Distributed networks

From Computer Science Wiki
Web Science[1]

Distributed networking is a distributed computing network system, said to be distributed when the computer programming and the data to be worked on are spread out across more than one computer.[2]

“A Distributed System is a collection of independent computers that appear to its users as a single coherent system”[3]

Hardware used in a distributed networks[edit]

Distributed networks can be run on a wide range of hardware, from small devices like smartphones and Raspberry Pi's to large servers in data centers. The hardware used in a distributed network can vary depending on the specific needs of the network, such as the amount of data being processed or the number of users. Some common types of hardware used in distributed networks include:

Computers: Desktop computers, laptops, and servers can all be used to participate in a distributed network. These devices typically have more processing power and storage than smaller devices, and can be used to perform more resource-intensive tasks.

Smartphones: Many distributed networks, such as blockchain networks, allow users to participate using their smartphones. Smartphones are relatively powerful devices that can perform a variety of tasks, and they are widely available, making them an attractive option for distributed networks.

Internet of Things (IoT) devices: Distributed networks can also be run on small devices like sensors and smart home appliances, which are connected to the Internet and can communicate with other devices. These devices are often used to gather data from the physical world and transmit it to the network for processing.

Cloud servers: Distributed networks can also be run on servers located in data centers, which are connected to the Internet and can be accessed remotely. These servers are often used to provide the processing power and storage needed to support large distributed networks.

Graphics processing units (GPUs): Many distributed networks, particularly those involved in machine learning or data analysis, make use of GPUs to accelerate the processing of large amounts of data. GPUs are specialized processors that are designed to perform many calculations simultaneously, making them well-suited for tasks such as training deep learning models.

Field-programmable gate arrays (FPGAs): FPGAs are hardware devices that can be programmed to perform specific tasks. They are often used in distributed networks because they can be reconfigured quickly and can be more energy efficient than CPUs or GPUs for certain types of tasks.

Custom hardware: Some distributed networks may require the use of custom hardware, such as specialized sensors or devices with specific capabilities. These devices may be designed and built specifically for the needs of the network, or may be modified versions of existing hardware.

Overall, the hardware used in a distributed network can vary widely depending on the specific requirements of the network and the resources available.

An important distinction[edit]

Please remember, for the rest of your life, the image below, and the distinction between centralized, decentralized, and distributed networks. Although we are learning about web science in this article, this idea is used in many different areas of computer science. Image used from https://openclipart.org/detail/277506/Centralized-Decentralized-and-Distributed-Networks who have released this under the Creative Commons Zero 1.0 License


Do you understand this?[edit]

From the IB: Students should be aware of developments in mobile technology that have facilitated the growth of distributed networks.


These standards are used from the IB Computer Science Subject Guide[4]

  • Describe the range of hardware used by distributed networks.


  1. http://www.flaticon.com/
  2. https://en.wikipedia.org/wiki/Distributed_networking
  3. http://comp.ist.utl.pt/ec-ds/SistDist2IntroducaoAlunos.pdf
  4. IB Diploma Programme Computer science guide (first examinations 2014). Cardiff, Wales, United Kingdom: International Baccalaureate Organization. January 2012.