In many ways, IoT is still a buzzword. But more significantly, new technologies, which have grown from various IoT initiatives, have led to a revolution in the data center. The abundance of new data is having a significant impact on how deep learning, artificial intelligence (AI), and machine learning (ML) applications are leveraged. Cloud computing platforms can only perform to the extent of their ability to process relevant data sets. IoT deployments are allowing cloud computing to do more than ever before.
With the explosion of new endpoints collecting near infinite amounts of data, the question must be asked: Is this data meaningful? Oftentimes, one of the reasons we have so much data in the cloud is because there is nowhere else for it to go. With the rise of edge computing, and the ability to run inference engines on-premises using low-power gateways, data can now be processed before sending it to the cloud. This means the data sent to the cloud is more significant and meaningful, which allows deep learning programs to better understand matters that might have been missed before.
Edge computing is the next state of IoT transformation in the data center industry. It allows for data centers to offload workflows that can be better allocated. This, in turn, enables the cloud to perform a greater amount of analytics while giving the edge the ability to implement changes and updates based on work done in the cloud. The data center industry will transform itself through the ability to provide actionable results as well as the capability to process more valuable information in significantly shorter timeframes.
A traditional data center has incredible amounts of computing ability but is costly to build and maintain — especially when doing work on basic data sets. It is like having a chef at the front of the house taking orders. This is what the front of house staff is for — to take orders, inquire about specifications, and relay information to the back of house. In turn, the chef can focus on his role of preparing the customers’ meals. Edge computing empowers a traditional data center’s most critical assets to focus on the most demanding and highest priority items.
Automation is one of the key drivers in the rise of edge computing — managing workflows and processes of facilities without needing constant supervision. With automation helping to manage daily data center operations, including management and monitoring done by human operators, it allows for greater focus on meaningful analytics. Much like the data center itself, human operators and technical staff can be devoted either to running the data center or expanding its capabilities. Many of the tools needed for edge computing are unable to be implemented with the same automation tools used for maintenance tasks. As a result, critical assets are freed from simply continuing the required operation of the data center and, instead, further utilized to expand its capabilities.
One of the main challenges of implementing edge computing is the hefty price tag of the initial “lift and shift” of building out a use case. There is an infinite number of proof of concepts (POCs) that never quite materialize past their initial deployment. Scaling requires capital investment and an ability to see the long-term benefits versus the seemingly near-instant return of traditional cloud computing. The misconception that a solution will be easy to install and scale out is often remedied after the initial distribution.
Additionally, in many edge computing cases, it’s challenging to merge various infrastructure teams. Case in point: the stereotypical clash of the informational technologies (IT) and operational technologies (OT) departments. OT teams have well-founded, real concerns, and it’s the role of an IT team to demonstrate the ability to understand them and accept the slow and steady approach of implementation that comes with this new space.
While the complete benefits of edge computing will be fully revealed as more edge computing cases are compiled, there is already a significant reduction in cost to end users. For simple tasks, inference engines and analytics at the edge are reducing active consumption of cloud processing. This is beneficial as it increases accessibility to a greater number of people by removing the financial barrier. This also alleviates concerns about security.
It’s a popular opinion that data stored on-premises, or at the edge, is more secure because operators can control the physical access points. This reinforces the idea that edge computing can help drive overall adoption of cloud computing platforms. Furthermore, the evolution of the edge enables greater amounts of work to be done far more quickly.
The future of data centers, IoT, and edge computing is “more.” Undoubtedly, there will continue to be more successful use cases and wide-scale deployments. As we see the hype transform from initial buzz to significant results, and the first few case studies evolve into real and widespread success, it will become less challenging to deploy new edge computing instances. Once there is a sufficient level of readability established across major partner channels — which is already well underway — the process of building out a new application will be less of a custom deployment and more of an off-the-shelf experience. Scalability will lead to a degree of commoditization, resulting in exponential growth.