Edge Computing

Edge Computing

What is Edge Computing?

Edge computing in IT is defined as the deployment of data-handling activities or other network operations away from centralized and always-connected network segments, and toward individual sources of data capture, such as endpoints like laptops, tablets or smartphones. Through this type of network engineering, IT professionals hope to improve network security and enhance other network outcomes.Generally, the term "edge computing" is used as a kind of catch-all for various networking technologies including peer-to-peer networking or ad hoc networking, as well as various types of cloud setups and other distributed systems. One other predominant type of edge networking is mobile edge networking or computing, an architecture that utilizes the edge of the cellular network for operations.

One of the major uses of edge computing is to improve network security. There is a lot of concern about security architecture in the internet of things age, where more and more diverse devices are getting different kinds of access to a network. One strategy is to pursue edge computing to aggregate data further out, and encrypt it as it passes further in, for example, through firewalls and perimeters.

Edge computing can also decrease the distance that data must travel in a network, or help with a detailed network virtualization model.

Edge computing works in various ways, and contributes to IT architectures in different capacities. It is a frequent and popular means of enhancing networks to promote efficiency and more capable security for business systems.

How does Edge Computing Work?

Edge computing entails processing and analyzing data closer to the source of where that data is collected.

Instead of a device or sensor sending all of its data over the internet to the cloud or an on-premise data center, it can:

• Process this data itself, essentially becoming its own mini data center.
• Deliver this data to a nearby computing device, such as a gateway networking device, a computer, or micro data center for analysis. This is sometimes called fog computing, though edge and fog computing are often used interchangeably.

With this new kind of architecture, a vast amount of processing power becomes decentralized from cloud service providers, which can help increase the speed of data analysis and decrease the load placed on internet networks to transmit huge amounts of data.

Advantages of Edge Computing :

• The first problem that edge computing solves is latency – how long it takes to process and analyze the captured data.The prototypical example of the use of edge computing to reduce latency is driverless cars.
• Real-time or near real-time data analysis as the data is analyzed at the local device level, not in a distant data center or cloud;
• Lower operating costs due to the smaller operational and data management expenses of local devices vs. clouds and data centers;
• Reduced network traffic because less data is transmitted from local devices via a network to a data center or cloud, thereby reducing network traffic bottlenecks;
• Improved application performance as apps that don’t tolerate latency can achieve lower latency levels on the edge, as opposed to a faraway cloud or data center.

Examples of Edge Computing:

• Mobile:A backend service for a mobile app is run from 20 data centers geographically distributed to be close to population centers. When a user opens the app they receive data updates and services from the data center closest to them.

• Web: A video website serves videos from a content delivery network to reduce latency. They also run code from each edge of the content delivery network to speed the delivery of digital advertising.

• Software as a Service: A business application running on cloud infrastructure is run from 12data centers with users connecting to the one closest to them.

• Internet of Things: A smart window firm monitors windows for errors, weather information, maintenance needs and performance. This generates a massive stream of data as each device is regularly reporting information. Edge services filter this information and report a summary back to a centralized service that is running from the firm's primary data centers. By summarizing information before reporting it, global bandwidth consumption is reduced by 99%.

• eCommerce: An eCommerce company delivers images and static web content from a content delivery network. They also perform processing at edge data centers to quickly calculate product recommendations for customers.

• Markets: A hedge fund pays an expensive premium for servers that are in close proximity to various stock exchanges to achieve extremely low latency trading. Trading algorithms are deployed on these machines. These servers are expensive and resource constrained. As such, they connect back to a cloud service for processing support.

• Games: A game platform executes certain real time elements of the game experience on edge servers near the user. The edges connect to a cloud backend for support processing. The backend is run from three regions that need not be close to the end-user.