Edge is the new cloud...but only if it’s intelligent

No, edge computing is not going to kill the cloud.

smart industry iot iiot industrial internet of things digital transformation

FogHorn's Keith Higgins

 

While the cloud has brought massive constructive disruption to a broad range of industries, the edge is now the next big lever for digital transformation with the IIoT. According to Gartner, by 2022, as a result of digital business projects, 75% of enterprise-generated data will be created and processed outside the traditional, centralized data center or cloud, up from less than 10% in 2018.

The reasons are clear—there is simply too much data to move back and forth, security is a growing concern, and more applications require extremely low latency to be effective. Further, executing machine learning (ML) models and artificial intelligence (AI) at the edge generates higher quality predictive insights, delivering greater operating efficiencies, including uptime, yield and energy savings.

How do we define “the edge” in its truest form?

Truly processing data at the edge can help companies digitize to drive differentiation and a establish a clear path toward securing a competitive advantage, yet there is some confusion in the market regarding the proper definition.

In specific terms, “real” edge computing starts with software that offers a hyper-efficient complex event processor (CEP) that cleanses, normalizes, filters, contextualizes and aligns “dirty” or raw streaming industrial data as it’s produced. Without a CEP, latency is higher, the data remains “dirty” making analytics much less accurate, and ML models are significantly compromised. This definition of the edge is use-case driven—it’s the closest to the source of data you can get.

A “real edge” solution includes integrated ML and AI capabilities, all deployable into the smallest (and largest) compute footprints. The CEP function enables real-time, actionable analytics on-site at the industrial edge, with a user experience and alerts optimized for fast remediation by OT personnel. It also prepares the data for optimal ML/AI performance, generating the highest quality predictive insights to drive asset performance and process improvements.

The benefits of ‘true’ edge computing

True edge computing can yield enormous cost savings, as well as improved efficiencies and data insights for industrial organizations looking to embark on the path toward digital transformation. To explain the value of true edge computing, it’s best to consider five elements:

1) Real-time streaming analytics, close to the data source. CEP-based, real-time analytics are ideal for industrial applications requiring low latency and result in greater efficiencies in things like uptime, yield and energy savings. It also provides much higher data fidelity than sampled batch processing.

2) Iterative edge-to-cloud machine learning. Edge devices, generating continuous inferencing on live-streaming industrial data (including audio and video) regularly send insights back to the cloud. These edge insights enhance the model, significantly improving its predictive capabilities. The tuned models are then pushed back to the edge in a constant closed loop, reacting quickly to changing conditions, and generating much higher quality predictive insights to improve asset performance and process adjustments.

3) Radically lowers data persistence and transport requirements. There is just too much data being produced by industrial sensors to send it all to the cloud. Processing live data at the source reduces data network and storage-resource needs and can reduce cloud storage and communications costs by 100-1000x.

4) Enhances security posture. With the help of edge intelligence, organizations can process most of their data locally, eliminating the need to transmit sensitive OT data across networks. Some environments, for security reasons, are not allowed to be connected to the internet at all. This also reduces security infrastructure, risk mitigation, and regulatory-compliance costs.

5) Leverages small footprint edge computing and controller hardware. Edge-intelligence software should be able to run on industrial-control systems and other highly constrained edge-computing devices, such as Raspberry Pi-based equipment. This minimizes investments in heavy compute or new industrial-control systems hardware.

So, there you have it. Edge will unlock the trillions of dollars of value-creation for IIoT and will move many pilots to production smoothly and with improved economics and business outcomes.

Keith Higgins is VP of marketing with FogHorn.