20 Years of Edge Computing

How long will you wait for something? That depends on what you're waiting for, of course. But in your daily interactions, think about how many "things" you interact with where you expect the response to be instantaneous -- tapping on mobile apps; logging in and transacting with a retailer or a bank; selecting and viewing content on a streaming media device; interacting with a connected device like a light, doorbell, or even a car; or checking in for a train or flight. These are all services that require real-time processing of information, at massive scale, and are examples of where edge computing can make the difference between a great experience and a really frustrating one by bringing processing closer to devices and data.

Edge computing is not a new idea, just one that for decades was ahead of the market's ability to fully appreciate it. High-speed trading or optimizing and localizing services at branch offices are two long-standing examples of pushing business logic closer to where the action is. Modern technology adds use cases including enabling faster decisions for connected cars and other IoT devices or improving network processing speeds with 5G. 

At the most basic level, edge computing brings data, insights, and decision-making closer to the things that act upon them. Rather than relying on a central location that can be thousands of miles away, the edge is as near to the "thing" as possible. The goal is ultimately a reliable, scalable implementation so that data, especially real-time data, does not suffer latency issues that can affect an application's purpose or performance. At Akamai, we have been operating an edge platform that delivers edge computing capabilities for over 20 years. The Akamai Intelligent Edge is broadly recognized as the largest distributed network platform and has been delivering edge computing solutions to our customers virtually from day one.

In the year 2000, centralized computing occurred in data centers, and Akamai implemented our first iteration of edge computing to battle the "world wide wait." Our platform provided situational performance by assembling and delivering content based on business logic for each customer using a computing paradigm we called advanced metadata. Advanced metadata is an implementation of edge computing that uses XML to describe business logic for a given customer. 

In 2001, we worked with other industry leaders to develop a standard edge computing implementation for developers called Edge Side Includes (ESi). ESi helped our customers scale, increase performance, and save money by moving fine-grained business logic that would have been done locally to our edge, reducing the amount of data that their infrastructure needed to process in a datacenter. Fast forward to today, and this capability is commonly called serverless computing, since the customer can abstract scaling business logic from scaling infrastructure.

In 2002, we pioneered the use of Java and .NET technology at the edge and started to use the term "edge computing" to describe our approach. As we worked with our customers to hone the value proposition for, and the utility provided by, edge computing, we realized that customers were not looking for raw performance from edge computing. Their goals were ultimately programmatic access to build out reliable, scalable and secure implementations of their business logic so that data, especially real-time data, did not suffer latency issues that can affect an application's purpose or performance. 

By leveraging Akamai's Intelligent Edge as a programmable edge, they could maximize quality and reliability for their consumers, evaluate and optimize personalized web and mobile experiences, and maximize data autonomy and infrastructure and app security at a global scale.  

Today we have a myriad of edge computing solutions in use by customers across every major vertical. In fact, edge computing innovations represent approximately 20% of Akamai's more than 400 technology patents, and edge computing solutions generated well over $2 billion in revenue over the twelve months ended June 30, 2020. Those solutions can be grouped in three categories: content & media delivery, app & IoT optimization, and cloud & enterprise security. Here is a sample