As many of us are seeing across every industry, the promise of edge computing is no longer hypothetical. It’s here now, and it’s bringing data storage and analysis closer to the actual source of that data, enabling faster response times for accessing, analyzing, and acting on real-time events. This is driving new applications that allow companies to monitor people, places, and devices — at any time — across diverse physical-digital environments to fundamentally change how organizations deliver near real-time awareness, efficiencies, safety, and sustainability.
The edge is a dynamic environment consisting of a network of sensors, devices, and legacy enterprise systems integrated to power next-generation business applications. It provides real-time visibility into events that allow companies to avoid the potentially long-lasting and devastating impact of a negative situation when coupled with the ability to respond immediately. From better managing operational processes, resources, and materials to preventing and mitigating accidents before they cause substantial damage, the ability to see when problems arise and immediately respond to these critical events is a business imperative. Unsurprisingly, the mission critical applications that run at the edge can be highly complex.
Edge applications are not standalone applications. Instead, they are part of larger, distributed applications that run across various network hosts — from edge devices and local systems to the cloud. To build a successful edge-native application, you need to determine not only where to place mission critical workloads within this complex environment but also how to dynamically transfer these workloads between hosts to ensure optimal performance. However, deciding where to run the various parts of the application and what to focus on when optimizing the deployment can be a significant challenge.
To optimize mission critical applications for the dynamic edge, you need to focus on three crucial characteristics.
While the centralized cloud systems used by most organizations provide ease of access and collaboration, the centralization of servers means they are remote from data sources — and data transmission introduces delays caused by network latency.
For mission critical applications, software computations often require near real-time response to input from a device — within milliseconds. It would be best to run these applications as close to the device as possible. Functionality that is less latency sensitive — for example, where you can afford to wait two to three seconds for a response — can remain in the cloud. This approach has the added benefit of potentially lowering your total costs by taking advantage of specialized compute capabilities within these environments.
As moving data between computing elements costs time and money, you always want to move less data whenever possible, especially for mission critical applications. This can be done by collecting and processing the data at the edge and reducing it before transmitting it to other computational centers. Sending only the results to the cloud requires significantly less data transmission bandwidth than the raw data.
Compute and storage costs
Compute and storage costs vary by location. Cloud computing is typically considered inexpensive and can provide an inexhaustible supply of compute power as resources can easily be increased or decreased to meet changing demands. Unfortunately, there are still substantial costs associated with getting the data to the cloud in addition to bandwidth and latency considerations.
While edge computing can meet the real-time compute needs of these applications, it is limited by a preset amount of computing resources. There is no vertical scalability. However, a distributed approach to application deployment can help. By using edge processing to address the time-sensitive parts of the application and filter out all expendable data, you can get the best of both worlds, limiting expensive computing and bandwidth costs.
Taking a “develop once, deploy anywhere” approach is also a key factor when optimizing applications for the edge. Using partitions for the various software components will allow you to allocate the most appropriate hosts within your computing environment. While optimizing performance across multiple computing elements can be challenging, as it’s difficult to accurately predict the behavior of the resulting system until it is deployed on a real-life workload, it will be worth the effort. Doing this will enable you to optimize the latency, bandwidth, and compute and storage costs of your mission critical applications more efficiently and easily.