Between the sensors collecting data and the cloud computing services processing data, there are a variety of new technologies emerging. These technologies allow for complex processing and storage, closer to where data is collected — at the edge.
With the global proliferation of internet-connected devices, efficiency in data transmission and processing is becoming increasingly crucial.
While cloud computing has traditionally served as a reliable and cost-effective means for connecting many of these devices to the internet, the continuous rise of IoT and mobile computing has created the demand for lower network latency and more reliability.
Edge computing technology is now emerging to meet these demands.
It involves placing computing resources closer to where data originates (i.e. motors, pumps, generators, or other sensors) — or at the “edge.”
These computing resources may be located in the devices themselves or in hyper-local, small scale data centers.
For example, Telsa cars have powerful onboard computers which allow for low-latency data processing (in near real-time) collected by the vehicle’s dozens of peripheral sensors. This provides the vehicle with the ability to make timely, autonomous driving decisions.
On the other hand, there are technologies, such as wireless medical devices, that lack the necessary resources to process and store large streams of complex data. As a result, smaller, modular data centers are being deployed to provide storage and processing capacity at the edge.
These modular data centers are typically the size of a shipping container and are placed at the base of cell towers, or close to industrial facilities.
These two types of edge computing technologies play an important role in the broader edge computing ecosystem.
The hierarchy for the ecosystem is broken into 4 primary tiers:
- Edge Sensors & Chips: This is where data is initially collected. These technologies include sensors and chips manufactured for a wide range of use cases in addition to Application-Specific Integrated Circuits (ASICs) and Application-Specific Standard Products (ASSPs), which are optimized for very specific use cases.
- Edge Devices: These devices provide the first line of offense in processing and storing information. They include the edge sensors and chips, which collect the data, as well as the computational resources to process and analyze it — to an extent. These edge devices range from smart watches to autonomous vehicles.
- Edge Infrastructure: Data centers come in all shapes and sizes. More recently, microdata centers are being deployed to offer a hyper-local, resource-dense midpoint between the edge devices and the centralized cloud. They offer far more data processing and storage capacity than edge devices as well as extremely low latency compared to the centralized cloud (which could be located states away).
- Centralized Cloud: Cloud computing has become a primary location for storing, analyzing, and processing large scale data sets. With that said, the cloud is no place for analyzing data and delivering insights needed in real time. This is where edge data will go to end its journey and be added to other, relevant, historic data.
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