Edge computing is gaining traction as an enterprise IT strategy, as companies seek to bring storage and analytics closer to the data collection point, such as in IoT networks.
In terms of finances, edge computing has already arrived: according to IDC, “New IDC Spending Guide Forecasts Double-Digit Growth for Investments in Edge Computing”, firms will spend $176 billion on edge computing in 2022, up 15% from 2021.
In the end, it’s just a large number. In terms of architectural approaches, technical capabilities, enterprise use cases, security techniques, and more, there may be more qualitative evidence of edge computing’s development.
IT leaders don’t address business issues without a plan in place. Hence, edge strategies and related categories such as IoT and machine learning – figure exceptionally on their roadmaps. According to Red Hat’s 2022 Global Tech Outlook report, 61% of respondents anticipate running IoT, edge, or both workloads in the next 12 months.
Here are three areas of worry that companies should address in their edge strategies.
Don’t rely on universal definitions of “the edge” too much
Like other huge tech buzzwords, there’s a propensity in the industry to use dogmatic definitions that don’t take into consideration the realities of a single team or company on a day-to-day basis. A one-size-fits-all definition, on the other hand, entails a one-size-fits-all technique.
There is no one-size-fits-all answer, and this is the first gap in the method that comes to mind. Businesses should not try to shoehorn their objectives into an out-of-date edge strategy or technological platform.
Edge computing applications differ from one industry to industry, as well as from one region to region. While drone-based inspection may be appealing in one location, it may not be so in another. This isn’t to argue that there are no universal issues. An excellent example is security: an edge approach that ignores security is incomplete.
Another common denominator is automation. Because a lack of automation might result in greater maintenance costs, negating the business benefits of edge, proper automation solutions must be addressed ahead of time.
Consistency, predictability, and repeatability should be top priorities
Long-term troubles will result from edge strategies that rely on one-off “snowflake” patterns for their success.
Another area where hybrid cloud architecture experiences will likely benefit edge thinking is automation and repeatability. If businesses already understand the value of automation and repeatability in running hundreds of containers in production, they will see a similar value in edge computing.
Businesses should adhere to a consistent architecture to minimize fragmentation, which is the nightmare of managing hundreds of separate systems. In edge deployments, consistency and predictability will be crucial, just as they are in cloud-based deployments.
Indeed, the cloud-edge interaction is deepening in this area. Some of the same ideas that make hybrid clouds useful and viable will, for example, be carried forward to the edge. In general, firms are on the right track if they have already solved some of the complexities associated with hybrid cloud or multi-cloud systems.
Businesses must consider how they will conduct management at a large scale
When choosing a platform, businesses should look for one that enables the control of edge infrastructure and workloads from central management. While most edge use cases aim to run workloads with persistent cloud connectivity, a management platform that allows for configuration changes and sending new workloads from the cloud to the edge is essential. Enterprise scale and adoption are driven by reporting status and alerts from the edge to the cloud.