Blog > Challenges at the Edge: Distributed Data

Challenges at the Edge: Distributed Data

As specialized edge databases become a necessity for distributed edge computing systems, so will connection to local, interconnected colocation data centers.

It can be tempting to fall into the trap of thinking edge computing will easily remedy the cloud’s shortcomings — limited bandwidth, latency issues, network dependencies, and so forth. But as advantageous as ultra-low latency and on-site data processing is, the edge is not a magic bullet that lets you simply pick up where the cloud left off. For your edge computing environment to operate successfully, you need to solve the problem of distributed data.

Stateless vs. Stateful Data

Edge computing is very easy when the data is stateless — that is, when an application doesn’t require the server to have a record of previous interactions with other devices, programs, or users. A calculator is a stateless application – it always starts with zero not storing previous calculations. Each interaction request can be completed based entirely on the information that comes with it. Other examples of stateless data include hypertext transfer protocol (HTTP), and domain name system (DNS).

Most applications that require edge computing are processing what is known as stateful data. This means the data comes with historical information of all previous events and interactions with other devices, programs, and users. Most desktop applications like Microsoft Office are stateful, as are mobile applications, IoT devices, virtual, and augmented reality. Applications dependent on complex systems to gather contextual information about end-users and their devices need the edge to deliver personalized experiences in a timely and coordinated manner.

Edge Systems are Highly Distributed

Edge computing systems are highly distributed. Multiple devices are processing information across a wide geographic area — even an entire smart city. In edge computing architectures, edge devices need to work independently to perform their own unique functions, but these devices also need to share and synchronize stateful data with other devices and nodes. The coordination of multiple edge devices working both independently and in parallel can be a challenge for designers of distributed systems.

Database Limitations 

Conventional databases rely on the centralized coordination of stateful data, which makes sense when your data is being processed in one place, such as a traditional data center. However, when you are processing vast amounts of data across multiple locations, you can’t rely on centralized coordination. Centralization can create the very problem edge computing is designed to solve — latency.

To guarantee consistency, conventional databases have coordination protocols in place. Databases at different points of presence (PoPs) across an edge computing system may go idle, waiting until another edge node sends a signal that it has successfully completed a task. Rather than being able to perform its own function while simultaneously processing incoming stateful data, the device is forced to wait until the stateful data is processed and coordinated by another node before it can perform its function.

Edge Databases

Conventional databases scale-up to meet stateful data coordination challenges within a data center. But due to their outdated design, they can’t effectively scale-out across large, dispersed geographic areas. Therefore, the coordination of stateful data, becomes a constraining factor in how many devices can perform complex tasks in an edge computing system.

To support edge computing at scale, edge devices need to work together to minimize or, in some cases, eliminate the need for coordination among all devices. Edge devices can perform many operations without centralized coordination, but are forced to rely on centralization, since they are working with outdated databases. With next-generation edge databases old-fashioned coordination protocol effects will be mitigated allowing stateful data to be processed in a distributed fashion.

Edge databases are geo-distributed, multi-master data platforms that support multiple edge locations using a coordination-free approach. They guarantee consistency without requiring centralized forms of consensus and will arrive at a shared version of truth in real-time. These databases don’t require the restructuring of cloud applications to scale, nor do they require developers with specialized expertise.

Edge databases are pivotal in addressing data processing limitations and hold the key to unleash the power and promise of the edge, 5G, and IoT innovations. Specialized edge databases are a necessity for distributed edge computing systems, as is the connection to local, interconnected colocation data centers. Netrality’s interconnected colocation facilities located at key edge locations in Chicago, Houston, Kansas City, Philadelphia, St. Louis, and Indianapolis enable edge computing systems to meet and exceed tomorrows distributed data needs.

Contact us for more information.

More similar blogs

Quantum Computing

By the end of 2022, the global enterprise quantum computing market is estimated to be over $2.7 billion and is projected to reach about $11 billion by 2027.

The Metaverse: Your Alternate Reality?

In 1992, the science fiction novel Snow Crash by Neal Stephenson first spoke about the metaverse. He described it to be a world where humans were programmable avatars, interacting with each other in a virtual world — parallel to the physical world.  For many, the metaverse is futuristic, not tangible, or viable. Let’s reminisce about...

Data Centers: The Core, the Heart, and the Cutting Edge

Netrality’s colocation data centers, home to hundreds of the biggest networks around the globe, are the epicenter of connectivity in our markets. Peering at Netrality’s urban-located facilities provides Faster speeds, increased bandwidth, greater reliability, and lower costs.