Edge Computing Architecture, Explained:How Edge Computing Works

Edge computing


Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth. It is a topology rather than a technology

Edge computing is a distributed information technology (IT) architecture in which client data is processed at the periphery of the network, as close to the originating source as possible.

Data is the lifeblood of modern business, providing valuable business insight and supporting real-time control over critical business processes and operations. Today’s businesses are awash in an ocean of data, and huge amounts of data can be routinely collected from sensors and IoT devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world.

But this virtual flood of data is also changing the way businesses handle computing. The traditional computing paradigm built on a centralized data center and everyday internet isn’t well suited to moving endlessly growing rivers of real-world data. Bandwidth limitations, latency issues and unpredictable network disruptions can all conspire to impair such efforts. Businesses are responding to these data challenges through the use of edge computing architechture

In simplest terms, edge computing moves some portion of storage and compute resources out of the central data center and closer to the source of the data itself. Rather than transmitting raw data to a central data center for processing and analysis, that work is instead performed where the data is actually generated — whether that’s a retail store, a factory floor, a sprawling utility or across a smart city. Only the result of that computing work at the edge, such as real-time business insights, equipment maintenance predictions or other actionable answers, is sent back to the main data center for review and other human interactions.

Thus, edge computing is reshaping IT and business computing. Take a comprehensive look at what edge computing iis,how it works, the influence of the cloud, edge use cases, tradeoffs and implementation considerations.

How Edge Computing Works

Edge computing 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 computing 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:

  1. It needs no further action and does not need to be stored
  2. It should be retained for later analysis and/or record keeping
  3. 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.

Examples of edge computing

Edge computing offers a range of value propositions for smart IoT applications and use cases across a variety of industries. Some of the most popular use cases that will depend on edge computing to deliver improved performance, security and productivity for enterprises include:

Autonomous vehicles

For autonomous driving technologies to replace human drivers, cars must be capable of reacting to road incidents in real-time. On average, it may take 100 milliseconds for data transmission between vehicle sensors and backend cloud datacenters. In terms of driving decisions, this delay can have significant impact on the reaction of self-driving vehicles.

Toyota predicts that the amount of data transmitted between vehicles and the cloud could reach 10 exabytes per month by the year 2025. If network capacity fails to accommodate the necessary network traffic, vendors of autonomous vehicle technologies may be forced to limit self-driving capabilities of the cars.

In addition to the data growth and existing network limitations, technologies such as 5G connectivity and Artificial intelligence are paving the way for edge computing.

  • 5G will help deploy computing capabilities closer to the logical edge of the network in the form of distributed cellular towers. The technology will be capable of greater data aggregation and processing while maintaining high speed data transmission between vehicles and communication towers.
  • AI will further facilitate intelligent decision-making capabilities in real-time, allowing cars to react faster than humans in response to abrupt changes in traffic flows.

Fleet management

Logistics service providers leverage IoT telematics data to realize effective fleet management operations. Drivers rely on vehicle-to-vehicle communication as well as information from backend control towers to make better decisions. Locations of low connectivity and signal strength are limited in terms of the speed and volume of data that can be transmitted between vehicles and backend cloud networks.

With the advent of autonomous vehicle technologies that rely on real-time computation and data analysis capabilities, fleet vendors will seek efficient means of network transmission to maximize the value potential of fleet telematics data for vehicles travelling to distant locations.

By drawing computation capabilities in close proximity of fleet vehicles, vendors can reduce the impact of communication dead zones as the data will not be required to send all the way back to centralized cloud data centers. Effective vehicle-to-vehicle communication will enable coordinated traffic flows between fleet platoons, as AI-enabled sensor systems deployed at the network edges will communicate insightful analytics information instead of raw data as needed.

Predictive maintenance

The manufacturing industry heavily relies on the performance and uptime of automated machines. In 2006, the cost of manufacturing downtime in the automotive industry was estimated at $ 1.3 million per hour A decade later, the rising financial investment toward vehicle technologies and the growing profitability in the market make unexpected service interruptions more expensive in multiple orders of magnitude.

With edge computing, IoT sensors can monitor machine health and identify signs of time-sensitive maintenance issues in real-time. The data is analyzed on the manufacturing premises and analytics results are uploaded to centralized cloud data centers for reporting or further analysis.

