Distributed Cloud: What it is and How it Facilitates Edge Computing

Edge computing and distributed cloud are two essential elements of digital transformation. As enterprises work to meet customer demand, reduce response times, and decrease data center costs, they need a new type of cloud that allows for rapid deployment and high performance computing. Edge computing is the process of collecting data at the source rather than in a centralized location, using artificial intelligence (AI) or another technique. Essentially, edge computing is about bringing compute resources as close to the final use case as possible. Distributed cloud is a collection of independent networks with their own data centers that have unique access to services from different providers. It’s an extension of standard private or public cloud infrastructure, but with added benefits for edge computing.

What is Edge Computing?

The concept of edge computing dates back to the 70s and 80s, but the phrase “edge computing” is a relatively new one. It represents a shift in data analysis and storage to a decentralized computing model where data resides as close as possible to the final use case. The goal is to reduce latency and increase the efficacy of AI models. The term “edge computing” is used to describe the process of collecting data at the source rather than in a centralized location. Data is collected at the edge in three main ways: By subscribing to middleware, pushing data to the edge through APIs, and deploying edge devices. The first two methods work at the application level and can be done by any organization. The last method requires specialized hardware and software.

What is Distributed Cloud?

Distributed cloud is a collection of independent networks with their own data centers that have unique access to services from different providers. A distributed cloud is an extension of standard private or public cloud infrastructure but with added benefits for edge computing. The distributed cloud allows distributed computing and storage to happen within a single cloud architecture context. The goal is to allow compute resources to be geographically distributed while maintaining high data security and governance levels. Distributed cloud architectures are typically built in one of two ways:

● Centralized distributed cloud — In this model, the cloud is distributed through a single fabric architecture but with multiple compute nodes in each data center.
● Multicloud distributed cloud — In this model, different cloud providers are integrated across multiple fabrics, each having its own compute nodes.

Distributed Cloud Use Cases

Distributed cloud provides centralized control of distributed resources, so it makes sense to use it for hybrid cloud deployment. A distributed cloud can also be used for disaster recovery, something that is becoming increasingly important as more businesses rely on cloud services. Another distributed cloud use case is for security, as it is designed to secure data in transit and at rest, which is particularly important for edge computing. Finally, the distributed cloud provides access to specialized resources that are otherwise inaccessible. For example, distributed cloud can be used to host a real-time data lake in the same way that a standard cloud is used.

Distributed Cloud Empowering Edge Computing

Distributed cloud is important for edge computing because it allows for the decentralized computing necessary for collecting data at the edge and the secure data transfer necessary for edge computing. Distributed cloud offers scalable, real-time data and compute services at a lower cost and with reduced latency when compared to traditional centralized cloud services. These benefits make distributed cloud a great fit for edge computing because it allows for decentralized processing, meaning the data can be collected at the edge and done securely.

Key Takeaways

The concept of edge computing dates back to the 70s and 80s, but the phrase “edge computing” is a relatively new one. It represents a shift in data analysis and storage to a decentralized computing model where data resides as close as possible to the final use case. The goal is to reduce latency and increase the efficacy of AI models. The term “edge computing” is used to describe the process of collecting data at the source rather than in a centralized location. Data is collected at the edge in three main ways: By subscribing to middleware, pushing data to the edge through APIs, and deploying edge devices. The first two methods work at the application level and can be done by any organization. The last method requires specialized hardware and software. Distribution cloud is a collection of independent networks with their own data centers that have unique access to services from different providers. It’s an extension of standard private or public cloud infrastructure but with added benefits for edge computing. Distributed cloud provides centralized control of distributed resources, so it makes sense to use it for hybrid cloud deployment. Distributed cloud can also be used for disaster recovery, as well as for security purposes.