The age of Edge computing has finally dawned. The rapid developments in digital and mobile technologies have made Edge computing increasingly more prevalent, and more critical to the success of businesses across a wide range of industries.
What is Edge Computing:
Edge computing essentially takes memory and computing out of the traditional data center to bring them as close as possible to the location where they are needed – often in the form of hand-held or local devices, appliances, or point-of-sale or physical units that are distributed across different locations.
Edge means different things to different industries. For automotive, for example, it may mean the growing importance of compute capacity in smart cars or in hand-held devices used by technicians and service centers. For retail, it might mean new kinds of compute capacity available at point-of-sale systems and new experiences being delivered to customers in storefronts. Even in the fast food industry, Chick-fil-A shared it was running edge devices with container-based applications in every restaurant.
Edge applications which interact the closest with the local devices in the field are getting more sophisticated and intelligent with every passing quarter. There’s a lot of opportunity and promise in edge computing – for both end consumers and for business. These applications can offer customers a seamless and personalized experience, help improve business processes, and more.
Let us first examine some of the key promising use cases for Edge computing. We’ll then discuss the challenges with Edge computing and some of the key questions business leaders should ask themselves around supporting the intelligent Edge.
Five Key Edge Use Cases:
- Field and Industrial IoT – Various sensors and other field devices across verticals like Manufacturing, Transportation, Power are a prime candidate for Edge computing. These devices can be HVAC systems, Energy Meters, Aircraft engines, Oil rigs, Scanners in Retail, Wind turbines, Connected cars, RFIDs in Supply chain, Robotics, AR, and much more. These are often characterized by applications that collect data from edge devices and analyze it for different business use cases – security management, predictive maintenance, performance or usage tracking, demand forecasting, etc.
- Smart Cities and Architecture – Many cities across the globe are vying for the tag of a Smart City. IoT devices will make living in such cities easier for citizens. The use cases here range from municipalities providing faster urban services (repair of equipment), traffic management (to reduce gridlock), public safety and green energy provisioning
- Customer Experience in Retail and Hospitality – Customer sentiment data and social media data is collected and analyzed to improve customer experience. Data here is being captured by a kiosk or a Point of Sale (POS) system or Terminal.
- Connected Vehicles – For example, telematics data used for navigation, or to influence dynamic pricing for auto insurance, predict required maintenance, and so on.
- Facial and image recognition – as a way of identifying customers and reducing fraud in verticals such as Retail, Banking, and Entertainment.
The Edge Represents a Unique Computing Challenge
Edge computing is very different from traditional data center environments for the following reasons:
- Compute and hardware constraints: Many edge environments are constrained from the standpoint of technical computing footprint. For example, in the case of embedded devices, you can’t fit as much hardware as on a full-scale data center.
- Accessibility and Operations constraints: Often, Edge applications pose logistical difficulties in deploying human IT resources to manage them and do not allow for high operator cost. Companies can not have a dedicated admin to monitor and service each and every Edge location. For example, in the case of wind turbines spread across thousands of miles, or sensors located in the depth of oil wells or mining sites, or for every payment processing device at every checkout line at a department store, or thermostats located in people’s private domains.These operator limitations – either due to distance, the volume of devices, geographical accessibility, and other costs/ROI considerations mandate that Edge applications be not just very low on computing footprint but also on technical IT overhead. They have to be “plug & play” from installation and on-going operations perspectives.
- Remote management: In many environments, skilled personnel are not available to deploy & manage the solution on a regular basis. An unskilled operator may need to perform simple plug and play deployments. This includes delivering secure edge application updates, debug-ability in the case of problems and deployment of additional devices. The Edge applications need to be highly sophisticated and should be able to provide a range of features: data caching in case of lost connections, raw data stream processing to filter, analyze relevant data, message brokering for event-based applications, device management, fault tolerance, etc. Saving bandwidth costs of constrained networks is also another important consideration.
- Connectivity: The ability of the technology provider to work with all sorts of latency and jitter issues is also key.
- Support for Air-gapped deployments – the ability to manage remote, air-gapped devices in compute constrained locations without resorting to manual intervention is a key need in Edge Computing. High latency to the central cloud can cause delays and interfere with the workings of the application. This also means that assumptions that originate in “normal operations mode” of datacenter networking often do not hold true in Edge environments.
- Security is a foundational consideration. This includes secure communication from the datacenter to the Edge, ensuring the privacy of data both at rest and in motion – anonymizing sensitive customer data stored at the Edge. Other security requirements include establishing mutual trust between the central datacenter and Edge devices, the ability to find and stop rogue devices in the event of an attack and secure communication over the WAN.
- Unified architecture and release processes that span both the Edge deployment targets, as well as traditional datacenters. This is a major challenge since many Edge applications also need to be deployed across other environments or data centers, creating a complex and practically unmanageable matrix of code bases, pipelines, deployment processes and operational practices. These architecture silos are as much a cause of technical debt as are the data and processes silos.
Digital Transformation Via the Intelligent Edge
In light of the challenges above, the following are some of the key questions technology leaders should ask themselves around Edge applications’ release:
- How can we impact the customer experience in a connected world? What ecosystem partnerships can be beneficial for specific use cases? For instance, if you are a retailer, how can real-time customer traffic affect dynamic promotions to drive sales? Can you partner with Banks or players in complementary verticals to gain customers?
- How will the new edge experience(s) integrate with existing channels and processes for increasing customer engagement? Based on the above example, can we suggest other products to the customer based on their previous purchases?
- How can existing business workflows be augmented using Edge insights? Going back to our retail example, how do popular items affect “just in time” manufacturing, procurement, and supply chain workflows? Can these be augmented with this fresh data?
- What is the right architecture stack that enables the business to accomplish these capabilities? What does that look from a Cloud, Data, and Middleware design standpoint? Do we need a combination of VM and Containers running on the edge?
- What does this mean in terms of enabling self-service across the technology stack and the various stakeholders? Should business transformation be about autonomous capabilities? IT should not become a bottleneck. For example, can certain busier stores receive more compute capacity on the fly without a long manual provisioning cycle?
- How can we do so while eliminating most of the grunt work around building, operating and managing clouds? In short, how can our IT avoid becoming “Cloud janitors”?
- How can these Edge systems continuously learn and improve? Can robots or Drones deployed in distribution centers learn how to assemble boxes and stock those in the right areas? Can a mathematical model be deployed which enables a robot to understand metrics such as mean time to restock, error rate, accuracy in business processes, and more.
- How can the data being collected across edge devices help reduce unnecessary inventory, damage, and other quality issues?
- From a compute cloud standpoint, the key requirements are to support low latency, a high degree of workload parallelism and fault tolerance.
Stay tuned for the next post in this series, as we look at how the Edge is being revitalized with the advent of containers (hint: Kubernetes plays a HUGE role here!), and review some of the architecture and design best practices required to realize Edge applications. For a quick taste- check out this recorded webinar around 3 Kubernetes Use Cases: Cloud Native Apps, Hybrid Clouds, at the Edge
This article originally appeared on IoT Agenda
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