Excellent use cases and benefits of edge computing

The use of edge computing in the enterprise will increase significantly as businesses and consumers connect more devices to the internet, ultra-fast 5G network services expand their coverage, and companies strive to pursue the opportunities brought by the technology.

According to a 2019 report by Gartner, by the end of 2021, more than 50% of large enterprises will deploy at least one edge computing use case to support IoT or immersive experiences, compared to less than 5% in 2019.

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The number of edge computing use cases will grow further in the coming years, with Gartner predicting that more than half of large enterprises will have at least six edge computing deployments by the end of 2023. In 2019, only 1% of large enterprises had six or more edge computing deployments.

The rise of edge computing depends on the continued improvement of analytical technologies that can analyze data generated by endpoint devices in real time. When combined with AI, machine learning and automation, this analysis can be used to control the operation of endpoints - these endpoints have limited or no human intervention.

For example, a fully automated assembly line could detect and correct errors, or a security checkpoint could allow authorized users in based on biometrics. These scenarios require the low latency and reliability that edge computing can provide.

“The rapid growth and deployment of the Internet of Things, sensors, mobile devices, and other connected devices means more data and a widespread need for edge computing,” said Bruce Guptill, chief strategy officer at Addressable Markets and a member of The Analyst Syndicate, a community of senior independent analysts.

“However, with the amount of data in use, the types of data and formats changing widely, the growing need for enterprises to protect more types of data in more ways, and the growing need to leverage data on so many types of devices, moving data back and forth between multiple clouds has become inefficient. And network bandwidth has not kept up, so response times have gotten worse.”

5 Advantages of Edge Computing

As the name implies, edge computing moves computing out of an enterprise’s core data center and places it close to the endpoint devices where the data is generated. This brings several key advantages, including the following:

1. Speed

Edge computing is provided through dedicated equipment (adjacent servers or virtual data centers close to the endpoint), eliminating the need to move data from the endpoint to the cloud and back again. Reducing transmission time saves process time, which can be measured in seconds, sometimes even milliseconds. David Williams, managing director of Ahead, a provider of enterprise cloud solutions, said: "Because data does not have to travel all the way back to the core site (i.e., data center or public cloud provider), edge applications and services can benefit from real-time or near real-time latency levels."

2. Security

Yannis Kalfoglou, head of AI and blockchain at PA Consulting, said edge computing enables greater security and flexibility because its decentralized nature eliminates a single central point of failure.

The result is that security teams can isolate compromised endpoints and edge computing devices. Additionally, edge devices can have device-specific security protocols deployed, making it harder for bad actors to learn how to hack into more devices.

3. Cost savings

Williams believes that one of the main benefits of enterprise edge computing is greater cost savings. He said: "Edge computing architectures and technologies are generally less expensive than centralized equivalents. Since less data is traveling between the edge and the core/central site, the reduction in connectivity costs can provide further cost savings."

4. Reliability

According to experts, edge computing can continue to operate even when communication channels are slow, intermittently available, or temporarily interrupted. For example, an energy company deploying edge computing on an oil rig does not have to rely on available satellite connections to relay all data back to the data center for processing, but instead chooses to move necessary processing information from the edge when connectivity is available, said Teresa Tung, managing director of Accenture Labs.

If any failure occurs at the edge, the impact is likely to be limited to the affected equipment - overall operations will still continue, improving the reliability of the entire system.

5. Scalability

Like cloud computing, enterprises can add edge devices only as their use cases expand, ensuring they deploy and manage only what they need, said Dan Miklovic, founder and principal analyst at Lean Manufacturing Research and a member of The Analyst Syndicate. Others also mentioned how the decentralized approach of edge computing makes large-scale deployments easier to manage. "It's much easier to address the scale of each edge location individually with a decentralized approach than at a centralized processing location (which we call the 'core,'" Williams said. "This is how enterprises support thousands, if not millions, of endpoints, which is a number that is difficult to handle in a centralized model."

7 use cases for edge computing

Each enterprise has its own considerations and motivations for deploying edge computing for a specific use case, for example, low latency and speed may be required in one case, while reliability is required in another.

Technology leaders and researchers say that many companies in nearly every industry are now deploying or testing edge computing use cases. Notable use cases include the following:

1. Self-driving cars

Self-driving cars are a prime use case for edge computing, as they can only operate safely and reliably if they can analyze all the data they need to drive in real time. The amount of data these vehicles accumulate is staggering. Industry experts estimate that a self-driving car could generate between 5 TB and 20 TB of data per day. And, while 5G can certainly handle more capacity, existing 4G networks are nowhere near fast enough to handle all that data.