  • Analyzing anomalies can allow the workforce to perform corrective measures or predictive maintenance earlier, before the issue escalates and impacts the production line.
  • Analyzing the most impactful machine health metrics can allow organizations to prolong the useful life of manufacturing machines.

As a result, manufacturing organizations can lower the cost of maintenance, improve operational effectiveness of the machines, and realize higher return on assets.

Voice assistance

Voice assistance technologies  such as Amazon Echo, Google Home, and Apple Siri, among others, are pushing the boundaries of AI. An estimated 56.3 smart voice assistant devices will be shipped globally in 2018. Gartner predicts that 30 percent of consumer interactions with the technology will take place via voice by the year 2020. The fast-growing consumer technology segment requires advanced AI processing and low-latency response time to deliver effective interactions with end-users.

Particularly for use cases that involve AI voice assistance capabilities, the technology needs go beyond computational power and data transmission speed. The long-term success of voice assistance depends on consumer privacy and data security capabilities of the technology. Sensitive personal information is a treasure trove for underground cybercrime rings and potential network vulnerabilities in voice assistance systems could pose unprecedented security and privacy risks to end-users.

To address this challenge, vendors such as Amazon are enhancing their AI capabilities and deploying the technology closer to the edge, so that voice data doesn’t need to move across the network. Amazon is reportedly working to develop its own AI chip for the Amazon Echo devices.

Prevalence of edge computing in the voice assistance segment will hold equal importance for enterprise users as employees working in the field or on the manufacturing line will be able to access and analyze useful information without interrupting manual work operations.


Benefits of Edge Computing

1. Speed

Speed is absolutely vital to any company’s core business. Take the financial sector’s reliance upon high-frequency trading algorithms, for example. A slowdown of mere milliseconds in their trading algorithms can result in expensive consequences. In the healthcare industry, where the stakes are much higher, losing a fraction of a second can be a matter of life or death. 

For businesses that provide data-driven services to customers, lagging speeds can frustrate customers and cause long-term damage to a brand. This may not sound as serious as life and death, but poor network performance and slow speeds can spell the end of your company altogether. Speed is no longer just a competitive advantage—it’s a best practice.

Edge computing’s most significant benefit is its ability to increase network performance by reducing latency. Since IoT edge computing devices process data locally or in nearby edge data centers, the information they collect doesn’t have to travel nearly as far as it would under a traditional cloud architechture

In today’s world, it’s easy to forget that data doesn’t travel instantaneously; it’s bound by the same laws of physics as everything else in the known universe. Current commercial fiber-optic technology allows data to travel as fast as the speed of light, moving from New York to San Francisco in about 21 milliseconds. 

However, as more and more data continues to be transmitted, digital traffic jams in the future are almost a sure thing. In 2020, the world generated roughly 44 zettabytes (one zettabyte equals a trillion gigabytes) of data. By 2025, 463 exabytes (one exabyte equals a billion gigabytes) of data will be generated every day.

There’s also the problem of the last mile buttleneck in which data must be routed through local network connections before reaching its final destination. Depending on the quality of these connections, the “last mile” can add anywhere between 10 to 65 milliseconds of latency.

By processing data closer to the source and reducing the physical distance it must travel, edge computing can greatly reduce latency. This means higher speeds for end-users, with latency measured in microseconds rather than milliseconds. Considering that even a single moment of latency or downtime can costcompanies thousands of dollars, the speed advantages of edge computing are paramount to your network.

2. Security

While the proliferation of IoT edge computing devices does increase the overall attack surface for networks, it also provides some important security advantages. Traditional cloud computing architecture is inherently centralized, which makes it especially vulnerable to  distributed denial of service (DDoS) attacks and power outages. Edge computing distributes processing, storage, and applications across a wide range of devices and data centers, which makes it difficult for any single disruption to take down the entire network.

One major concern about IoT edge computing devices is that they could be used as a point of entry for cyber attacks allowing malware or other intrusions to infect a network from a single weak point. While this is a genuine risk, the distributed nature  of edge computing architecture makes it easier to implement security protocols that can seal off compromised portions without shutting down the entire network.

