Size of Industry

$61,140,000,000

What is it?

In the beginning, there was One Big Computer. Then, in the Unix era, we learned how to connect to that computer using dumb (not a pejorative) terminals. Next we had personal computers, which was the first time regular people really owned the hardware that did the work.

Right now, in 2018, we’re firmly in the cloud computing era. Many of us still own personal computers, but we mostly use them to access centralized services like Dropbox, Gmail, Office 365, and Slack. Additionally, devices like Amazon Echo, Google Chromecast, and the Apple TV are powered by content and intelligence that’s in the cloud — as opposed to the DVD box set of Little House on the Prairie or CD-ROM copy of Encarta you might’ve enjoyed in the personal computing era.

MOST OF THE NEW OPPORTUNITIES FOR THE “CLOUD” LIE AT THE “EDGE”

As centralized as this all sounds, the truly amazing thing about cloud computing is that a seriously large percentage of all companies in the world now rely on the infrastructure, hosting, machine learning, and compute power of a very select few cloud providers: Amazon, Microsoft, Google, and IBM.

Amazon, the largest by far of these “public cloud” providers (as opposed to the “private clouds” that companies like Apple, Facebook, and Dropbox host themselves) had 47 percent of the market in 2017.

The advent of edge computing as a buzzword you should perhaps pay attention to is the realization by these companies that there isn’t much growth left in the cloud space. Almost everything that can be centralized has been centralized. Most of the new opportunities for the “cloud” lie at the “edge.”

So, what is edge?

The word edge in this context means literal geographic distribution. Edge computing is computing that’s done at or near the source of the data, instead of relying on the cloud at one of a dozen data centers to do all the work. It doesn’t mean the cloud will disappear. It means the cloud is coming to you.

That said, let’s get out of the word definition game and try to examine what people mean practically when they extoll edge computing.

HOW does it work?

Edge compute is the data processing that takes place at the network edge to decrease latency and reduce demands on cloud compute and data center resources. Edge computing takes place in intelligent devices — right at the location where sensors and other instruments are gathering and processing data — to expedite that processing before devices connect to the Internet of Things (IoT) and send the data on for further use by enterprise applications and personnel.

The primary reason for the growth of edge compute is efficiency. All of that collected data needs to be processed somewhere. And as the volume of IoT data has increased, more and more of the processing is taking place at the edge. Connected devices today are smarter, enabling the ability to program "edge AI" — artificial intelligence at the edge — a growing trend in edge intelligence.

With decades of experience in the rapidly evolving IoT industry, Digi has a complete product offering for optimizing IoT applications with edge compute functionality.

Delivering Only the Important Data

In IoT, massive amounts of data are collected at the edge of the network, but not all of it is useful. On average, most monitoring data tends to be standard “heartbeat” data. If the data isn’t changing significantly, that means things are working well. For example, it wouldn’t make sense to send hours of data to a distant data center, showing that a machine's vital signs haven’t changed.

In the past, companies would send all of their monitoring data into the cloud or to a corporate data center for processing, analysis and storage. As the IoT has grown, however, the volume of data makes this approach impractical. This is where edge compute enters the picture.

Edge compute performs processing close to where the data originates. That can greatly reduce or even eliminate the cost of the bandwidth needed to transmit it to the cloud or the corporate data center. Some applications do need to examine data at the edge. An intelligent or AI-enabled edge compute process can then immediately assess whether the situation demands a response in real time, or send it on to the data center for analysis.

Data collected at the edge falls into roughly three types:

• It needs no further action and does not need to be stored

• It should be retained for later analysis and/or record keeping

• It requires an immediate response

The mission of edge computing is to distinguish between these types of data, identify what level of response is required and act on it accordingly. In most cases it’s far more efficient to perform these functions right there at the edge, where the data is being collected.

When outlier data appears, action may need to be taken. Edge computing can provide a near real-time response to local events thanks to its physical proximity and resulting low latency. No round-trip of data from the edge to the cloud and back again is needed. In addition, the reduced flow of data over the network can produce substantial savings in bandwidth and thus significantly lower networking costs, especially for wireless cellular connections.

Use Case

1. Autonomous vehicles

Autonomous platooning of truck convoys will likely be one of the first use cases for autonomous vehicles. Here, a group of truck travel close behind one another in a convoy, saving fuel costs and decreasing congestion. With edge computing, it will be possible to remove the need for drivers in all trucks except the front one, because the trucks will be able to communicate with each other with ultra-low latency.

2. Remote monitoring of assets in the oil and gas industry

Oil and gas failures can be disastrous. Their assets therefore need to be carefully monitored.

However, oil and gas plants are often in remote locations. Edge computing enables real-time analytics with processing much closer to the asset, meaning there is less reliance on good quality connectivity to a centralised cloud.

3. Smart grid

Edge computing will be a core technology in more widespread adoption of smart grids and can help allow enterprises to better manage their energy consumption.

Sensors and IoT devices connected to an edge platform in factories, plants and offices are being used to monitor energy use and analyse their consumption in real-time. With real-time visibility, enterprises and energy companies can strike new deals, for example where high-powered machinery is run during off-peak times for electricity demand. This can increase the amount of green energy (like wind power) an enterprise consumes.

