You now need three reasons for edge calculations


The Internet of things (IoT) is not just about connecting devices to the Internet and the cloud, but creating new business insights that automate business and production processes and accelerate innovation cycles. A large number of Internet of things are difficult to understand, because they can cover everything from workers to smart city factories, virtual reality assistance and medical procedures.

The common thing about all these use cases is that multiple connected devices do not slow down very quickly. In fact, the number of internet-connected devices is expected to reach 8 billion by 2020, which will generate a lot of data. However, the ability to store and move these data is becoming problematic. And, from a data-processing point of view, less than a third of the data generated by these machines is considered to be of high value.

In order to optimize the capture, transmission, processing, analysis and storage of petabytes per month, the data needs to be processed and analyzed at the edge of the wide area network (WAN). Access to “edge computing”, which ranges from single collection, parses and forwards “denatured” data to a rich analysis involving machine learning and localization event handling and operations.

Edge Computing can help companies address the cost, bandwidth, and latency problems of various iot applications. Here are the three main reasons you need Edge Computing:

Reduce the amount of data transferred and stored in the cloud

The amount of data being generated on the edge of the network is increasing exponentially faster than the ability of the network to process the network. Instead of sending data to a cloud or a remote data center to complete work, the endpoint should transfer data to the edge computing device that processes or analyzes data.

Bringing this computing power to the edge of the network will help solve most of the challenges of data set up in closed iot systems. The ultimate goal is to minimize costs and delays while controlling network bandwidth. The main benefit of edge computing is the reduction of data that needs to be transferred and stored in the cloud. This typically costs about $4,000 for long-term cloud storage, and about 10 times for real-time access storage. The ability to use technology to reduce these costs is a real benefit to companies that ultimately help them save money.

Reduces latency in data transmission/processing

Edge computing can also reduce latency between data transfers, processing, and ultimately required operations. Analysis and event handling can also be done faster and more economically, since most raw data does not need to be streamed to the cloud for processing and analysis. Cloud data centers may be hundreds of miles (or even thousands of miles) of connected devices, leading to round-trip delays of tens to hundreds of milliseconds. Robotic surgery, autonomous vehicles and sophisticated manufacturing delays are relatively life-span. Marginal computing can shorten the cycle to a few milliseconds.

Reduced signal-to-noise ratio

Finally, Edge Computing helps reduce signal-to-noise ratio, enabling companies to prioritize data, such as key data that needs to be analyzed, stored and processed. Take the monitoring of commercial refrigeration equipment as an example. The data collected is generated by the machine and is governed by the “I am good” telemetry state data. Every once in a while, the machine produces an event that “I can’t” – something that monitors the company’s real concern. Everything else is redundant “noise” data that overwhelms the signal event. Edge computing helps prioritize data that needs attention.

The need for marginal computing is becoming more and more obvious. As the volume of machine data will soon exceed global network capacity, Edge Computing will play an important role in the adoption of sustainable growth in the Internet of things. Companies and chief information officers must innovate to find ways to drive intelligence and calculate the way out of the edges to manage the impact of the data itself.