The ‘edge’ in edge computing
Last Updated on April 20, 2021 by Team Wobot
In its sprint into the mainstream, the cloud revolutionized the way companies deal with data. But as it faced a series of challenges, such as insufficient bandwidth, unsatisfactory real-time, privacy protection, and energy consumption, the next deluge of that revolution emerged in the form of Edge. Edge computing is purely the data processing marvel that exists between the application and the cloud. Evolving around the growing need to move maximum part of the processing nearer to IoT sensors themselves to improve efficiency, it optimizes cloud computing systems to avoid disruptions in the sending and receiving of data.
So what is the edge in Edge Computing? The edge is basically an abstract space that facilitates a data center resource to be accessed in minimum time. You can say that edge is that very corner in a network diagram where the traffic enters or leaves the network.
Edge computing forces applications, data and services away from centralized nodes to the logical extremes of a network and processes data as close to the source as possible. This speeds up the analysis process making it easier for the decision makers to take swift actions on insights. This feature is remarkably favorable for organizations and opens up exciting new avenues for the investors.
To those who think that even today, edge computing is more of an idea than a product, more of a vision than a mature market? It is already evident how process has been pushed to the edge of the network. Constantly imbibing IoT data to the public cloud and then retrieving it for the users is both, expensive and complicated. It also creates performance lags that some apps can absolutely not afford. And thus, edge is the better option as it keeps data closer to the user.
The applications of Edge Computing are cosmic. As it decreases latency, it finds significant usage in devices like drones or smart traffic lights. Instead of being dependent on home for instructions or data analysis, these devices can carry out independent analytics on streaming data and communicate with other devices to complete the task.
Edge devices are proving to be a game changer in Industrial IoT. Applications like power production and manufacturing use edge devices that capture streaming data that can be used to avert failures. In manufacturing sector machinery uptime and functionality is extremely important. Malfunctions can result in security glitches, waste employee time, and slow-down production. But in edge computing IoT data is processed upon far quickly than cloud computing, machine failures can be identified and averted.
Another prominent example are the special chips being used in the new iPhones that retain the authentication data on the device rather than saving it on the Apple servers. This is a major add-on to the privacy factor.
Similarly, Google uses custom chips for elaborate machine language. This has resulted in faster processing and best photos as the Pixel phones process image data directly on the device.
The industrial giant Coca-Cola has also put edge technology to good use by incorporating edge computing to their free-style dispensers to help them decipher the consumer behavior so they can become more responsive to consumer needs.
Edge computing is also being deployed by GE locomotives to gather and process real-time data about railway conditions, train maintenance, and crew morale in order to assist railroad companies. This helps them adopt the most efficient and safe manner to move the trains through crowded railway corridors
All these examples are just a projection of how edge computing will rise as the Internet of Things evolves. It can be predicted that the year 2018 will witness the trend of cloud and edge computing being used in tandem. With companies like Google, AWS, and Microsoft expanding their data centers around the world so they can be closer to devices, it won be wrong to say that the future of computing is the edge.