4. Predictive maintenance

Manufacturers want to be able to analyse and detect changes in their production lines before a failure occurs.

Edge computing helps by bringing the processing and storage of data closer to the equipment. This enables IoT sensors to monitor machine health with low latencies and perform analytics in real-time.

5. In-hospital patient monitoring

Healthcare contains several edge opportunities. Currently, monitoring devices (e.g. glucose monitors, health tools and other sensors) are either not connected, or where they are, large amounts of unprocessed data from devices would need to be stored on a 3rd party cloud. This presents security concerns for healthcare providers.

An edge on the hospital site could process data locally to maintain data privacy. Edge also enables right-time notifications to practitioners of unusual patient trends or behaviours (through analytics/AI), and creation of 360-degree view patient dashboards for full visibility.

6. Virtualised radio networks and 5G (vRAN)

Operators are increasingly looking to virtualise parts of their mobile networks (vRAN). This has both cost and flexibility benefits. The new virtualised RAN hardware needs to do complex processing with a low latency. Operators will therefore need edge servers to support virtualising their RAN close to the cell tower.

7. Cloud gaming

Cloud gaming, a new kind of gaming which streams a live feed of the game directly to devices, (the game itself is processed and hosted in data centres) is highly dependent on latency.

Cloud gaming companies are looking to build edge servers as close to gamers as possible in order to reduce latency and provide a fully responsive and immersive gaming experience.

8. Content delivery

By caching content – e.g. music, video stream, web pages – at the edge, improvements to content deliver can be greatly improved. Latency can be reduced significantly. Content providers are looking to distribute CDNs even more widely to the edge, thus guaranteeing flexibility and customisation on the network depending on user traffic demands.

9. Traffic management

Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.

With edge computing, there is no need to transport large volumes of traffic data to the centralised cloud, thus reducing the cost of bandwidth and latency.

10. Smart homes

Smart homes rely on IoT devices collecting and processing data from around the house. Often this data is sent to a centralised remote server, where it is processed and stored. However, this existing architecture has problems around backhaul cost, latency, and security.

By using edge compute and bringing the processing and storage closer to the smart home, backhaul and roundtrip time is reduced, and sensitive information can be processed at the edge. As an example, the time taken for voice-based assistant devices such as Amazon’s Alexa to respond would be much faster.

Market

The global edge computing market size is anticipated to reach USD 61.14 billion by 2028, exhibiting a CAGR of 38.4% over the forecast period, according to a new report by Grand View Research, Inc. The amalgamation of Artificial Intelligence (AI) into the edge ecosystem is expected to drive market growth. An edge AI system is expected to help enterprises make real-time decisions in a matter of milliseconds. The need to eliminate the privacy issues associated with transmitting large amounts of data and issues related to latency and bandwidth, which reduce the data transmission capacity of an organization, are expected to drive the market growth over the next few years.

Precision monitoring and machinery control are some use cases that would be well suited to be using AI in the edge. The latency requirement at a fast operating production line must be kept minimal, which can be achieved by deploying the edge. Processing data closer to the manufacturing plant can prove to be highly valuable, which can be achieved by deploying AI. AI-based edge devices such as chips can be used in multiple endpoint devices such as smartphones, cameras, sensors, and other IoT devices.

Furthermore, the telecom edge is expected to grow exponentially over the forecast period. The telecom edge shall perform computing close to the mini-data centers, which are owned and operated by the telco on a telco-owned property. Various telecom providers such as Telefonica and Telstra are engaging in pilot projects and prototypes of an open-access network incorporated with edge computing. Edge will spearhead the telecom sector once 5G is entirely rolled out. The telecom sector is in an exclusive position to benefit from edge computing, but telecom companies risk being relegated to irrelevant edge providers if they do not move up the value chain.

Currently, edge computing use cases have exceeded initial infrastructure deployments and are expected to provide an impetus to emerging edge computing use cases and infrastructure investments. Over the forecast period, edge computing is expected to become pervasive and transition toward platform-centric solutions. With this transition, edge platforms could reduce the infrastructure complexity using sophisticated management and orchestration software, and develop user-friendly environments for developers to deploy innovative edge applications and services with relative ease.

The edge server is expected to emerge as the fastest-growing hardware segment over the forecast period. The promising growth prospects of the segment can be attributed to the rising demand for edge servers across various industry verticals

In terms of application, the AR/VR segment is expected to expand at a significant CAGR owing to the growing cellular network, which has created growth opportunities for edge computing. For instance, Ericsson has optimized its 5G core and radio infrastructure to offer a high-quality VR experience

In terms of industry vertical, the datacenters segment is envisioned to witness the highest CAGR over the forecast period. This can be attributed to the fact that edge datacenters overcome intermittent connections and store and compute data close to the end-user

The Asia-Pacific region is expected to register the highest CAGR over the forecast period owing to the rising number of IoT integrated devices and the advent of 5G in the region. The rollout of 5G networks is expected to contribute towards the development of telco edge infrastructure for supporting 5G-enabled applications

Some of the key market players are Cisco Systems, Inc.; Moxa Inc.; Sierra Wireless; and Belden Inc.