EDGE COMPUTING

Formerly a new technology trend to watch, cloud computing has become mainstream, with major players AWS (Amazon Web Services), Microsoft Azure and Google Cloud Platform dominating the market. The adoption of cloud computing is still growing, as more and more businesses migrate to a cloud solution. But it’s no longer the emerging technology trend. Edge is.

As the quantity of data organizations is dealing with continues to increase, they have realized the shortcomings of cloud computing in some situations. Edge computing is designed to help solve some of those problems as a way to bypass the latency caused by cloud computing and getting data to a data center for processing. It can exist “on the edge,” if you will, closer to where computing needs to happen. For this reason, edge computing can be used to process time-sensitive data in remote locations with limited or no connectivity to a centralized location. In those situations, edge computing can act like mini datacenters.

Edge computing will increase as use of the Internet of Things (IoT) devices increases. By 2022, the global edge computing market is expected to reach $6.72 billion. And this new technology trend is only meant to grow and nothing less, creating various jobs, primarily for software engineers.

What is edge computing?

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.

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.

Edge computing can mean faster, more stable services at a lower cost. For users, edge computing means a faster, more consistent experience. For enterprises and service providers, edge means low-latency, highly available apps with real-time monitoring.

Edge computing can reduce network costs, avoid bandwidth constraints, reduce transmission delays, limit service failures, and provide better control over the movement of sensitive data. Load times are cut and online services deployed closer to users enable both dynamic and static caching capabilities.

Applications that benefit from lower response time, such as augmented reality and virtual reality applications, benefit from computing at the edge.

Other benefits of edge computing include the ability to conduct on-site big data analytics and aggregation, which is what allows for near real-time decision making. Edge computing further reduces the risk of exposing sensitive data by keeping all of that computing power local, thereby allowing companies to enforce security practices or meet regulatory policies.

Enterprise customers benefit from the resiliency and costs associated with edge computing. By keeping computing power local, regional sites can continue to operate independently from a core site, even if something causes the core site to stop operating. The cost of paying for bandwidth to take data back and forth between core and regional sites is also greatly reduced by keeping that compute processing power closer to its source.

An edge platform can help deliver consistency of operations and app development. It should support interoperability to account for a greater mix of hardware and software environments, as opposed to a datacenter. An effective edge strategy also allows products from multiple vendors to work together in an open ecosystem.

One way to view edge computing is as a series of circles radiating out from the code data center. Each represents a different tier moving closer to the far edge.

Provider/enterprise core: These are traditional "non-edge" tiers, owned and operated by public cloud providers, telco service providers, or large enterprises.
Service provider edge: These tiers are located between the core or regional datacenters and the last mile access, commonly owned and operated by a telco or internet service provider and from which this provider serves multiple customers.
End-user premises edge: Edge tiers on the end-user side of the last mile access can include the enterprise edge (e.g., a retail store, a factory, a train) or the consumer edge (e.g., a residential household, a car).
Device edge: Standalone (non-clustered) systems that directly connect sensors/actuators via non-internet protocols. This represents the far edge of the network.

Edge computing can simplify a distributed IT environment, but edge infrastructure isn’t always simple to implement and manage.

Scaling out edge servers to many small sites can be more complicated than adding the equivalent capacity to a single core datacenter. The increased overhead of physical locations can be difficult for smaller companies to manage.
Edge computing sites are usually remote with limited or no on-site technical expertise. If something fails on site, you need to have an infrastructure in place that can be fixed easily by non-technical local labor and further managed centrally by a small number of experts located elsewhere.
Site management operations need to be highly reproducible across all edge computing sites to simplify management, allowing for easier troubleshooting. Challenges arise when software is implemented in slightly different ways at each site.
Physical security of edge sites is often much lower than that of core sites. An edge strategy has to account for a greater risk of malicious or accidental situations.

As data sources and data storage become distributed across many locations, organizations need a common horizontal infrastructure that spans across their entire IT infrastructure, including edge sites. Even for organizations that are used to operating across multiple geographical locations, edge computing presents unique infrastructure challenges. Organizations need edge computing solutions that:

Can be managed using the same tools and processes as their centralized infrastructure. This includes automated provisioning, management, and orchestration of hundreds, and sometimes tens of thousands, of sites that have minimal (or no) IT staff.
Address the needs of different edge tiers that have different requirements, including the size of the hardware footprint, challenging environments, and cost.
Provide the flexibility to use hybrid workloads that consist of virtual machines, containers, and bare-metal nodes running network functions, video streaming, gaming, AI/ML, and business-critical applications.
Ensure edge sites continue to operate in the event of network failures.
Are interoperable with components sourced from various vendors. No single vendor can provide an end-to-end solution.
Edge Computing Use Cases
Intel has worked with many industry partners and end customers to deploy tens of thousands of edge computing solutions. Below are four edge computing use cases that show how Intel has helped companies enable new experiences and drive more-efficient operations.

Retail: Edge computing can use sensors and cameras to improve retail inventory accuracy and help make supply chains and product development more efficient. In addition, edge computing can support analysis of customer behavior in near real-time for an enhanced and potentially safer shopping experience. For instance, the Sensormatic video-based AI solution helped retailers open stores safely during the COVID-19 pandemic by tracking occupancy and monitoring social distancing.

Industrial: Edge computing can offer a foundation for Industry 4.0 by integrating digital and physical technologies for more-flexible and responsive manufacturing. For example, Intel and Nebbiolo Technologies worked with Audi auto manufacturing engineers to create a scalable, flexible platform that uses predictive analytics and machine learning algorithms to boost weld inspections and enhance critical quality-control processes.

Education: Some software-based education solutions use on-device AI for personalized virtual assistance, natural language interaction, and even augmented reality experiences. For instance, the ViewSonic digital whiteboard experience uses edge and vision technology to re-create the classroom experience for students and teachers engaged in distance learning

Healthcare: Edge computing can help transform outcomes with inpatient and outpatient monitoring and telehealth services and use machine and deep learning inference on imaging equipment to help detect health issues faster. Philips improved AI inference for medical images by 188 percent on existing CT scan equipment with no need for expensive new hardware.

The potential of edge computing

Considering that IoT and edge computing are still in their relative infancy, their maximum potential is far from full realization. At the same time, they are already accelerating digital transformation across many verticals, as well as changing day-to-day lives around the world.

At a base level, edge computing streamlines how much data businesses and organizations can process at any given time, and as a result, they are learning more and uncovering insights at an incredible rate. With more detailed data from a variety of multi-access edge computing locations, businesses are better equipped to predict, manage, prepare, and adapt for future demands using historical and near-real-time data and scalable and flexible processing without the costs and constraints of older IT options.

The acceleration of data and convenience of edge computing is also the driving force behind many new and exciting technologies, from the faster and more powerful mobile devices, online collaboration, and faster and more exciting gaming, content creation, and transportation. The ongoing development of self-driving cars, in particular, is a prime example of edge computing in action, with driverless cars reacting and adapting in real time instead of waiting for commands from a data center hundreds of miles away.

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