Cloud computing and distributed computing

Distributed computing and cloud computing look similar. However, cloud and distributed computing are different methods of computing efficiency.

A service provider provides cloud computing services via the Internet. These services would be accessible via the Internet by users. These services could include cloud-versions for programs that were previously available on-premise. These services include hardware, software, as well as infrastructure components.

Two computers are involved in cloud computing services. One computer is the host’s, while another one will be the user’s. Cloud computing involves multiple computers. This makes Cloud computing and distributed computing very similar.

Multiple computers are involved in distributed computing. These computers are not connected to the Internet as is the case with cloud computing. Computers are connected via a single network in distributed computing. This is the main difference between distributed computing and cloud computing.

Continue reading to learn more about Cloud computing and distributed computing, and to discover their differences.

What is Cloud Computing?

Distributed computing and cloud computing might seem very similar. Their setups could explain their similarities. They have multiple computers that work together as one system.

There are many differences between distributed and cloud computing. For example, cloud computing has its own unique features. These features are not available in a distributed environment. These are some features that cloud computing has to offer.

  1. Architecture. Cloud computing is distinguished by its structure. There are two major parts to cloud computing’s architecture. These two main components are the front and back ends. The cloud’s front end is what is visible to the user. This is the cloud as it appears for the customer. On the other side, the back end is the platform’s host. This is the cloud platform as it is seen by the service provider.These two major components of the cloud platform are connected by an important component. The Internet is this connecting component.
  2. Service Models. Cloud computing is different from distributed computing in that it has three major service models. These are Software as A Service, Platform as A Service, and Infrastructure as As a service. These models are abbreviated to IaaS, PaaS and SaaS. SaaS allows applications to be accessed via the Internet, rather than being installed. PaaS allows app developers to access their tools via the internet, rather than installing them. IaaS hosts infrastructure components like storage and servers through a third-party provider.
  3. Proprietorship. Comparing Cloud computing and distributed computing The cloud is composed of three major proprietorship models. These include the hybrid, private, or public clouds. Cloud platforms that can be shared with multiple entities are called public clouds. Public clouds are cloud platforms that can be shared by multiple entities. Private clouds, on the other hand are for the exclusive use and management of one entity. Hybrid cloud refers to a combination of public and private cloud components. Hybrid cloud is a combination of security and accessibility for the public.

What is distributed cloud?

Distributed cloud allows you to run your public cloud infrastructure at multiple locations, not just on your cloud provider’s cloud infrastructure, but also on premises in other cloud providers data centers or third-party colocation centers. You can manage all of it from one control plane.

This centrally managed, targeted distribution of public cloud services allows your business to deploy applications and individual components in the cloud locations and environments that meet your needs for performance and regulatory compliance. The operational and management issues that can arise in multicloud or hybrid cloud environments can be solved by distributed cloud.

Perhaps most importantly, distributed cloud is the perfect foundation for edge computing. It allows you to run applications and servers closer to where your data is created.

The demand for distributed cloud and edge computing is driven primarily by Internet of Things (IoT), artificial intelligence (AI), telecommunications (telco) and other applications that need to process huge amounts of data in real time. Distributed cloud can also help companies overcome the challenges of compliance with industry-specific data privacy regulations. It has been used to provide IT services to employees, end-users, and others affected by the COVID-19 pandemic.

How the distributed cloud works

Distributed computing is a form of computing in which components of an application are distributed across multiple computers and can communicate through messaging or APIs with each other. This allows for greater application performance and computing efficiency.

Distributed cloud goes a giant step further by distributing a public cloud provider’s entire compute stack to wherever a customer might need it – on-premises in the customer’s own data center or private cloud, or off-premises in one or more public cloud data centers that may or may not belong to the cloud provider.

Distributed cloud is essentially a provider’s cloud that includes micro-cloud satellites distributed geographically. The cloud provider maintains control over all operations, updates and governance of distributed infrastructure. The customer has access to all of it – the cloud provider’s centralized services and satellites, wherever they may be located – and can manage it all from one control plane. Gartner, an industry analyst, puts it this way: distributed cloud fixes with hybrid clouds and hybrid multicloud breaks.

Edge and distributed cloud computing

Edge computing is again the term used to describe the ability to locate and run application workloads as close to data sources as possible. This could be where users interact with their devices, such as mobile phones, barcode scanners or IoT devices like security cameras and machine sensors, collecting and generating data.

Edge computing allows you to “bring the math into the data” – instead of moving data to a central cloud data center, the computation is done where it’s created. Then, the data can be returned to the place where they are needed to support decision making or process automation. Edge computing is increasingly being regarded as essential for applications that need to process large amounts of data quickly or in real-time, and where low latency is crucial.

Edge computing could be implemented without a distributed cloud architecture. However, edge computing can be implemented without a distributed cloud architecture. This makes it much easier to deploy and manage applications.

Imagine multiple manufacturing plants with their own edge servers hosted by different cloud service provider, processing data from thousands of sensors. With distributed cloud, you can control and manage everything – such as deploying and managing Kubernetes clusters, making security updates, monitoring performance – from a single control plane, one dashboard and one set of tools from one cloud. These tasks and tools can vary depending on the location of the edge server.

Use cases for edge and distributed cloud computing

Distributed cloud computing and edge computing offer many benefits, including simplified multicloud management, improved scalability, development velocity, and deployment of state-of the-art automation and decision support apps and functionality.

  • Improved visibility and management of hybrid cloud/multicloud: By allowing visibility and management from a single console with one set of tools, distributed cloud can give organizations greater control over hybrid multicloud infrastructure.
  • Scalability and agility that is efficient, effective, and cost-effective Expanding a dedicated data center or building new locations in different regions can be costly and time-consuming. Distributed cloud allows organizations to expand to existing infrastructure and edge locations without the need for physical build out. They can also quickly develop and deploy any environment they want using the same tools as their staff.
  • It is easier to comply with local regulations or in the industry. Many data privacy regulations stipulate that personal information (PI), cannot be transferred outside of the country where it is stored. A distributed cloud infrastructure makes it easier for organizations to process PI in every user’s country. Data privacy regulations can be simplified by processing data directly at the source. This is especially true for healthcare, telecoms, and other industries.
  • Faster content delivery: A content delivery network (CDN), deployed on a distributed cloud, can improve streaming video content performance and user experience by storing and delivering video content closer to the end-users.
  • IoT, (AI) and machine learning applications: Real-time data analysis is essential for video surveillance, manufacturing automation and self-driving cars. These applications require low latency through distributed cloud computing and edge computing.


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