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IOTech Systems unveils top five predictions for edge computing
EDINBURGH, Scotland - OhioPen -- IOTech, the edge software company, today announced five predictions for edge computing in 2022. Among its forecasts, the company foresees a proliferation of AI and machine learning at the IOT edge and edge-cloud architectures as the norm.
The company also believes that the effect of edge computing on blockchain and other digital ledger technologies will become clearer in a few years. Similarly, 5G's impact on edge computing is still too early to predict.
"This past year, we've seen edge computing emerge from pilot programs to deployments," said Jim White, CTO, IOTech. "We believe 2022 will be the year that edge computing is fully integrated into the architecture of every major industrial IoT system."
IOTech's 2022 predictions are related to technology advancements, architectures and impact to industries.
Prediction 1: There will be pervasive adoption of AI/ML at the edge
The new status quo: edge systems will incorporate AI and machine learning. Simple rules engines and edge analytics are already at the edge. Today, organizations demand more intelligence at the edge. The raw compute to run AI/ML at the edge was a prohibiting factor, but this is no longer the case. While training ML systems will largely occur in the cloud or in the enterprise, ML models running on lighter AI runtime engines at the edge are more common place and will soon be the norm. Visual inference has been a leading use case, but other AI/ML solutions are soon to follow. Edge platform providers will play a key role in developing solutions that can easily integrate AI/ML technologies.
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Prediction 2: Hybrid edge-cloud architectures will be the norm
It's not edge compute "or" cloud compute, it's a case of "and". Organizations are finding that processing edge data needs to be performed at the edge and in the cloud or enterprise. Although initially there was much excitement related to the cloud providers reaching down to the edge, the reality is that there are significant challenges in moving all edge data to the cloud and performing all the processing in the cloud. The cost of data transport, latency issues and security/data privacy concerns are among the chief challenges. Likewise, the raw processing power of the edge and ability to do deeper exploration of the edge data over longer periods of time for better insights means edge computing alone is not a solution. Solutions must allow for the right processing at the right levels, and this calls for hybrid edge-cloud architectures.
Prediction 3: The industrial sector emerges from edge/IOT research mode
The industrial sector is becoming focused and organized in its effort to offer new solutions at the IoT edge. Businesses in manufacturing, building automation, and smart energy are in full "build" or "buy" mode for IoT edge solutions. Many large industrial sector businesses are fully committing to grow their edge/IoT products and strategy. Buy mode leads when companies need to accelerate digital transformation.
Prediction 4: Customers will demand solutions rather than pieces/parts
Companies looking to benefit from edge/IoT technology are looking for more fully integrated solutions. They want immediate tangible business outcomes and are not interested in receiving a bucket of technology parts that they then have to pull together themselves. For system integrators, it means developing the right technology partnerships to pre-assemble and deliver complete solutions to customers. Integrators will naturally gravitate to edge products that are inherently more open and flexible as these will be easier to integrate and adapt to more use cases.
More on Ohio Pen
Prediction 5: Realization that K8s is not enough edge management
Organizations deploying and orchestrating IoT/edge applications are discovering that Kubernetes is not always a fit in resource constrained edge environments. Furthermore, K8s only addresses part of the edge management need. There is more to edge management than managing/monitoring containers. An edge management solution must deal with preparing, managing, and monitoring the host edge nodes, allow for rapid configuration changes, and even assist in sensor/device onboarding. K8s will be part of some edge management solutions – where there are more resources or smaller K8s solutions (K3s as an example) can be applied. However, the thin, resource constrained, network constrained, latency concerned, sometimes non-containerized, OT device-touching environments demand alternative and more complete edge management solutions.
The company also offered further insights here
About IOTech
IOTech builds and deploys vendor-neutral software platforms and tools to support the rapid development, deployment and management of applications at the IoT edge helping drive IoT innovation, global market adoption, velocity and scale. The company's products address the full spectrum of secure hard and soft real-time edge computing needs, dramatically reducing time to market, development and system integration costs for its partners who are the supply chains to multiple vertical IoT market domains. IOTech leverages an open-source ecosystem to collaboratively improve time to market, develop global channel partnerships and achieve pervasive adoption of its software products.