“Autonomous vehicles must integrate and process large amounts of different types of data in different ways from multiple sources, including other vehicles, and do so instantly while on the move,” Guptill said.

This requires on-board computing power and edge data centers for mission-critical processing for navigation, vehicle-to-vehicle communications, and integration with emerging smart cities.

Edge computing can also help municipalities (such as transportation departments, public transformation departments, and private transport companies) better manage their fleets and overall traffic flows – by making rapid adjustments based on real-time on-the-ground conditions. For example, an edge computing platform deployed to process vehicle data can determine which areas are experiencing congestion and then re-route vehicles to alleviate traffic.

2. Higher security

Enterprises can use edge computing to implement video surveillance and biometric scanning, as well as other monitoring and authorization measures, where real-time analysis of data is critical to ensure that only authorized personnel and approved activities are allowed in. For example, enterprises can use biometric security products with optical technology to perform iris scans, with edge devices analyzing these images in real time to confirm a match with a worker with authorized access.

3. Healthcare

Healthcare data comes from a variety of devices, including doctors' offices, hospitals, and patients' own devices. Transmitting this data to a central location for analysis can cause bandwidth congestion, but not all data needs to be moved and stored in a centralized server. For example, every normal heart rate reading from a patient's medical device may not need to be retained, but some data is so important that it needs to be analyzed quickly without any delays caused by low-latency or unreliable network connections.

Edge computing can acquire and process data from endpoint medical devices in real time and determine which data points are not critical (i.e., normal heart rate readings) and can also identify, process, and respond to critical data points, alerting clinicians to take action as quickly as possible.

4. Manufacturing and Industrial Processes

The Industrial Internet of Things has added millions of connected devices in manufacturing plants and other such production operations, which collect data about production lines, equipment performance, and finished products. However, all this data does not need to be processed in a centralized server - every temperature reading from every connected thermometer is not important.

Most businesses only need to bring aggregated or averaged readings back to their central systems, or they only need to know when such readings indicate a problem, such as equipment temperatures falling outside of normal ranges.

That's what edge computing enables; it enables companies to acquire and understand data very quickly so problems can be identified and resolved quickly, said Gerald Kleyn, vice president and general manager of HP Inc. Speed ​​is especially important in manufacturing and similar environments, where automated assembly lines move quickly and require real-time intervention to resolve issues, he noted.

For example, at one manufacturing plant, edge computing took just one second to analyze product quality, a full 20 seconds faster than transferring manufacturing data to the cloud for processing.

5. Augmented Reality

Coaching employees through their jobs, training workers on new processes and teaching students complex concepts will increasingly be done with headsets that deliver virtual or augmented reality learning experiences, Miklovic said.

This experience can be provided by centralized computing resources, but the cost and latency may affect the user experience, while edge computing can provide reliable, real-time access to required information at a lower cost.

6. Enhance workplace safety

Advances in sensors, computer vision, and artificial intelligence are further expanding workplace safety applications, as running these technologies at the edge allows businesses to monitor conditions and identify and alert on hazardous situations in real time.

For example, companies can use the location data of employees in the field to enforce new social distancing requirements during the COVID-19 pandemic and warn them not to get too close. Because such location data has no value after that moment, the information can be collected and processed at the edge instead of having to be moved and stored within corporate data centers.

7. Streaming Services

“OTT streaming platforms are quickly becoming the standard means of distributing content,” Williams said. “While IPTV (IP television) was the original target for content production and centralized distribution to consumer devices, we are seeing OTT evolve to include original content, live events and even regional content with a more demanding user experience.”

According to Williams, this is a driving factor for media companies to leverage edge computing capabilities - it enables businesses to reduce latency while ensuring high-quality video and streaming performance.

More edge computing opportunities on the horizon

While these use cases for edge computing are already delivering value, experts predict that enterprises will continue to expand how they deploy edge computing to improve current operations and activities, as well as to develop and support new products and services.

"As businesses continue to look for ways to leverage the data they collect, they will also continue to provide applications and services to process and use that data locally," Williams said.

He continued: “This ‘data gravity’ will drive a new generation of data-driven solutions at the edge that were not possible before. Service providers will continue to invest to solve today’s connectivity challenges; AI will continue to evolve and become more distributed and decentralized between the edge and the cloud computing aspects of its technology architecture; and we will see an accelerated move to open, secure, cloud-native standards, with a focus on deploying edge technologies, from self-recovery to self-healing. The future of edge computing is bright.”