As global consultancy Bain & Company pointed out, COVID-19 and the shift to remote work may accelerate the shift to edge computing, since “dramatic shifts in traffic patterns have exposed weaknesses in network infrastructure, strengthening the case for investments in technology that reduces bottlenecks.” But IT leaders must first understand where the value of edge computing lies for their organizations.
Understanding the specific business case for emerging technology capabilities is always important. Exploring increasingly common use cases is particularly helpful when it comes to potential enterprise edge computing investments because their applications can vary so widely.
“Defining use cases upfront is important in edge computing because it drives architectural decisions. Diversity in edge use cases leads to diversity in edge solutions,” says Dave McCarthy, research director within IDC’s worldwide infrastructure practice focusing on edge strategies. Edge use cases involving wirelessly connected Internet of Things (IoT) devices may warrant a Multi-access Edge Computing (MEC) network solution from a communications service provider that offers services and computing functions required by users on edge nodes. An organization investigating a use case in heavy industry, on the other hand, will often deploy an on-site edge solution.
[ Get a shareable primer: How to explain edge computing in plain English. ]
While many organizations are not ready to deploy edge computing at scale, they are making moves to set themselves up for success. “I see many enterprises tackling infrastructure modernization as a first step in edge computing,” says McCarthy. “This means going into remote or branch locations and replacing legacy systems with software-defined infrastructure and cloud-native workloads. It provides a foundation for new edge use cases.”
Where digital transformation and edge fit together
Those that have completed the infrastructure modernization phase are moving on to digital transformation initiatives that benefit from real-time data generated in edge locations.
Unlike some other enterprise technology areas where demand drives the market, edge computing use cases thus far are largely supplier-led, says Yugal Joshi, vice president at management consultancy and research firm Everest Group. “Edge computing use cases continue to evolve as technology vendors up their innovation,” Joshi says. “As more suitable, sustainable, and reliable edge capabilities are built by hardware, software, and cloud vendors, newer use cases are emerging.”
As Stu Miniman, director of insights on the Red Hat cloud platforms team, has noted, “If there is any remaining argument that hybrid or multi-cloud is a reality, the growth of edge solidifies this truth: When we think about where data and applications live, they will be in many places. The discussion of edge is very different if you are talking to a telco company, one of the public cloud providers, or a typical enterprise. When it comes to Kubernetes and the cloud-native ecosystem, there are many technology-driven solutions competing for mindshare and customer interest. While telecom giants are already extending their NFV solutions into the edge discussion, there are many options for enterprises. Edge becomes part of the overall distributed nature of hybrid environments, so users should work closely with their vendors to make sure the edge does not become an island of technology with a specialized skill set.”
[ New to edge? Check out our primer: How edge servers work. ]
Notes Joshi, “The fundamentals of edge use cases continue to remain similar where the key ask is low-latency and reduction in network traffic transit.”
5 edge computing examples
We asked several edge computing experts where they see enterprises investing their edge dollars right now.
1. Predictive maintenance
Use cases around predictive maintenance have gained steam, says Joshi. Edge solutions are particularly popular in sectors where high-value assets can cost organizations massive losses when they go down. In the global oil and gas industry, the digitization of its pipeline coupled with edge data and analytics expertise can enable organizations to proactively manage their pipelines, addressing defects and preventing failures.
Results and reports that used to take weeks may be delivered in seconds. In this industry, trouble in the pipelines associated with a drilling rig can have large financial and environmental costs. Long-term corrosion is an environmental worry. Using a combination of field data (from cameras) and past experiences, systems that employ edge computing and machine learning analytics can alert operators to possible upcoming failures.
2. Remote workforce support
The pandemic has pushed many organizations quickly into remote working, dispersing the location of employees around the region, country, or globe. It also has proven to be a perfect use case for edge computing.
“The shift to remote work seems to be a good candidate for considering edge computing. Especially as companies increasingly consider remote workers in widespread geographic regions, they will also want to consider how those workers are accessing corporate systems,” says Seth Robinson, senior director of technology analysis at CompTIA. Taking an approach that includes edge computing would likely increase productivity and also improve resiliency.
As Frost & Sullivan recently noted: “As companies re-evaluate their long-term network needs based on their experience of tackling the current crisis, edge computing is now coming to the forefront as a necessary pillar of the network architecture to sustain this new distributed workforce and to effectively leverage the growing universe of devices and sensors at the edge of their networks.”
Edge has singular advantages that prove valuable in supporting the distributed workforce, such as reducing massive volumes of data needing to be moved across the network, providing computing flexibility and density, reducing data latency, and addressing regulatory requirements around data geolocation.
[ Want to learn more about implementing edge computing? Read the blog: How to implement edge infrastructure in a maintainable and scalable way. ]
3. Retail/commerce optimization
E-commerce optimization is another area gaining traction, according to Joshi. As more organizations in both B2C and B2B increase their digital sales capabilities in the era of COVID-19, edge computing can offer lower latency and greater scalability. This is particularly true when demand may fluctuate wildly. Brick-and-mortar retailers, likewise, see value using edge computing in combination with IoT on a number of fronts, including inventory management, customer experience, touchless checkout and curbside pick-up, demand sensing, and warehouse management.
4. Federated learning
“Edge AI happens when AI techniques are embedded in Internet of Things ( IoT) endpoints, gateways, and other devices at the point of use,” explains Jason Mann, vice president of IoT at SAS. It powers everything from smartphones and smart speakers to automotive sensors and security cameras.
According to IDC’s McCarthy, AI is “the most common workload” in edge computing.
“Now there is also an emphasis on leveraging AI at the edge to drive federated learning,” says Joshi. Federated Learning is an AI framework, whereby model development is distributed over millions of mobile devices. Federated learning can be a promising solution for enabling smart IoT-based applications. As Dr. Santanu Bhattacharya, chief data scientist at Airtel, explains on the Toward Data Science blog: The model development, training, and evaluation takes place on edge devices with no direct access to or labeling of raw user data, enabling the retraining of models with real use data – while maintaining data privacy.
[ Read also: 6 misconceptions about AIOps, explained. ]
5. Healthcare innovation
The healthcare industry was already seeing an uptick in edge investments prior to the pandemic, but the pandemic rapidly accelerated the move to telehealth and medical devices to track patients at home. As we have previously reported, a number of healthcare problems match up to edge’s ability to reduce latency in applications. In life-or-death scenarios, healthcare organizations can store and process data locally instead of depending on centralized cloud services. As a result, clinicians can get more immediate access to important medical data like MRI or CT scans, or information from an ambulance or ER for faster diagnoses or treatments.
[ Want to learn more about edge and data-intensive applications? Get the details on how to build and manage data-intensive intelligent applications in a hybrid cloud blueprint. ]