The company also believes that the effect of edge computing on blockchain and other digital ledger technologies will become clearer in a few years. Similarly, 5G's impact on edge computing is still too early to predict.
"This past year, we've seen edge computing emerge from pilot programs to deployments," said Jim White, CTO, IOTech. "We believe 2022 will be the year that edge computing is fully integrated into the architecture of every major industrial IoT system."
IOTech's 2022 predictions are related to technology advancements, architectures and impact to industries.
Prediction 1: There will be pervasive adoption of AI/ML at the edge
The new status quo: edge systems will incorporate AI and machine learning. Simple rules engines and edge analytics are already at the edge. Today, organizations demand more intelligence at the edge. The raw compute to run AI/ML at the edge was a prohibiting factor, but this is no longer the case. While training ML systems will largely occur in the cloud or in the enterprise, ML models running on lighter AI runtime engines at the edge are more common place and will soon be the norm. Visual inference has been a leading use case, but other AI/ML solutions are soon to follow. Edge platform providers will play a key role in developing solutions that can easily integrate AI/ML technologies.
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Prediction 2: Hybrid edge-cloud architectures will be the norm
It's not edge compute "or" cloud compute, it's a case of "and". Organizations are finding that processing edge data needs to be performed at the edge and in the cloud or enterprise. Although initially there was much excitement related to the cloud providers reaching down to the edge, the reality is that there are significant challenges in moving all edge data to the cloud and performing all the processing in the cloud. The cost of data transport, latency issues and security/data privacy concerns are among the chief challenges. Likewise, the raw processing power of the edge and ability to do deeper exploration of the edge data over longer periods of time for better insights means edge computing alone is not a solution. Solutions must allow for the right processing at the right levels, and this calls for hybrid edge-cloud architectures.
Prediction 3: The industrial sector emerges from edge/IOT research mode
The industrial sector is becoming focused and organized in its effort to offer new solutions at the IoT edge. Businesses in manufacturing, building automation, and smart energy are in full "build" or "buy" mode for IoT edge solutions. Many large industrial sector businesses are fully committing to grow their edge/IoT products and strategy. Buy mode leads when companies need to accelerate digital transformation.
Prediction 4: Customers will demand solutions rather than pieces/parts
Companies looking to benefit from edge/IoT technology are looking for more fully integrated solutions. They want immediate tangible business outcomes and are not interested in receiving a bucket of technology parts that they then have to pull together themselves. For system integrators, it means developing the right technology partnerships to pre-assemble and deliver complete solutions to customers. Integrators will naturally gravitate to edge products that are inherently more open and flexible as these will be easier to integrate and adapt to more use cases.
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Prediction 5: Realization that K8s is not enough edge management
Organizations deploying and orchestrating IoT/edge applications are discovering that Kubernetes is not always a fit in resource constrained edge environments. Furthermore, K8s only addresses part of the edge management need. There is more to edge management than managing/monitoring containers. An edge management solution must deal with preparing, managing, and monitoring the host edge nodes, allow for rapid configuration changes, and even assist in sensor/device onboarding. K8s will be part of some edge management solutions – where there are more resources or smaller K8s solutions (K3s as an example) can be applied. However, the thin, resource constrained, network constrained, latency concerned, sometimes non-containerized, OT device-touching environments demand alternative and more complete edge management solutions.
The company also offered further insights here
About IOTech
IOTech builds and deploys vendor-neutral software platforms and tools to support the rapid development, deployment and management of applications at the IoT edge helping drive IoT innovation, global market adoption, velocity and scale. The company's products address the full spectrum of secure hard and soft real-time edge computing needs, dramatically reducing time to market, development and system integration costs for its partners who are the supply chains to multiple vertical IoT market domains. IOTech leverages an open-source ecosystem to collaboratively improve time to market, develop global channel partnerships and achieve pervasive adoption of its software products.
Source: IOTech Systems
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