What is Distributed Computing: Definition & Examples
Data CentersEdge Computing
28 Oct 2021
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Introduction to Distributed Computing
To understand the distributed computing meaning, you must have proper know-how of distributed systems and cloud computing. So, before we jump to explain advanced aspects of distributed computing, let’s discuss these two.
What is a Distributed System?
A distributed system is a collection of multiple physically separated servers and data storage that reside in different systems worldwide. These components can collaborate, communicate, and work together to achieve the same objective, giving an illusion of being a single, unified system with powerful computing capabilities.
A distributed computing server, databases, software applications, and file storage systems can all be considered distributed systems.
Examples of Distributed Systems
The internet (World Wide Web) itself.
Telecommunication networks with multiple antennas, amplifiers, and other networking devices appear as a single system to end-users.
What is Cloud Computing?
The cloud stores software and services that you can access through the internet. Companies who use the cloud often use one data center or public cloud to store all of their applications and data.
Cloud computing is the approach that makes cloud-based software and services available on demand for users. Just like offline resources allow you to perform various computing operations, big data and applications in the cloud also do — but remotely, through the internet.
Distributed Computing Definition: What is Distributed Cloud?
In a distributed cloud, the public cloud infrastructure utilizes multiple locations and data centers to store and run the software applications and services. With this implementation, distributed clouds are more efficient and performance-driven.
A distributed cloud computing architecture also called distributed computing architecture, is made up of distributed systems and clouds.
Content Delivery Networks (CDNs) utilize geographically separated regions to store data locally in order to serve end-users faster.
Ridge Cloud is a distributed cloud that can be deployed inany location to give its end users hyper-low latency.
Distributed Computing vs. Cloud Computing
What is the role of distributed computing in cloud computing? Distributed computing and cloud computing are not mutually exclusive. In fact, distributed computing is essentially a variant of cloud computing that operates on a distributed cloud network.
Distributed Cloud vs. Edge Computing
Edge computing is a type of cloud computing that works with various data centers or PoPs and applications placed near end-users. With data centers located physically close to the source of the network traffic, companies can easily serve users’ requests faster.
Distributed clouds optimally utilize the resources spread over an extensive network, irrespective of where users are.
Cloud architects combine these two approaches to build performance-oriented cloud computing networks that serve global network traffic fast and with maximum uptime.
How Does Distributed Computing Work?
Distributed computing connects hardware and software resources to do many things, including:
Work in collaboration to achieve a single goal through optional resource sharing;
Manage access rights per the authority level of users;
Keep resources, e.g., distributed computing software, open for further development;
Achieve concurrency that lets multiple machines work on the same process;
Ensure all computing resources are scalable and operate faster when multiple machines work together;
Detect and handle errors in connected components of the distributed network so that the network doesn’t fail and stays fault-tolerant.
Advanced distributed systems have automated processes and APIs to help them perform better.
From the customization perspective, distributed clouds are a boon for businesses. Cloud service providers can connect on-premises systems to the cloud computing stack so that enterprises can transform their entire IT infrastructure without discarding old setups. Instead, they can extend existing infrastructure through comparatively fewer modifications.
The cloud service provider controls the application upgrades, security, reliability, adherence to standards, governance, and disaster recovery mechanism for the distributed infrastructure.
What Are the Advantages of Distributed Cloud Computing?
According to Gartner, distributed computing systems are becoming a primary service that all cloud services providers offer to their clients. Why? Because the advantages of distributed cloud computing are extraordinary. Here is a quick list:
All nodes or components of the distributed network are independent computers. Together, they form a distributed computing cluster. You can easily add or remove systems from the network without resource straining or downtime. Scaling with distributed computing services providers is easy.
Improved Fault Tolerance
Distributed systems form a unified network and communicate well. At the same time, the architecture allows any node to enter or exit at any time. As a result, fault-tolerant distributed systems have a higher degree of reliability.
Boosted Performance and Agility
Distributed clouds allow multiple machines to work on the same process, improving the performance of such systems by a factor of two or more. As a result of this load balancing, processing speed and cost-effectiveness of operations can improve with distributed systems.
As resources are globally present, businesses can select cloud-based servers near end-users and speed up request processing. Companies reap the benefit of edge computing’s low latency with the convenience of a unified public cloud.
