Before we dive into the world of distributed computing, it’s important to understand its building blocks: distributed systems and cloud computing. So, let’s take a look:
What is a Distributed System?
A distributed system is a collection of physically separated servers and data storage that reside across multiple systems worldwide. These components collaborate and communicate with the objective of being a single, unified system with powerful computing capabilities.
Examples of Distributed Systems
The internet (World Wide Web)
Telecommunication networks with multiple antennas, amplifiers, and other networking devices that 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, typically through a data center or a public cloud that store all the applications and data.
Cloud computing makes cloud-based software and services available on demand for users. Just like offline resources allow you to perform various computing operations, 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 data centers to store and run applications and services. A distributed cloud computing architecture, also known as a distributed computing architecture, is made up of distributed systems and clouds.
Content Delivery Networks (CDNs) that utilize geographically separated regions 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. 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 data centers or PoPs placed near end-users. With data centers located physically close to the source of the network traffic, applications serve users’ requests faster.
Distributed clouds utilize resources spread over a network, irrespective of where they have users.
Cloud architects combine these two approaches to build performance-oriented cloud computing networks that serve global network traffic with maximum uptime.
How Does Distributed Computing Work?
Distributed computing connects hardware and software resources to accomplish many things, including:
Collaboration to achieve a single goal through optional resource sharing
Manage access rights per user authority level;
Enable resources, to be open for further development
Achieve concurrency so multiple machines can work on a single process
Ensure that all computing resources are scalable and can operate faster when working with multiple machines
Detect errors in connected components so that the network stays fault-tolerant.
Advanced distributed systems include automated processes and APIs to help them perform better.
From the customization perspective, distributed clouds provide businesses with the ability to connect their on-premises systems to the cloud computing stack so that they can transform their entire IT infrastructure without discarding old setups. 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?
Distributed computing systems are becoming a basic service that all cloud services providers offer their clients.. Here is a quick list of its advantages:
Ultimate Scalability
All nodes or components of the distributed network are independent computers. You can easily add or remove systems from the network without resource straining or downtime.
Improved Fault Tolerance
Distributed systems form a unified network with the architecture allowing 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. As a result of this load balancing, processing speed and cost-effectiveness of operations can significantly improve.
Lower Latency
As resources are globally present, businesses can select cloud-based servers near end-users to reduce latency and speed up request processing. Companies reap the benefit of localized workloads together with the convenience of a unified public cloud.
Helpful in Compliance Implementation
For both industry compliance and regional compliance, distributed cloud infrastructure enables businesses to utilize local or country-based resources across different geographies. This way, they are able to comply with varying data privacy rules, such as GDPR in Europe or CCPA in California.
Broadly, we divide distributed cloud systems into four models:
Client-Server Model
In this model, the client directly fetches data from the server and then formats the data and renders it for the end-user. To modify this data, end-users directly submit their edits back to the server.
An example of this model is Amazon which stores customer information. When a customer updates their address or phone number, the client sends this to the server, and the server updates the information in the database.
The three-tier model introduces an additional tier between client and server: the agent tier.
This tier holds the client data and frees the client from needing to manage its own information. The client can access its data through a web application. As a result, the client application’s and the user’s work is reduced and is easier to automate.
An example is a cloud storage space with the ability to store files and a document editor. Such a storage solution makes files available anywhere through the internet, saving the user from the effort of managing data on a local machine
Multi-Tier Model
Enterprises need business logic to interact with backend data tiers and with frontend presentation tiers. This logic enables requests to multiple enterprise network services to be sent easily. That’s why large organizations prefer n-tier or multi-tier distributed computing model.
An example is an enterprise network with n-tiers that collaborates when a user publishes a social media post to multiple platforms. The post itself goes from the data tier to the presentation tier.
Peer-to-Peer Model
Unlike hierarchical client and server models, this model is comprised of peers. Each peer acts as a client or server, depending upon the request it is processing. They share their computing power, decision-making power, and capabilities to work in collaboration.
An example is blockchain nodes collaboratively working to make decisions regarding adding, deleting, and updating data in the network.
CDNs locate resources across geographies so users can access the nearest copy to fulfill their requests faster. Industries such as streaming and video surveillance get 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 ( that process data in a time-critical manner) must perform efficient data fetching, distributed machines greatly help such systems to work faster.
Multiplayer games with heavy graphics data (such as PUBG and Fortnite), applications with payment options, and torrenting apps are three examples of real-time applications where distributing cloud computing can improve user experience.
Using the distributed cloud platform by Ridge, companies can build a customized distributed system that has the agility of edge computing and the power of distributed computing.
And by facilitating interoperability with existing infrastructure, enterprises are empowered 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 for a single 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. Rather, it focuses on concurrent processing and shared memory.
For example, a parallel computing implementation could comprise four different sensors that are set to reveal medical pictures. The final image takes input from each sensor separately to produce a combination of those variants.
What makes distributed computing powerful?
Machines that work remotely on one task, improve the performance efficiency of distributed systems. Fault-tolerance, agility, cost convenience, and resource sharing make distributed computing a powerful technology.
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