You’ve come to the conclusion that your business needs an edge computing strategy. You’re buried under a mountain of sensor data and your existing business systems just can’t keep up. Edge computing will enable you to take advantage of real-time analytics to reduce costs, prevent equipment failure, and improve visibility into business processes, but you’re just not sure how to get started.
We've put together this Infographic to share our learnings on edge computing for massive real-time apps. Edge Computing enables massive scale Smart Cities applications. Distributing application intelligence to each Edge Device allows Smart City applications to dynamically compose multiple data sources into real-time experiences.
One of the biggest challenges with monitoring the health of industrial equipment is in transforming torrents of raw, unstructured machine data into useful insights about maintenance needs, system performance, and anomaly detection. Traditional methods of “store everything now, ask later” can overwhelm networks and expose bottlenecks which delay the discovery of insights until it is too late. In order to capture insights about machine health and react to those insights in a timely manner, it is critical that machine data be processed at the edge.
Edge Computing is at a stage where discussions about how Edge Computing can solve business problems happen in the abstract. Vendors tout their benchmark statistics, system integrators roll out new capabilities, but enterprises are left to guess how technologies like Edge Computing will work with their machines and other business objects. In this post, we’ll use the example of a manufacturing facility to demonstrate the concrete differences between Edge Computing/Hybrid and cloud-only OT applications. We look at the Part, Machine, Composite, and System levels to see how Edge Computing affects how applications interact with the entire hierarchy of an industrial deployment.
As 2017 draws to a close, the Swim team wanted to look forward to 2018 and the changes we’ll see in the IIOT technology landscape in the New Year. This year, major industry analysts like Gartner, Forrester, Frost & Sullivan, and many others have highlighted Edge Computing as an increasingly important piece the IIOT puzzle. Edge deployments benefit from significantly decreased latency of data delivery, while providing a distributed computing network which ensures more consistent uptime. Pairing Edge Computing technologies with other recent advancements in Machine Learning and streaming analytics can enable industrial enterprises to transform raw sensor data at the edge into real-time insights structured for use in IT and OT applications. In this post we’ll make some predictions about both the industrial IT and OT landscape in 2018, and explain why Edge Learning will see wide adoption in the New Year.
We've put together this Infographic to share our learnings on edge analytics: insights at the source. Learn more about Industrial Transformation, Fast Data Analytics Adoption, and Technology Investment Priorities in our Infographic:
Machine Learning has become a dominant strategy for applying artificial intelligence capabilities to real-world information. The self-training nature of Machine Learning enables for pattern discovery in unstructured data, which is ideal for edge environments. Industrial edge sensor data is usually unstructured, with data being generated at a high rate, high volume, and with low information density. Sensor data often lacks the context necessary to identify which information is valuable, and therefore traditional application architectures have forwarded the entirety of raw data to the cloud for processing.
This is part 2 of a two part series. Read more about Fog & Edge Computing in Part 1.
Scores of articles discuss the difference between Edge and Fog computing, but their close proximity to each other in application architectures has led to significant confusion, often with the two terms being used interchangeably. Generally speaking, the difference between Fog and Edge computing concerns where data processing capabilities are located in the physical network architecture of an application. According to Automation Weekly:
This is part 1 of a two part series. Read more about Fog & Edge Computing in Part 2.
Over the past few years, there have been scores of articles discussing the difference between Edge and Fog computing. Though Fog and Edge are distinct concepts, their close proximity to each other in real-world deployments has led to significant confusion, often with the two terms being used interchangeably. Generally speaking, the difference between Fog and Edge computing concerns where data processing capabilities are located in the physical network architecture of an application. The most concise definitions I’ve come across are from an article by Automation Weekly in 2016, which contained interviews with industrial software executives and marketers. According to the article:
How Edge Computing Solves for Latency at Scale
Latency is a primary concern for most industrial applications. Timely delivery of insights about impending part failures, misrouted assets, misplaced tools, and other insights can have significant impact on business operations. Insights that are delivered too late lead to inefficiencies such as equipment failures and costly downtime. Last year, French energy management and automation giant Schneider Electric went so far as to declare that “latency is the enemy.” But Schneider also proposed a solution for “how Edge Computing can combat” latency in industrial environments. In this post, we’ll explore the ways latency can affect industrial applications and how Edge Computing optimizes for, and in some cases eliminates, the causes of latencies that can bog down traditional cloud applications.