Since more data is being processed on local devices rather than transmitting it back to a central data center, edge computing also reduces the amount of data actually at risk in a single moment. There’s less data to be intercepted during transit, and even if a device is compromised, it will only contain the data it has collected locally rather than the trove of data that could be exposed by a compromised central server

Even if an edge computing architecture incorporates specialized edge data centers, these often provide additional security measures  to guard against crippling DDoS attacks and other cyber threats. 

A quality edge data center should offer a variety of tools clients can use to secuure and monitor their networks in real-time.

3. Scalability

As companies grow, they cannot always anticipate their IT infrastructure needs. Building a dedicated data center is an expensive proposition which makes it even more difficult to plan for the future. 

In addition to the substantial up-front construction costs and ongoing maintenance, there’s also the question of tomorrow’s needs. Traditional private facilities place an artificial constraint on growth, locking companies into forecasts of their future computing needs. If business growth exceeds expectations, they may not be able to capitalize on opportunities due to insufficient computing resources.

Fortunately, the development of cloud-based technology and edge computing has made it easier than ever for businesses to scale their operations. Computing, storage, and analytics capabilities are increasingly being bundled into devices with smaller footprints that can be situated nearer to end-users.

Expanding data collection and analysis no longer requires companies to establish centralized, private data centers, which can be expensive to build, maintain, and replace when it’s time to grow again. By combining colocation services with regional edge computing data centers, organizations can expand their edge network reach quickly and cost-effectively. As they grow, the flexibility of leveraging edge computing’s capabilities allows them to adapt quickly to evolving markets and scale their data and computing needs more efficiently.

In short, edge computing offers a far less expensive route to scalability, allowing companies to expand their computing capacity through a combination of IoT devices and edge data centers. The use of processing-capable edge computing devices also eases growth costs because each new device added doesn’t impose substantial bandwidth demands on the core of a network.

4. Versatility

The scalability of edge computing also plays into its versatility By partnering with local edge data centers, companies can easily target desirable markets without having to invest in expensive infrastructure expansion. 

Edge data centers allow them to service end-users efficiently with minimal physical distance or latency. This is especially valuable for content providers looking to deliver uninterrupted streaming services. They also do not constrain companies with a heavy footprint, allowing them to nimbly shift to other markets if economic conditions change.

Edge computing empowers IoT devices to gather unprecedented amounts of actionable data. Rather than waiting for people to log in with devices and interact with centralized cloud servers, edge computing devices are always on, always connected, and always generating data for future analysis. 

The unstructured information gathered by edge networks can either be processed locally to deliver quick services or delivered back to the core of the network, where powerful analytics and machine learning programmes will dissect it to identify trends and notable data points. Armed with this information, companies can make better decisions and meet the true needs of the market more efficiently. 

By incorporating new IoT devices into their edge network architecture, companies can offer new and better services to their customers without completely overhauling their IT infrastructure. Purpose-designed devices provide an exciting range of possibilities to organizations that value innovation as a means of driving growth. It’s a huge benefit for industries looking to expand network reach into regions with limited connectivity (such as the healthcare,Agriculture and manufacturing sectors).

5. Reliability

Given the security advantages provided by edge computing, it shouldn’t come as a surprise that it offers better reliability as well. With IoT edge computing devices and edge data centers positioned closer to end-users, there is less chance of a network problem in a distant location affecting local customers. Even in the event of a nearby data center outage IoT edge computing devices will continue to operate effectively on their own since they handle vital processing functions natively.

By processing data closer to the source and prioritizing traffic, edge computing reduces the amount of data flowing to and from the primary network, leading to lower latency and faster overall speed. Physical distance is critical to performance as well.

By locating edge systems in data centers geographically closer to end-users and distributing processing accordingly, companies can greatly reduce the distance data must travel before services can be delivered. These edge networks ensure a faster, seamless experience for their customers, who expect to have access to their content and applications in an instant anywhere, anytime.

With so many edge computing devices and edge data centers connected to the network, it becomes much more difficult for any singular failure to shut down service entirely Data can be rerouted through multiple pathways to ensure users retain access to the products and information they need. Effectively incorporating IoT edge computing devices and edge data centers into a comprehensive edge architecture can therefore provide unparalleled reliability.

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