Distributed Cloud Computing and its Examples in Business

What is Distributed Cloud Computing?

Distributed Cloud Computing is a cloud system that incorporates the physical location of cloud-based services. However, locations were privy to the scope and definition of cloud computing until recently. Moreover, a distributed cloud computing system originates from the concepts of public clouds, hybrid clouds, and edge computing.

According to Tanenbaum, Van Steen editors of the book “Distributed Systems-Principles and Paradigm” states, “A distributed system is a collection of independent computers that appears to its users as a single coherent system”. 

Further, distributed computing refers to the use of distributed systems that solve one large problem by dividing it into multiple tasks. Therefore, an individual computer that connects and communicates into networks computes and evaluates the tasks. Hence, the connected computers in the network communicate with one another to complete individual tasks.

A distributed computing also expands computation tasks and activities across multiple servers. It also generalizes the process within the cloud infrastructure. Moreover, its components arrange in geographical locations as it is an execution environment. Hence, here are the following requirements to execute distributed cloud computing:

• Location: Especially when the latency is critical and a large set of data needs transferring it is important to distribute applications in different locations for more responsive and performant delivery.
• Regulations: It is also important to maintain compliance while transferring the data and to make sure it reaches the appropriate set of users.
• Security: Moreover, when it comes to certain data and processes, businesses must manage and monitor them in a private cloud or a data center that integrates with a public cloud. 
• Redundancy: Most importantly, businesses must mitigate large-scale outages by only incorporating local, regional, and national site redundancies.

Key Characteristics and Features of Distributed Computing:

It is pivotal to understand the key features and characteristics that define distributed computing:

• Scalability: Firstly, it is important to understand that a distributed computing system must expand with the development and growth of the tasks. Moreover, it is an important feature that requires additional processing units or nodes in the network.
• Concurrency: It is also important to comprehend that the components in a distributed system run concurrently. They do not function on a “global clock”, that is to say, they execute tasks that occur out of sequence and a various rates.
• Availability: In case there is a failure in one of the nodes, the other nodes continue to function. Hence, the execution takes place easily without disrupting the complete computation.
• Transparency: An external programmer or an end-user views the system as one computational unit. Therefore, it enables users to associate with a logical device and avoid concerns regarding the system architecture.
• Heterogeneity: It is also important to know that the components in a distributed computing system are non-contemporary. As a result, this enables it to expand and incorporate additional components.
• Replication: Most importantly, distributed systems also allow users to share and transfer information and messaging. Therefore, this ensures consistency within resources like software and hardware components.

How does Distributed Computing Work?

As we see, distributed computing evolves with time and requirements. Moreover, its applications primarily cater to execute tasks and processes using the internet, and more specifically the cloud.

It is also important to understand that distributed computing includes application components across various networks. Therefore, it is pivotal for these components to communicate through messaging or APIs. Hence, this helps with the objective of improving comprehensive application performances and optimizing computing efficiencies.

Further, distributed computing takes a leap forward by distributing the complete stack of a public cloud provider according to the requirements. It then sets a data center, a private cloud, or an off-premises data center separate from the cloud provider. 

As a result, distribute cloud computing expands the centralized cloud by geographically allocating micro-cloud satellites. Although, the cloud provider maintains and monitors the operations, updates, compliances, security, and reliability of the distributed environment. 

That is to say, the customer retains access over all the cloud services, satellites, etc as a sole cloud. Hence, the customer manages and controls all operations and executions from one control panel.

Types of Distributed Cloud Computing

Here are the three primary types of Distribute Cloud Computing Systems:

• Information Systems:

It refers to the objective of distributing information throughout the systems in various servers through different communication models.

• Pervasive Systems:

It refers to the distributed system that incorporates embedded computing devices. Moreover, pervasive systems are more unstable in comparison to“traditional” distributed systems.

• Computing Systems:

This type of system implies the use of computers that connect to a single network to transfer messages and information to monitor activities.

Here are the Examples of Distributed Cloud Computing:

Distributed Cloud Computing Examples in Edge Computing

Distributed cloud and Edge Computing often support simple multi-cloud management, scalability improvement, development velocity. Moreover, distributed cloud helps deploy modern automation and decision-making applications and functionality. 

Moreover, it improves hybrid and multi-cloud visibility and manageability with the infrastructure. It also helps businesses attain considerable control over the infrastructure with the help of a single set of tools.

Above all, it also helps save cost by enabling businesses to expand within the existing environment. Further, it uses edge locations to avoid physical buildouts to develop and deploy that tasks faster using the same tools and personnel.

One of the most notable examples of distributed cloud computing in edge helps with traffic monitoring, QoS, and Data Security. It also offers solutions for a better and environment-friendly computation by exploring the use of renewable energy.

Automotive Industry Examples of Distribute Cloud Computing

There is an increase in the development of semi-autonomous and self-driving cars with the usage of distribute cloud computing. Moreover, the primary thought to build these vehicles is to gather data in real-time, access traffic information, and process and assess data to make quick decisions.

Further, cars in the future would work on technologies like AI to analyze data and for decision intelligence features. It will also use 5G to capture the data in real time. Hence leveraging distribute cloud computing enables better opportunities for faster data transfer and more accurate decision-making. Moreover, the projects by Tesla are real-life examples of using distribute cloud computing.

Distributed Cloud Computing Examples in Healthcare

Distributed Cloud Computing also brings in the opportunity to analyze and present large data sets. Therefore, this is a great opportunity with the rise of data in healthcare. Moreover, there can be endless possibilities for the Healthcare industry with distribute cloud computing.

For example, with distribute cloud computing, healthcare professionals can use hybrid clouds and edge computing to monitor both in-hospital and at-home patients. Further, it can help track and monitor symptoms and conditions using IoT-based applications and devices.

Content Delivery Networks Examples of Distributed Cloud Computing

CDN, similar to the Internet is also powered by the edge and distribute cloud computing technologies. Further, two of the most prominent examples of distribute cloud computing are Netflix and YouTube. 

The online platforms use the technology along with the microservice architecture for a faster transfer of larger data sets. Moreover, it also uses AI to offer content recommendations for its users.