidth, whereas grid computing is more about efficiently utilizing distributed resources for solving complex problems.
• Sensor networks are specialized networks designed for collecting and transmitting data from sensors in the physical world to a central processing unit, often with a focus on real-time data acquisition.
• While resource allocation models may share some common principles, the specific challenges and considerations for each paradigm are different. For example, in sensor networks, energy efficiency is often a critical concern, whereas in grid computing, load balancing and task scheduling are primary considerations.

In summary, while there are some similarities in resource allocation modeling across these paradigms, the specific requirements, challenges, and objectives differ, reflecting the distinct characteristics and goals of fog computing, edge computing, sensor networks, and grid computing.

Thanks a lot! A very comprehensive answer.:)))

The terms "fog computing" and "grid computing" are often used interchangeably, but there are some important differences between the two.

Fog computing is a distributed computing paradigm that extends the cloud computing model by bringing computation and storage closer to the edge of the network. This is done by using a "fog node" - a small, powerful computer that is placed closer to the devices that are generating data. This reduces the amount of data that needs to be sent to the cloud, which can improve latency and reduce bandwidth costs.

Grid computing is a distributed computing paradigm that uses a network of computers to share resources and work together on large tasks. This can be used to solve problems that would be too large or complex for a single computer to handle. Grid computing is often used for scientific research and other computationally intensive applications.

In general, fog computing is used for applications that require low latency and high bandwidth, such as real-time data processing and analytics. Grid computing is used for applications that require a lot of computing power, but do not require as low of latency or bandwidth as fog computing applications.

Here is a table summarizing the key differences between fog computing and grid computing:

Here are some examples of how fog computing and grid computing are being used today:

• Fog computing:Self-driving cars: Fog nodes can be used to analyze sensor data from the car's environment in real time, which can help to improve safety and efficiency. Smart homes: Fog nodes can be used to control smart home devices, such as lights and thermostats, which can help to save energy and improve comfort. Industrial automation: Fog nodes can be used to monitor and control industrial equipment, which can help to improve productivity and reduce downtime.
• Grid computing:Scientific research: Grid computing is often used to solve complex scientific problems, such as simulating climate change or modeling the human brain. Drug discovery: Grid computing can be used to analyze large amounts of data to identify new drug candidates. Financial modeling: Grid computing can be used to run complex financial models to assess risk and make investment decisions.

Thanks! :)