Last year at this time, I wrote about where I saw the future of edge/IoT computing heading for 2022. It was part of an exercise to help my company and the EdgeX Foundry open-source project chart a course for the coming year. Trying to make predictions, especially predictions on the future of technology, is an equivocal business at best. Casey Stengel (the famous American professional baseball manager and player) warned to “never make predictions, especially about the future.” In his humorous way, Stengel offers common sense advice to treat all predictions with a certain skepticism. I hope this year’s list of predictions will help you take stock of what you see, plan your 2023 solutions, and adjust accordingly as edge technology inevitably changes.
Top 5 Edge Predictions
Let’s take a look at our five edge predictions for the new year:
#1: Edge Playtime is Over
Last year, I suggested that organizations were going to be transitioning from research, proof-of-concept, and pilot projects to full-scale deployments. I also suggested that customers were going to look for more complete solutions than pieces/parts to satisfy their edge/IoT needs. I am seeing evidence that this is already in full swing. Importantly, and more to the 2023 prediction, I see companies growing impatient with solution providers not being able to provide solutions that are already working at scale. Edge elements must be fully integrated into their choice of technology (hardware, sensors, devices, network, cloud providers, data visualization, analytics, security, management, and more). Companies want edge solutions that are easily installed and even easier to own and operate.
This is difficult for solution providers because no edge/IoT solution can do it all (and don’t believe any company that says they do). Solution providers need to find the right partners and complementary solutions, integrate like mad, and offer the “easy button” to companies wanting production-ready solutions and visible ROI today. Creating edge solutions is hard. Even when there are lots of fantastic technology ingredients available. But compounding the situation is that operating/owning the edge solution is even harder (and expensive). The edge is often operated by people with technical skill sets that are a fraction of what you might find in IT operation centers. People operating the edge systems are often doing so as a part-time or additional duty.
#2: OT Edge Security
I joked with many people in the industry that for the longest time when it came to what level of security organizations wanted, the response was: “Just keep us off the cover of the Wall Street Journal.” Organizations didn’t really know what edge/IoT security needed to do, but they were concerned about perceived threats at the edge. Threats at the edge are becoming more known. Requirements are becoming more clear and more specific. Companies are reading about the various attacks on the edge (such as Ring, St. Jude’s, Nortek, and Target) and they are becoming educated on what they want.
Companies are no longer under the illusion that closed-loop networks are truly closed, that obfuscation is good enough protection, or that no one would bother to want to get access to this type of data, and this is at the core of this edge prediction. Who would have imagined Elon Musk’s flight plans would be of interest to anyone? Today, organizations want to know how to protect all parts of the edge solution from sensor to cloud. They also want to know how to detect when something seamy or unexpected seems to be going on. I have seen a lot of edge/IoT security capabilities. Much of it originates in enterprise technology and helps to protect cloud-native environments. Most of it doesn’t integrate easily or well with existing OT technology. It doesn’t operate well at the edge where it is often disconnected, operating under resource constraints, and has to deal with OT protocols and sensors.
Some security startups are starting to recognize this, but these vendors will need to team up with more edge/IoT solution providers and be better integrated into the edge platforms. Security vendors will begin to provide solutions that really understand edge vulnerabilities and provide some solutions that suppress OT-based attacks.
#3: Reinvention & Disruption of Hyperscalers
Cloud providers and hyperscalers have tried and tried to lure all that precious edge data into the cloud where AI/ML and other analytics were to operate on it. The problem: the vast transfer, storage, and compute charges associated with moving all that edge data to the cloud are significantly expensive. Trying to sift through all that data for nuggets of commercial value doesn’t always show an ROI. Companies are beginning to wake up to this reality.
Google IoT Core went EOL this year. I am not predicting that more will follow that path. What I will predict is that the cloud providers and hyperscalers are going to re-invent themselves at the edge and figure out how to provide more value to companies building edge/IoT solutions. “Let us help you move all your data to our cloud” is not going to sell anymore. Organizations are helping the industry understand and build solutions that allow you to leave data at the edge and provide a real-time query mechanism to get the data from wherever it lives. No transport and central storage costs for the data beyond its origination point.
Hyperscalers who will have the most success are going to be those who team with organizations that understand the edge and IoT the best. This is because cloud native is not edge native. Companies need more help in deploying, orchestrating, upgrading, managing and monitoring the edge. Companies need more help figuring out what data to harvest and return to the enterprise or cloud if they have to move it at all and leave data that is chaff and noise at the edge. Companies need better visualization and operational control of the edge. Hyperscalers know how to do scale, they just need to do edge at scale and in a way that adds value and lowers cost. They can and will figure this out, but they are going to require help from organizations, people, and projects that know the edge. Watch for lots of new product announcements, new partnerships, and probably even some acquisitions as the hyperscalers finally take on edge native.
#4: Not All Requires AI/ML
Do you remember when everyone wanted to be a part of the latest AI/ML startup? When AI/ML engineers and data scientists were going for $400K a year? When AI/ML companies advertising to turn edge data into profits were being purchased for multiples of valuation? This is not history. It’s still happening. AI/ML is revolutionizing numerous industries and spaces. But it can be overapplied. There is a lot of edge processing going on. Some of it might even require sophisticated calculations and algorithms, but not all of it needs costly ML models and AI engines. Simple rules engines and scripting engines can provide value at the edge – saving operational costs, improving safety, and even generating new revenue.
Edge solutions don’t always require advanced/complex skill sets to produce, nor do they require all sorts compute power to operate. As an example, there is a lot of sensor data that comes from a hydroponic grow bed (moisture, soil temperature, pH level, nitrate and nutrient levels, and more). Growing the most crop with the fewest resources and the least amount of crop loss can be a delicate balance, but a sustainability scientist can find the right formulas and use some simple edge processing with actuation control to manage the necessary agricultural ecosystem.
To be sure, there are some edge problems well suited to AI/ML at the edge. Visual inference, for example, to do object detection and classification at the edge can be a valuable addition. This is especially true when combined with other sensor readings for corroboration.
But that complexity is not always needed. Companies are learning to keep it simple. There is still a lot of money to be found by measuring a few edge values and automatically actuating when things get out of range. Edge solution providers that help keep it simple and find low-hanging fruit at the edge, might become the new darlings of investors and companies looking to improve their company bottom lines.
#5: Kubernetes is Still Not the Full Answer
For our final edge prediction, it is important to recognize that everyone’s edge is different. Kubernetes can be used to deploy, orchestrate, and manage containerized workloads at some edges. However, Kubernetes does not solve all the issues around management at the edge and it struggles in resource-constrained environments or environments that aren’t going to support containerized workloads.
There have been, and continue to be, more CNCF efforts to extend cloud-native to the edge. Many of these have been attempts focused on shrinking Kubernetes at the cost of functionality. MicroK8s, KubeEdge, and K3s are all options that have been traversing this path. But I am seeing a recognition on the part of the CNCF community that Kubernetes-light isn’t enough.
Future of the Edge
From that understanding, I am predicting in 2023 we will see the emergence of new approaches and architectures to help address edge management. Probably still fledgling efforts, but keep an eye out. Past success or failure is probably not going to be an indicator for 2023, but at least I hope I have given you some food for thought to begin your 2023 planning. Remember, planning is everything, and these edge predictions will help you get started.
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