What is Distributed Cloud Computing?

Distributed cloud computing is the distribution of public cloud services across multiple geographic locations. In distributed clouds, the operations and governance—as well as updates—continue to remain under the purview of the primary public cloud provider.

In distributed computing, computation workload is spread across several connected servers. Distributed cloud computing takes the cloud computing model and distributes it to different geographic locations in a connected manner. It creates an execution environment where application components look at specific geographical locations that are chosen based on application needs. Some application requirements are:

Locational: To help enhance responsiveness and performance in delivery of applications, particularly those where latency is critical and transferring bulk data to a single cloud proves to be an expensive affair.

Regulatory: There are some countries where regulations stipulate that data must not leave the country. Distributed cloud computing helps in such cases.

Security and Control of Data: To make sure that an enterprise has the ability to retain specific data and processes in its private cloud, within its integrated public cloud.

Redundancy: To provide redundancy that goes beyond local, regional, and national site redundancy, which helps in mitigating large-scale outages.

Keep in mind that regardless of the use case, a distributed model implies that applications need to be connected together across multiple computer regions and domains. Therefore, integration must be a key component of a distributed computing strategy.

Similar Computing Systems

Fog and edge computing can be understood as extensions of distributed cloud computing.

With edge computing, data is collected and processed in close proximity to the point of generation. This is done with edge devices (devices that act as an entry point into enterprise networks). Applications that require low latency and a high throughput benefit greatly from edge computing.

Fog computing is an information technology architecture form that utilizes edge devices for computation, storage, and communication—all locally and over the Internet.

Both edge and fog computing can be considered extensions of distributed cloud computing. The two forms act as miniature data centers, are used for storage, and can be used to link to bigger cloud data centers for big data analysis and storage.

How Distributed Cloud Computing Works

Fundamentally, cloud computing and distributed cloud computing are the same. However, distributed cloud computing extends cloud computing across geographies. Distributed cloud computing splits one task across multiple computers at different locations—all of which are networked. Each computer will complete an aspect of the task, allowing for the task to be completed faster.

Cloud computing can help by allowing remote network access to hardware and software. This provides advantages such as resource sharing, scalability, cost reductions, and platform independence. However, distributed cloud computing is a network that has multiple computers working together to achieve an end goal. Every computer in this network performs a part of the overall task.

Cloud computing is where all necessary resources are accessed and delivered via the internet, whereas distributed cloud computing is a sharing of resources between multiple systems through a network. Each computing model comes with its set of unique benefits.

Users of distributed cloud computing can take advantage of extra features they can purchase. These features can include facilities for data to remain in a specified region or the setting of performance targets for latency and throughput. The onus of providing the infrastructure needed for this capability lies with the service provider. Most major distributed cloud service providers have technology they have developed to help with specific client requests and ensure transparency when doing so.

Distributed cloud computing is a trend aimed primarily at improving the functioning of businesses. Simply put, distributed cloud computing is the way forward for enterprises.

Use Cases of Distributed Cloud Computing

Intelligent Transport

Long-distance autonomously driven trucks can process data they receive from the dashboard and road sensors. This information is processed to help the vehicle maintain cruising speeds and ensure safe distances between other vehicles on the road. Simultaneously, the vehicle sends back information on performance and engine data to the main cloud. The route of the vehicle can be monitored by any association fleet management application that is placed in the cloud of specified geographical locations.

Intelligent Caching

A particular over-the-top (OTT) video service provider utilizes a central cloud to transcode and format videos for use on a range of devices over multiple networks. All content is cached in several formats. If there is a high anticipation level for a new release, the system will place the series in a cache that is closest to the end user. An example of this would be increased storage at servers in residential locations or at 5G stations in densely populated urban centers to make mobile viewing seamless.

Benefits of Distributed Computing

Here is a look at the benefits of distributing cloud computing:

Ideal for Scalability and Modular Growth

Since distributed cloud computing works on several machines, it can be scaled horizontally. Rather than update one system multiple times to handle increased loads, another machine can simply be added to ease the pressure. There is no limit to how many times this addition can be done. When loads are high, all connected machines can work on full capacity, and when the work reduces, machines that are not needed can be taken offline.

Higher Fault Tolerance and Better Reliability

By nature, distributed systems are much more fault tolerant compared to single machines. If an enterprise runs with 20 machines spread over five locations, work will not be stalled if one or even two of the locations face downtime. The reliability is therefore much higher. If a location does face downtime, the performance demand on the other centers automatically increases, ensuring there is no dip.

Low Latency

Users of the distributed cloud computing network will see that their traffic hits the location node closest to the point of data generation. This results in lower latency and enhanced performance. The only drawback here is that the system has to be designed to run on multiple nodes simultaneously, and this feature is complex and adds significantly to the cost of the service for the enterprise.

Cost Effectiveness

The investment in distributed cloud computing is much higher than setting up standalone systems. However, this is true only to a particular point, following which it becomes a service that is based on the economies of scale. In the long run, they are way more budget-friendly than large-scale centralized systems.

Efficiency

The distributed cloud system takes large complex data or problems and breaks them down into smaller bits, distributing the processing parallel across multiple computers. This reduces the amount of time taken to complete the task at hand and increases efficiency.

Disadvantages of Distributed Cloud Computing

There are pros and cons to every system. Here is a look at the downside of distributed cloud computing:

Complex to Implement

Considering the kind of work they achieve, distributed computing systems are complex to deploy as well as to maintain and troubleshoot when compared to more centralized systems. This increased complexity is not just related to the hardware but also the software that is needed to handle security aspects and communications.

Higher Investment

As mentioned earlier, investing in a distributed cloud computing system can be quite expensive initially. Adding on services and capacity for increased processing when required and for the handling of data transmission can add significantly to initial costs.

Security Concerns

Security of data in centralized systems is far easier than it is with distributed ones. The entire network has to be secured, and users also will need to have complete control over replicated data across different locations.

When it comes to large-scale projects, distributed computing works to improve performance, bringing the might of multiple machines. It is a scalable model and grows according to the needs of the workload it takes on. It may have some disadvantages, but scalability, better performance, and higher reliability tip the scales in its favor when it comes to large workloads and big data.