When you think about the U.S. Postal Service, you likely imagine your local post office and friendly mail carrier. But the reason the postal service works on its scale is that it’s a massive and complex logistics system made up of industrial distribution centers, shipping hubs, and a large fleet of trucks, airplanes, semitrailers, boats, helicopters, and even mules.
Your local mail carrier is simply the most efficient way for you — the end consumer — to get your deliveries. It’s a much faster experience, for example, than going to a 100,000-square-foot distribution center lined with endless rows of P.O. boxes. And it’s certainly more efficient than having your mail delivered by an 18-wheeler.
Much like the postal service, modern IT environments are extremely complex. They’re massive webs consisting of applications, databases, and devices across infrastructures around the world. And these environments are only becoming more complex thanks to the move to the cloud and digital transformation efforts meant to improve productivity and efficiency.
Embracing the cloud has been crucial for modern enterprises as it increases scalability and ensures employees have access to their work environments. But it’s also led to latency — or a delay in the time it takes for data to transmit — and security concerns as data moves in and out of the four walls of the office. The network perimeter has completely broken down. In practice, this means significantly more data now exists and moves through the cloud.
This process has created large cloud environments where much of the actual workload exists. To support this, many companies deploy virtual machines, which can introduce packet delays if they exist on separate networks.
In addition to the increased likelihood of latency issues, cloud computing makes it harder to even track latency. This is because many of the tools used to monitor latency (like pings) are based on ICMP packets, a protocol used most commonly on network devices, which won’t suffice in cloud environments. These virtual machines can leave you with slower environments and also leave you without tools to provide insights into the issue. This problem has led companies to rethink where their devices exist, and many are now moving to the edge.
Edge Computing + Observability
- Edge computing is a practice in which devices process data closer to where it is needed rather than sending it back to a central server in the cloud.
- Observability provides visibility across distributed, hybrid and multi-cloud IT environments by using machine learning and artificial intelligence to manage complex IT environments easily.
- Together, these two practices allow companies to efficiently process data while monitoring for any problems and quickly intervening when issues arise.
Embracing Edge Computing
By prioritizing localization and speed, many enterprises now deploy the same model in their IT operations that postal services do. In IT, this is called edge computing, where data is processed closer to where it’s needed rather than sending it back to a central server in the cloud. In edge computing, devices — from internet of things (IoT) gateways and smart displays to drones and sales terminals — have enough memory and computing power to process data and execute it without having to send everything back through the cloud.
One of the most important benefits of edge computing is faster data processing. Moving data processing to the “edge,” or as close to the devices generating the data as possible, helps increase the speed with which data can be processed in real-time. This system is especially beneficial in situations where there are networking constraints and processing is time sensitive.
Edge computing also lowers bandwidth requirements for enterprises. By minimizing the need for long-distance communications between the server (or the central hub) and the machines receiving information, edge computing helps decrease latency and bandwidth usage. Additionally, edge computing provides greater flexibility. When data is processed closer to where it’s generated, computing processes can be altered to more quickly adapt to any necessary changes.
Though edge computing has solved many of the challenges related to the move to the cloud, the story doesn’t end there. The process itself has caused other issues that enterprises must overcome. For example, edge computing requires a more distributed infrastructure, which adds even more complexity to company IT environments. And the vast amount of data generated by a network of distributed devices makes it difficult to understand the information being produced. This makes monitoring environments and identifying and remediate potential issues even more difficult.
Observability Brings Visibility to the Edge
Thankfully, observability offers a solution to help enterprises gain the efficiency of edge computing without losing visibility. Observability analyzes massive amounts of information across an IT environment and pinpoints the causes of outages or performance issues. Observability solutions can also generate actionable insights to resolve these problems quickly, which is critical to understanding complex IT environments. Observability helps ensure ongoing availability and reliability and can help identify bottlenecks in the network, troubleshoot problems, and optimize system performance.
The postal service example can help demonstrate how observability works. Without monitoring, the post office would have no way of knowing what packages are being processed where, how long it takes to process them, or if there are delays in the system. Without this information, the entire system runs the risk of losing, misrouting, or delaying packages. This is why the postal service actively monitors the location and status of every package they process.
Observability works the same way in an IT environment by providing single-pane-of-glass visibility into the enterprise and giving teams access to real-time information. With observability, organizations can better monitor network traffic, identify potential security threats, detect anomalies, and optimize system performance. This reduces downtime, improves security, and ensures high availability and reliability. By gaining these key insights, teams can more clearly see and understand the edge computing systems in their network and work to quickly resolve issues.
Edge computing is one of the most effective models for companies to adopt. When deployed in tandem with observability, teams can better understand and address potential issues quickly and efficiently. By combining edge computing with observability, enterprises can ensure greater productivity and efficiency.