When to architect for the edge
Edge computing refers to geographically locating infrastructure in proximity to where data is generated or consumed. Instead of pushing this data to a public or private cloud for storage and computing, the data is processed “on the edge,” using infrastructure that can be simple commodity servers or sophisticated platforms like AWS for the Edge, Azure Stack Edge, or Google Distributed Cloud.
Computing “at the edge” also has a second meaning around the upper boundaries of performance, reliability, safety, and other operating and compliance requirements. To support these edge requirements, shifting compute, storage, and bandwidth to edge infrastructure can enable scaling apps that aren’t feasible if architected for a centralized cloud.
Mark Thiele, CEO of Edgevana, says, “Edge computing offers the business leader a new avenue for developing deeper relationships with customers and partners and obtaining real-time insights.”
The optimal infrastructure may be hard to recognize when devops teams are in the early stages of developing low-scale proofs of concepts. But waiting too long to recognize the need for edge infrastructure may force teams to rearchitect and rework their apps, increasing dev costs, slowing timelines, or preventing the business from achieving targeted outcomes.
Arul Livingston, vice president of engineering at OutSystems, agrees, “As applications become increasingly modernized and integrated, organizations should account for edge technologies and integration early in the development process to prevent the performance and security challenges that come with developing enterprise-grade applications.”
Devops teams should look for indicators before the platform’s infrastructure requirements can be modeled accurately. Here are five reasons to consider the edge.
What’s a few seconds worth on a manufacturing floor when a delay can cause injury to workers? What if the manufacturing requires expensive materials and catching flaws a few hundred milliseconds earlier can save significant money?
Thiele says, “In manufacturing, effective use of edge can reduce waste, improve efficiency, reduce on-the-job injuries, and increase equipment availability.”
A key factor for architects to consider is the cost of failure due to a failed or delayed decision. If there are significant risks or costs, as can be the case in manufacturing systems, surgical platforms, or autonomous vehicles, edge computing may offer higher performance and reliability for applications requiring greater safety.
Sub-second response time is a fundamental requirement for most financial trading platforms, and this performance is now expected in many applications that require a quick turnaround from sensing a problem or opportunity to responding with an action or decision.
Amit Patel, senior vice president at Consulting Solutions, says, “If real-time decision making is important to your business, then improving speed or reducing latency is critical, especially with all the connected devices organizations are using to collect data.”
The technological challenge of providing consistent low-latency experiences is magnified when there are thousands of data sources and decision nodes. Examples include connecting thousands of tractors and farm machines deployed with machine learning (ML) on edge devices or enabling metaverseor other large-scale business-to-consumer experiences.
If action needs to be taken in real time, start with edge computing,” says Pavel Despot, senior product manager at Akamai.