Helpful in Compliance Implementation
Whether there is industry compliance or regional compliance, distributed cloud infrastructure helps businesses use local or country-based resources in different geographies. This way, they can easily comply with varying data privacy rules, such as GDPR in Europe or CCPA in California.
Under the umbrella of distributed systems, there are a few different architectures. Broadly, we can divide distributed cloud systems into four models:
In this model, the client fetches data from the server directly then formats the data and renders it for the end-user. To modify this data, end-users can directly submit their edits back to the server.
For example, companies like Amazon that store customer information. When a customer updates their address or phone number, the client sends this to the server, where the server updates the information in the database.
The three-tier model introduces an additional tier between client and server — the agent tier.
This middle tier holds the client data, releasing the client from the burden of managing its own information. The client can access its data through a web application, typically. Through this, the client application’s and the user’s work is reduced and automated easily.
For example, a cloud storage space with the ability to store your files and a document editor. Such a storage solution can make your file available anywhere for you through the internet, saving you from managing data on your local machine.
Enterprises need business logic to interact with various backend data tiers and frontend presentation tiers. This logic sends requests to multiple enterprise network services easily. That’s why large organizations prefer the n-tier or multi-tier distributed computing model.
For example, an enterprise network with n-tiers that collaborate when a user publishes a social media post to multiple platforms. The post itself goes from data tier to presentation tier.
Unlike the hierarchical client and server model, this model comprises peers. Each peer can act as a client or server, depending upon the request it is processing. These peers share their computing power, decision-making power, and capabilities to work better in collaboration.
For example,blockchain nodes collaboratively work to make decisions regarding adding, deleting, and updating data in the network.
CDNs place their resources in various locations and allow users to access the nearest copy to fulfill their requests faster. Industries like streaming and video surveillance see maximum benefits from such deployments.
If a customer in Seattle clicks a link to a video, the distributed network funnels the request to a local CDN in Washington, allowing the customer to load and watch the video faster.
Real-time or Performance-driven Systems
As real-time applications (the ones that process data in a time-critical manner) must work faster through efficient data fetching, distributed machines greatly help such systems.
Multiplayer games with heavy graphics data (e.g., PUBG and Fortnite), applications with payment options, and torrenting apps are a few examples of real-time applications where distributing cloud can improve user experience.
Distributed Systems and Cloud Computing
Distributed systems and cloud computing are a perfect match that powers efficient networks and makes them fault-tolerant. From storage to operations, distributed cloud services fulfill all of your business needs.
Using the distributed cloud platform by Ridge, companies can build their very own, customized distributed systems that have the agility of edge computing and power of distributed computing.
Ridge offers managed Kubernetes clusters, container orchestration, and object storage services for advanced implementations. Ridge Cloud takes advantage of the economies of locality and distribution.
As an alternative to the traditional public cloud model, Ridge Cloud enables application owners to utilize a global network of service providers instead of relying on the availability of computing resources in a specific location. And by facilitating interoperability with existing infrastructure, empowers enterprises to deploy and infinitely scale applications anywhere they need.
Frequently Asked Questions about Distributed Cloud Computing
What is the difference between distributed systems and distributed computing?
A distributed system is a networked collection of independent machines that can collaborate remotely to achieve one goal. In contrast, distributed computing is the cloud-based technology that enables this distributed system to operate, collaborate, and communicate.
Why do we need distributed computing?
Distributed computing results in the development of highly fault-tolerant systems that are reliable and performance-driven. Distributed systems allow real-time applications to execute fast and serve end-users requests quickly.
What is the difference between parallel and distributed computing?
Parallel and distributed computing differ in how they function. While distributed computing requires nodes to communicate and collaborate on a task, parallel computing does not require communication. Instead, it focuses on concurrent processing and shared memory.
For example, a parallel computing implementation could comprise four different sensors set to click medical pictures. The final image takes input from each sensor separately to produce a combination of those variants to give the best insights.
To take advantage of the benefits of both infrastructures, you can combine them and use distributed parallel processing.
What makes distributed computing powerful?
Machines, able to work remotely on the same task, improve the performance efficiency of distributed systems. The fault-tolerance, agility, cost convenience, and resource sharing make distributed computing a powerful technology.
Kenny Kleinerman, Head of Content | Ridge
As the Head of Content at Ridge, Kenny is in charge of navigating the tough subjects and bringing the Cloud down to Earth.