Placing compute, storage, and analytics close to where data is created sums up what is edge computing.
Cloud computing has become an integral part of technology transformations during the last few years, shifting the physical servers from the premises of each organization to the ‘cloud’ - a remote enterprise data center accessed by users on an on-demand basis. While this will continue to exist in the foreseeable future, the rise of real-time applications that require low latency, such as autonomous vehicles, predictive maintenance systems, and multi-camera video analytics, is driving the future of edge computing. The ongoing global shift to the 5G wireless standard will further enable edge computing to reap its full potential for use cases that need fast processing applications with low latency. Edge computing transforms the way the data generated by IoT and other devices is stored, processed, analyzed, and distributed.
Probably, one of the most comprehensive descriptions of edge computing is - It offers application developers and IT service providers the power of cloud computing capabilities, along with a faster and low latency service environment, at the edge of a network. Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge—where things and people produce or consume that information.” The simplest way to explain edge computing is that it is about processing and storing the data closer to where it is generated, rather than relying on a far-away centralized data center or in-house physical infrastructure.
Imagine hundreds and thousands of devices that monitor equipment and operations on a factory shop floor that send real-time data to the remote data center and back, making the quality and cost of the bandwidth a challenge. Edge computing can solve this problem by providing a processing and storage facility near the devices. An edge gateway, for example, can collect and process data from an edge device, before sending only the relevant data to the cloud for analytics using cloud capabilities. The cloud will then send the processed data back to the edge device for making decisions and implementing actions. This eliminates the need to send all data to the cloud and back, leading to cost savings and higher processing speed.
A typical edge computing environment can include the following components:
Edge Devices - if IoT devices, POS systems, robots, autonomous vehicles, smart speakers, watches, and sensors, can compute locally and talk to the cloud, they are edge devices.
Network Edge - Edge computing is typically located near or on individual edge devices. However, the network edge will be required in cases where it is too costly and complicated to deploy edge computing locally.
On-premise Infrastructure - like edge connects to the cloud, the servers, routers, containers, hubs, or bridges are used to connect edge devices to on-premise applications.
Edge devices can include IoT sensors, a laptop, smartphones, security cameras, autonomous mobile robots, or a surgical system that enables doctors to perform surgery remotely.
Edge computing is already at work in many places like hospitals, factories, and retail shops in solutions that do not require a network connection. Edge has the potential for transforming business across every industry and function, from customer engagement and marketing to production and equipment maintenance to back-office operations, proactive and adaptive, in real-time, leading to improved experiences for customers and employees.
The number of internet-connected devices deployed by businesses and the volume of data being produced and analyzed by those devices is growing at a great speed beyond the capacity that the traditional data center infrastructures can handle. Gartner predicted that by 2025, 75% of enterprise-generated data will be created outside of centralized data centers.
The increasing customer expectations and demand coupled with the emergence of other technologies lead to an increased number of use cases where edge computing can either help save money or take advantage of low latency.
Autonomous Vehicles: Flocking truck convoys - a group of trucks traveling close behind one another in a convoy - help save fuel costs and decrease congestion. With edge computing making trucks autonomous and facilitating communication with each other, only the truck in the front will need a driver.
Remote Asset Monitoring: Failure of remotely located plants and equipment, particularly in oil and gas, can be terrible. Edge computing enables real-time analytics much closer to the asset, facilitating quick and proactive actions.
Predictive Maintenance: It enables IoT and edge devices to monitor the health of machines, and collect and process data closer to the equipment, providing alerts, and signals to help teams plan and schedule predictive and prescriptive maintenance.
Patient Monitoring: Placing an edge close to the monitoring devices like a Pulse or Glucose monitor enables real-time notifications and alerts to doctors of changes in patient trends or behaviors.
Traffic Control: Edge computing enables effective traffic management in cities, including smart cities, like creating intelligent traffic controls, and managing the flow of autonomous cars.
Smart Homes: It allows service provider networks to provide a real-time response, ensuring privacy by keeping more of the data close and out of third-party systems.
Customer Service: Businesses across banking to retail to healthcare use edge computing to deliver hyper-personalized customer experiences and targeted ads.
Other use cases include Virtualised radio networks and 5G (vRAN), immersive cloud gaming experience, improved delivery of content, energy-saving smart grid, and brick and mortar retailing - for a unified store management application.
Edge computing helps businesses save costs, scale operations, and bring efficiency in the following ways, among others:
Enhanced speed and low latency - eliminates the need to transport data from endpoints to the cloud and back.
Improved security and privacy protections - because data is kept at the edge and out of the central servers.
Save operational costs - since all data need not be stored and processed in the cloud.
Reliability - application processing will continue even when communication channels get alow go temporarily down.
Scalability - organizations can add edge devices as the need arises, making them scalable to the expansion plans.
You can choose from a few edge procurement and adoption models for your organization. One way is to acquire total solutions that include hardware, software, and consulting from one vendor. Such solutions are typically created to address particular problems of specific industries. Another method is to buy devices from a hardware vendor, and analytical software from another vendor, and an in-house team implements them to the specific needs of each business. Since most companies, particularly small to medium companies may not have the required resources, capabilities, and bandwidth to adopt this method, the best option is to get the project done through TaaS platforms and source the best and most relevant resources through TaaS marketplaces to manage the deployment.
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