In a world where once-niche phrases like 鈥渢he cloud鈥 and 鈥淚nternet of Things鈥 (or IoT) have become everyday terms, edge computing remains something of an enigma to the general public. Part of the reason is that it鈥檚 a fairly new field. UM-Dearborn Assistant Professor Zheng Song, who鈥檚 made edge computing his primary research area, says the paper that sparked the discipline dates back a mere six years. Edge computing is so novel, in fact, there鈥檚 not even a dominant paradigm that defines it yet, though computer scientists seem to agree that as edge computing matures, it will be transformative.
To see why, it helps to understand some of the limitations of today鈥檚 connected environment. Cloud computing is a crucial part of that landscape and it鈥檚 absolutely transformed how we think about data. Before everything was connected to the internet, our digital content was often stored locally. Think back a few years, and you鈥檒l remember that Netflix was once a DVD rental by mail company, and you kept your documents, emails and vacation photos on your hard drive. To back them up, you needed an external hard drive or a CD/DVD burner. That seems old-school now. Today, movies and emails all exist in 鈥渢he cloud,鈥 meaning your data largely lives outside your devices on enormous central servers. This makes it possible to stream a movie or edit a Google doc anytime you ask for it, from any device.
But storing things on the cloud for instant, anywhere access has its limitations. The first challenge is the sheer amount of data that our devices are constantly downloading and uploading. Transmitting all that information requires robust networks that, just like highways, get bogged down when there鈥檚 too much traffic. Another challenge is that the seeming instant access provided by the cloud isn鈥檛 really instantaneous. In particular, Song says the physical distance between an end user and cloud server matters quite a lot. If you live several hundred miles away from a cloud server, which is common, since they are highly centralized, you鈥檙e going to bump up against both latency and network congestion issues. This might not matter much if you鈥檙e just waiting an extra second for an app to deliver all the pictures of cats in your cloud-stored photo archive. But in high-stakes, time-sensitive applications, like the split-second decisions your autonomous vehicle will one day make, delays aren鈥檛 something we can really accommodate.
Much of edge commuting鈥檚 potential lies in solving challenges like this, and in general, it does so by shrinking the distance between computing resources and the end user who is requesting them. By pushing more storage and computer processing away from the central hubs to the 鈥渆dges鈥 of networks, we reduce latency and congestion and get the results we want more swiftly. That鈥檚 the core idea anyway. But Song says there are still different ideas about how to organize this decentralized edge commuting environment, and there are at least three major paradigms contending for market space. One of the most straightforward is one in which cloud service providers, like Amazon and Akamai, simply build a network of mini data and computing centers that are more geographically dispersed, thus putting storage and computing services closer to end users. The wireless network operator Verizon is pushing a variation on this theme, by adding data and service centers right onto their cell towers and other network infrastructure.
But a third approach, the one Song is working on, looks much different than the other two. Rather than add a bunch of new storage and computing resources, Song is looking into ways various connected devices that already exist in close proximity could work together to form self-organized edge commuting platforms that are hyperlocal and thus very, very fast. Right now, for example, when your smart doorbell sends you an alert that someone is at your door, that notification is the end result of a series of executed tasks that still relies on the cloud. First, the camera on the doorbell records some video. It then uses your WiFi connection to send the imagery to the cloud for processing. Then a cloud server analyzes the video and makes a judgment about what鈥檚 happening. And if the AI determines there鈥檚 a person at your door, it鈥檒l send a notification to your phone. But all this takes time. And if you live far away from a cloud server, or all this happens during a time of network congestion, there might be a substantial delay between the time the video is recorded and when you get a notification. This is a common complaint, in fact, of WiFi-powered security systems. If it happens to be a burglar at your door, the intruder might already be in your house by the time you hear about it.
Now, let鈥檚 rerun the same scenario but with a hyper-local edge commuting environment. As before, your doorbell records the video, but instead of sending it to a cloud server hundreds of miles away for processing, it uses local resources that can execute the tasks instead. For example, Samsung鈥檚 new SmartThings Hub is being billed as just that: a cloudless data processing center for your various smart home IoT devices. And some newer devices contain that kind of processing power right in the device itself. But Song says there鈥檚 also a lot of potential for building edge networks out of things we already have readily available. Cell phones, for example, also have image processing capabilities 鈥 and could be leveraged as a super fast, on-demand edge node for a smart doorbell for the many hours someone is home. And Song is particularly excited by the possibilities for phone-car collaborations. For example, once autonomous vehicles are doing the driving for us, cars will feature more entertainment options for passengers. Given the car鈥檚 finite computing power, however, it鈥檒l be important not to strain the vehicle鈥檚 resources as it鈥檚 making real-time navigation decisions. To protect those resources, the car might form an edge network with your phone, asking the phone to use its cell connection to give you information about the sights you鈥檙e seeing along the way, or stream and mirror a movie to the car鈥檚 LED screen. That keeps the vehicle鈥檚 own internet connection and computer processing power free for the important stuff. To the end user, of course, the experience is seamless. It looks like the car is doing it all, even though some of the tasks are performed by the car鈥檚 computer and some are done by your phone.
To make such invisible 鈥渃ollaborations鈥 between different devices happen requires special software called 鈥渕iddleware,鈥 which Song recently received a National Science Foundation grant to develop. Song describes middleware sort of like a traffic cop, who, when a stoplight malfunctions, can signal to different drivers to yield or go, and thus keep traffic moving in a harmonious way. The middleware would be able to analyze a user鈥檚 request, divvy out which parts of the task can be handled by various local devices, send out orders, collect the results, and then send a unified response back to the user. It sounds like a lot of steps, but because they all happen hyper locally, and cut out the cloud, the process can happen really, really fast. Moreover, Song says edge computing like this has some distinct security and privacy advantages. Because the process doesn鈥檛 involve sending so much data out to the cloud, and can even rely on devices that are all owned by a user, there鈥檚 less opportunity for data to get into the wrong hands.
The potential applications for edge computing are multiplying fast. In addition to studying vehicle-phone collaborations, Song, for example, is interested in an even edgier edge application: vehicle-drone collaborations. And here in Southeast Michigan, there鈥檚 a huge appetite for edge computing in the data-driven advanced manufacturing space. For sure, there鈥檚 a lot riding on researchers like Song to perfect middleware applications, which could give edge computing a kind of ubiquity the cloud has today. Fortunately, he won鈥檛 be tackling his research questions alone. Some key local allies include students in his new edge commuting course. Over the next few semesters, they鈥檒l be building 鈥渢echnology collaboration鈥 applications between everyday IoT devices, allowing Song to put the middleware platform through its paces. Some successful projects for them could signal a bright future for his middleware 鈥 and an exciting future for edge computing.
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Story by Lou Blouin
