What is Edge Computing?
Edge computing is not just a methodology but also a philosophy of communication that mainly focuses on bringing computing devices closer to the network. The goal is to reduce any kind of latency in bandwidth usage. To put it in layman’s terms, edge computing means executing fewer operations in the cloud and migrating those operations to a more local environment such as a user’s computer, IoT device, or edge server. Implementing this process ensures that long-distance communications between client and server are minimized.
Edge Computing: A Brief History
Edge computing can be traced back to the 1990s, when Akamai launched a Content Delivery Network (CDN), which introduced nodes in geographic locations closer to the end user. These nodes store cached static content such as photos and videos. Edge computing takes this concept even further by allowing nodes to perform basic computational tasks. In 1997, computer scientist Brian Noble demonstrated how mobile phone technology could use cutting edge computing for speech recognition. Two years later, this method was also used to extend the battery life of the mobile phones. At the time, this process was called “online food search,” which is basically how both Apple’s speech recognition services and Google’s Siri work.
The year 1999 saw the arrival of peer-to-peer computing. In 2006, cloud computing came into being with the launch of Amazon’s EC2 service, and companies have embraced it in droves ever since. In 2009, “The Case for VM-Based Cloudlets in Mobile Computing” was published, detailing the overarching relationship between latency and cloud computing. The article called for a “two-tier architecture: the first level is the currently unmodified cloud infrastructure” and the second consists of dispersed elements called clouds with the first level cached state. This is the theoretical basis for many aspects of edge computing, and in 2012 Cisco introduced the term “fuzzy computing” for a dispersed cloud infrastructure designed to enhance the scalability of the Internet of Things.
This brings us to the current cutting edge solutions, of which there are many. Whether it is purely distributed systems, such as blockchain and peer-to-peer, or hybrid systems such as Green grass and Microsoft Azure IoT Edge, edge computing has become a major factor driving the adoption of technologies such as the Internet of Things.
Applications Of Edge Computing
Smart Cities
The edge computing architecture makes it viable for devices that control utilities and other public administrations to react to changing conditions in real time. Combined with the growing number of self-driving vehicles and the ever-growing Internet of Things, smart cities can change the way people live and use the benefits in the urban environment.
Since all Edge Computing applications rely on smart tools that gather information to perform basic processing tasks, the city of the coming days will have the ability to gradually respond to changing conditions as they occur.
Manufacturing
By joining data warehousing and logging in industrial equipment, manufacturers can aggregate data that will account for better perceptual maintenance and sufficiency of necessity, allowing them to reduce costs and use the essentials while maintaining better stability and useful uptime.
Moreover, intelligent manufacturing frameworks that are driven by continuous diversity of data and examination will help organizations change the build races to meet the most likely buyer’s needs.
Health Care
With the right IoT tools to deliver massive metrics of patient-generated health information (PGHD), healthcare providers can access essential data about their patients on a continuous basis rather than interact with benign and fragmented databases.
The medical devices themselves can also be manufactured to collect and process information during a diagnosis or treatment. While the regulatory imperatives for sharing and displaying medical data will make any sophisticated solution difficult to implement, other emerging safety efforts, for example, Blockchain innovation could provide better approaches to address these concerns.
Augmented Reality Devices
Wearable augmented reality gadgets like glasses and headphones are sometimes used to make this effect; however, most customers have encountered augmented reality through their mobile screens. Anyone who has messed around like Pokemon GO or used a channel on Snapchat or Instagram has used AR.
The innovation behind augmented reality expects devices to continuously process visual information and join previously displayed visual components. Without the edge computing design, this visual information will be transmitted back to centralized cloud servers where digital components can be added before being sent back to the tool. This course of action will inevitably lead to a critical latency.
AI virtual assistant
By incorporating Edge Computing engineering into their systems, organizations can completely improve performance and reduce inactivity. Instead of the AI virtual assistant sending preparation requests and data to a central server, they can instead scatter the weight between edge data centers while running some of their processing capabilities locally.
We can say that doubling the on-premises data servers for both cloud computing and edge computing makes it simpler than ever for associations to extend the reach of their network and set themselves in place to make the most of their data assets.
Conclusion
The implementation and adoption of edge computing has transformed the field of data analytics into a new dimension. More and more organizations are relying on this technology, which is entirely data-driven, and organizations that require instant, lightning-fast results. There are many online platforms that offer accredited courses in edge computing.