What is Edge Computing? And Why Does it Matter?
By: Cristovao Cordeiro
Tags: Big Data, Edge Computing, Cloud Computing, Computer Science, IoT

Cloud, edge, IoT, IIoT, ubiquitous, hybrid cloud, fog, big data…jeez, it’s like people are on acid and coming up with random tech terms just to make us, mortals, get confused.

Well, in this case, albeit being different, all of these terms actual make sense and are tightly interconnected — like a word cloud (pun intended).

Let’s build a timeline a make understand how all these come together.

In the old days, centralized computing was the to-go computing model. Heavy workloads would run in-house, on supercomputers, until distributed computing become possible and the World Wide Web was born in 1989, which made it possible for these centralized computing facilities to remotely connect to others around the world, forming heterogeneous clusters, and thus Grid Computing was born. Not too long after that, and even though the term was already coined, Cloud Computing became a Thing — actually, it became The Thing! Virtualization allowed for an opportunistic, secure and scalable way to accessing and sharing computing resources on-demand. Many companies decided to base their business on this concept and sell Cloud Computing resources as a Service (thus the popular terms IaaS, PaaS, SaaS…we’ll leave that for another day). Finally, mobile computing made its grand entrance as smartphones became better and more accessible, and single-board computers (like the Raspberry Pi) proved to be a cheap and capable way of running complex workloads.

Now, before getting to Edge Computing, you need to understand that by combining fast networks, interconnected heterogeneous devices and mobile computing, the Internet of Things became the largest data source available, generating unmanageable amounts of data — have a look at this study from IDC, which says:

there will be 41.6 billion connected IoT devices <…> generating 79.4 zettabytes (ZB) of data in 2025”

Fun fact: you would need to binge watch 3.5 billion years of YouTube videos in order to consume that much data.

So now you understand why big data is mentioned in this story and where we are going with it: how can we handle that much data?

And that’s the trigger for Edge Computing.

Edge Computing

move the computing and data management capabilities to the edge of the network, closer to where data is born, aka near-data processing

It is super impractical (if not impossible) to move ZBs of data to the cloud and expect the same computing experience. Plus, data is super valuable nowadays, which means it is a shame to let it go to waste. With this being said, there are four major factors you need to consider before moving to the Edge:

Cost — how much does it cost you to process the data, and to turn it into useful information? The main culprit here usually is the network cost, which happens when you move large amounts from data through the internet up to the cloud. Latency — do you have a real-time application? Is it supposed to provide immediate feedback? If so, you need to consider round-trip delays, which can be quite significant when sending raw data all the way up to the cloud and back down to the end-user. Reliability — how critical is your application? Does it need to perform consistently over time? For example, if your application fails to delivery for a short amount of time, is it life threatening? Just imagine a self-driving car…we cannot afford network delays or outages when trying to transfer raw sensor data to the cloud, to infer a potential collision. Privacy — is your application handling personal and/or sensitive data? Think about traffic cameras…the video feeds can contain personal information, like people’s faces! By moving that data up to the cloud through he public internet, you’d be leaving your data vulnerable to man-in-the-middle attacks.

So if you need:

↘️cost ️+ ↘️latency + ↗️reliability + ↗️privacy

then you should go to the Edge.