What Is Distributed Computing?

This computational method performs tasks in parallel from multiple computers in disparate locations.

Written by Brooke Becher
What Is Distributed Computing?
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UPDATED BY
Brennan Whitfield | Apr 10, 2024

Distributed computing is when multiple interconnected computer systems or devices work together as one. This divide-and-conquer approach allows multiple computers, known as nodes, to concurrently solve a single task by breaking it into subtasks while communicating across a shared internal network. Rather than moving mass amounts of data through a central processing center, this model enables individual nodes to coordinate processing power and share data, resulting in faster speeds and optimized performance.

Distributed Computing Definition

Distributed computing is a computational technique that uses a network of interconnected computer systems to collaboratively solve a common problem. By splitting a task into smaller portions, these nodes coordinate their processing power to appear as a unified system.

Distributed computing is particularly useful for handling tasks that are too large or complex to be handled efficiently by a single computer, such as big data processing, content delivery networks and high-performance computing.

As data volumes and demands for application performance increase, distributed computing systems have become an essential model for modern digital architecture.

 

What Is Distributed Computing?

Distributed computing uses a network of interconnected computer systems (or nodes) to perform big, complex tasks by splitting them into smaller portions and distributing them among multiple computers. These machines then communicate with each other and coordinate shared resources to execute tasks, process data and solve problems as a unified system.

This decentralized technique is used to tackle jobs too complex for single-node systems, explained Shashwat Kapoor, a data engineer at Lyra Health.

“Imagine you have a massive amount of data, like all the photos on Instagram — analyzing this data with just one computer would take forever,” Kapoor told Built In. “In this case, distributed computing could be used to significantly speed up the analysis by employing the use of multiple computers within a network.”

Cloud platforms, blockchain, search engines, peer-to-peer networks — even the internet itself — are some examples of distributed computing in action. Regardless of geographic location, each individual node stays in constant communication.

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Types of Distributed Computing Systems

Below are some of the most common types of distributed computing system architectures:

Client-Server 

A client-server model divides tasks between “client” and “server” nodes, where clients initiate requests (representing inputs) that are fulfilled by the servers (representing outputs). These requests involve asking a server to complete a certain task or allocate resources. Upon completing the request, the server will send a response back to the client. Client-server is often applied for web browsing, email systems and database operations.

Three-Tier

These models are structured in three tiers, with each responsible for specific functions. Typically, these are assorted as a presentation tier (that acts as a user interface), an application tier (that processes data) and a data tier (that stores data). Three-tier is often used for web and online applications, and allows developers to manage tiers independently without changing the whole system.

N-Tier

An n-tier or multitier model splits an application into multiple layers — typically more than three — each with an assigned function. These systems follow a similar model to three-tier systems but offer more complexity, as it’s able to contain any number of network functions. N-tier models tend to be used for web applications or data systems.

Peer-to-Peer

A peer-to-peer model is a decentralized computing architecture that shares all network responsibilities equally among each node participating in the network. It has no system hierarchy; every node can function independently as both the client and server, and carries out tasks using its own local memory base. Peer-to-peer models allow devices within a network to connect and share computing resources without requiring a separate, central server.

 

Advantages of Distributed Computing

Efficiency

Distributed computing systems recruit multiple machines that work in parallel, multiplying processing potential. This method of load balancing results in faster computational speed, low latency, high bandwidths and maximal resource use of underlying hardware.

“Distributed computing allows for the quick movement of massive volumes of data,” Kapoor said. “The faster that data is processed and sent back out, the quicker the system can operate.”

Reliability

Distributed computing systems are fault-tolerant frameworks designed to be resilient to failures and disruptions. By spreading out tasks and data across a decentralized network, no one node is vital to its overall function. Even if individual nodes fail, distributed systems can continue to operate.

“A distributed computing system comes with the guarantee that the failure of one node does not damage the entire system,” Adora Nwodo, a former Microsoft software engineer and founder of non-profit NexaScale, said. “It just means that the process will be routed to another node, allowing the system to continuously operate.”

Scalability

Expanding a distributed computing system is as simple as adding an additional node to the existing network. These highly configurable systems feature scale-out architecture, designed to scale horizontally in order to handle increasing workloads or heftier datasets.

Low Cost

Distributed computing infrastructure typically features off-the-shelf, commodity hardware. It’s compatible with most devices currently in circulation, and commonly programmed with open-source software or pay-as-you-go cloud platforms that are shared across the network. Compared to monolithic systems, distributed computing models deliver more for less.

 

Disadvantages of Distributed Computing

Complexity

Distributed computing systems are more complex than centralized systems in everything from their design to deployment and management. They require coordination, communication and consistency among all in-network nodes, and — given their potential to include hundreds to thousands of devices — are more prone to component failures.

And, as the number of parts in a system increases, so does the rate of human error: “If a developer unintentionally deletes a critical file, the entire system could become unusable,” Kapoor said. “By introducing multiple computers in a network, we are increasing the risk of human error as well.”

Maintenance

Managing communication and coordination between nodes may render possible failure spots, Nwodo said, resulting in more system maintenance overhead. Without centralized control, it becomes a matter of analyzing logs and collecting metrics from multiple nodes to even diagnose a performance issue, let alone fix it.

“This is where we have to discuss tradeoffs for availability and consistency when designing our systems,” Nwodo added. “Due to the decentralized nature of the architecture, debugging and troubleshooting in distributed systems are also more complex.”

Security Vulnerabilities

The total number of nodes needed to run Google’s search engine and Facebook’s platform — archiving the digital presence of its three billion users — is undisclosed. A best guess may range conservatively from tens of thousands to the millions. While mighty, expansive systems are more susceptible to cyberattacks due to their increased attack surface. With each additional device comes more potential entryways into the shared network, and possibilities to intercept messages as they move from one node to the next.

