Click to learn more about author Ken Hosac.
The Internet of Things (IoT) is much more than just connecting devices to the Internet and Cloud, it’s about generating new business insights, automating business and production processes and accelerating innovation cycles. The vast array of IoT implementations are difficult to comprehend, as they can encompass everything from factories that run with little staff to smarter cities to virtual reality assisted repairs and medical procedures.
What all these use cases have in common is multiple connected devices, which show no sign of slowing down anytime soon. In fact, the number of enterprise IoT devices is expected to reach 8 billion by 2020, which in turn is generating massive amounts of data. The ability to store and move this data, however, is becoming problematic. Moreover, less than one-third of the data generated from these machines is considered high-value from a data processing perspective.
In order to optimize the capture, transmission, processing, analysis and storage of what can amount to petabytes of data every month, data needs to be processed and analyzed at the edge of the wide area network (WAN). Enter Edge Computing, which can range from single collection, parsing and forwarding of “denatured” data to rich Analytics that involves Machine Learning and localized event processing and actions.
Edge Computing helps enterprises address cost, bandwidth and latency issues across a broad range of IoT applications. Here are three key reasons why you need Edge Computing:
Reduce the Amount of Data Transmitted and Stored in the Cloud
The amount of data constantly being generated at the edge is growing exponentially faster than the ability for networks to process it. Instead of sending data to the cloud or a remote data center to do the work, endpoints should transmit data to an Edge Computing device that processes or analyzes that data.
Bringing this computing power to the edge of the network helps address the challenge of data build-up, mostly in closed IoT systems. The ultimate goal is to minimize cost and latency, while controlling network bandwidth. A major benefit Edge Computing brings to the table is the reduction of data needing to be transmitted and stored in the cloud. This can typically costs around $4,000 per petabyte for long-term Cloud storage and around ten times that for real-time access storage. Being able to use a technology to reduce these costs is a real benefit for businesses to ultimately help save them money.
Reduce the Lag Time in Data Transmission/Processing
Edge Computing also reduces the lag that can occur between data transmission, processing and the action required at the end. Analysis and event processing can also be done more quickly and cost effectively because much of the raw data does not need to be streamed up to the Cloud to be processed and analyzed. Cloud data centers can be hundreds – if not thousands – of miles away from a connected device, thus resulting in round-trip latency of tens to hundreds of milliseconds. This kind of latency for robotic surgery, autonomous vehicles and precision manufacturing are a relative lifetime. Edge Computing can reduce the cycle to just a few milliseconds.
Reduce the Signal to Noise Ratio
Lastly, Edge Computing helps reduce the signal to noise ratio to allow companies to prioritize data, such as focus on critical data that needs to be analyzed, stored and processed imminently. Take the monitoring of a commercial refrigeration unit for example. The data collected is machine generated and dominated by “I’m OK” telemetry state data. Every once in a while, the machine will generate an “I’m not OK” event – this is what the monitoring company really cares about. Everything else is superfluous “noise” data that drowns out the signal event. Edge Computing helps prioritize data that needs attention.
The need for Edge Computing is becoming increasingly apparent. As the amount of machine data generated will soon exceed total worldwide network capacity, Edge Computing will play a fundamental role in the continued growth of IoT adoption. Companies and CIOs must innovate to find ways of pushing intelligence and computing out to the edge to manage the onslaught of the data itself.