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Examples of Distributed Computing

In the modern age of tech, it’s hard to imagine a system that doesn’t rely on distributed computing methods. Scientific simulations, weather forecasting, data analytics and content delivery networks are some examples with particular use cases for this method, said Mayank Jindal, a machine learning and software development engineer at Amazon building cloud-based softwares.

“Distributed computing is useful in scenarios where tasks or data processing demands exceed the capabilities of a single computer or require redundancy for fault tolerance,” Jindal told Built In.

More common examples of distributed computing include the following:

  • Networks: Ethernet and local area networks let computers share resources and communicate within a finite area. Distributed computing networks, like email and the internet, allow local data exchange, remote access and collaboration among different users and devices.
  • Telecommunication networks: Telecommunication networks use interconnected nodes to transmit data, voice, video and other forms of information over broad distances. Signals travel through cabled infrastructure or wireless systems, like Wi-Fi or satellite, and enable communication across different geographic locations.
  • Real-time systems: Real-time systems respond to inputs within a specified time frame. Its function is to promptly produce logical computations, and are implemented across airline, ride-sharing, financial trading and online, multiplayer gaming platforms.
  • Parallel processors: Parallel processing is a type of distributed computing that happens within one piece of hardware. Complex computational tasks are split among two or more processors, which simultaneously execute separate pieces of a single task to reduce a program’s run time. Today, it’s common for laptops to have at least four computer processing units.
  • Distributed database systems: These models focus on storing and managing data across multiple interconnected databases or nodes, and delivering a unified view of this information to an end user. This method improves availability, scalability and fault tolerance while ensuring consistency and integrity of the requested data.
  • Distributed artificial intelligence: Distributed artificial intelligence is a subfield of AI research where multiple, AI-powered autonomous agents team together to pull insights and predictive analytics from large-scale datasets and make data-driven decisions. This approach can reduce AI model training time and improve their interpretability. Swarm robotics is an example of DAI.

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Distributed Computing Use Cases

Distributed computing offers a multi-disciplinary approach to communication, real-time sharing, data storage and balancing workloads. Below are some examples of how these versatile systems are applied across varying industries.

  • Healthcare: Distributed computing enables efficient processing, storage and analysis of large volumes of medical data. Healthcare providers are better equipped to exchange electronic health records, medical images and other information with colleagues, hospitals, clinics and laboratories on behalf of their patients.
  • Engineering: Across different engineering disciplines, distributed systems are used to solve complex problems, perform simulations, optimize and explore designs as well as analyze large datasets.
  • Financial services: Common financial practices high-frequency trading, risk management, fraud detection, algorithmic trading, portfolio optimization and quantitative analysis — all use distributed computing, leveraging its high-speed networks, low-latency messaging protocols and co-location facilities.
  • Energy: Within the energy sector, distributed computing systems are used to maximize energy generation, distribution and consumption. Their real-time data analytics, forecasting algorithms and distributed control algorithms also boost grid reliability and help integrate renewable energy sources.
  • Education: Distributed computing models support online learning platforms and virtual labs with easy sharing, remote access and reliable, real-time access to resources as well as the administrative systems that keep educational institutions in operation.

 

How Does Distributed Computing Work?

In order for distributed computing to work, a network of interconnected nodes must share resources to execute tasks. Nodes operate autonomously and can come in the form of laptops, servers, smartphones, IoT devices and tablets.

When a request is made, nodes break down a task (or piece of data) into smaller segments. These “subtasks” are then distributed among the network, depending on a node’s programmed responsibilities. Each node serves as an endpoint within the network, and independently processes their assigned portion.

During this process, communication protocols enable nodes to send messages, share information and synchronize their activities as needed. Once all nodes solve their portion of the overall task, the results are collected and combined into a final output. This process filters through whatever architecture a distributed computing system is formatted in. 

 

Parallel Computing vs. Distributed Computing

Parallel computing is a technique that splits a task into smaller segments, and processes it within a single machine. Internally, these subtasks are divided up among the computer’s CPUs or cores, then tackled in parallel.

Similarly, distributed computing also deconstructs tasks into smaller segments; however, the workload is distributed across a number of interconnected nodes — such as computers, servers, smartphones and even IoT devices — that work independently toward a common goal.

While both computational methods share similarities, they differ in their architecture and execution: Parallel computing takes place within one computer, and distributed computing spreads across a cluster of networked machines that are often in different geographic locations.

Grid Computing vs. Distributed Computing

Like distributed computing, grid computing utilizes multiple computing resources that are spread across different locations to solve computational tasks. However, distributed computing can involve various types of architectures and nodes, while grid computing has a common, defined architecture consisting of nodes with at least four layers. Additionally, all nodes in a grid computing network use the same network protocol in order to act in unison as a supercomputer. Grid computing can be considered a form of distributed computing. 

“Distributed computing enables the efficient processing of vast amounts of data and complex tasks that would be impractical for a single machine,” Jindal said. “In the era of big data and cloud computing, it forms the backbone of many essential services and applications, impacting various aspects of our digital lives — from internet searches to online transactions and scientific research.”

Frequently Asked Questions

The most obvious example of a distributed system is the internet. Other everyday examples include peer-to-peer file-sharing platforms, such as BitTorrent, or multi-server models like the Google File System, which supports its search engine. 

Distributed computing involves the coordination of tasks across multiple interconnected nodes, whereas cloud computing acts more as an on-demand service to a node that pulls information from a centralized, shared pool of resources. 

Distributed computing coordinates tasks across a multi-node network, while parallel computing splits tasks across processors or cores within a single machine. 

The types of distributed computing systems include:

  • Client-server
  • Three-tier
  • N-tier
  • Peer-to-peer
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