The rapid expansion of IoT devices, as well as their expanding computational capacity, has resulted in massive amounts of data. And as 5G networks expand the number of linked mobile devices, data volumes will continue to rise.
The promise of cloud and AI in the past was that they would automate and speed up innovation by generating actionable insight from data.
However, network and infrastructure capacities have been overtaken by the extraordinary amount and complexity of data provided by connected devices. Bandwidth and latency difficulties develop when all device data is transmitted to a centralized data center or the cloud.
Edge computing is more efficient since data is processed and analyzed closer to the point of origin. Latency is greatly decreased since data is not transported across a network to a cloud or data center to be processed.
This post will explain how Edge computing works, why it is essential, and provide various instances of Edge computing with benefits and cons.
What is Edge computing?
Edge computing is a distributed computing platform that puts corporate applications closer to data sources such as IoT devices or local edge servers. This closeness to data at its source can provide significant business benefits such as faster insights, faster reaction times, and increased bandwidth availability.
At its most basic, edge computing brings processing and data storage closer to the devices that gather data, rather than relying on a central location that may be thousands of miles away.
This is done to guarantee that data, particularly real-time data, is not subjected to latency issues that might impair application performance. Furthermore, by performing the processing locally, businesses can save money by reducing the amount of data that must be sent to a centralized or cloud-based location.
Consider devices that monitor industrial equipment on a factory floor or an internet-connected video camera that streams live video from a distant office. While a single device producing data can easily move data across a network, problems arise when the number of devices transmitting data at the same time grows.
Multiply a single live video camera by hundreds or thousands of units. Not only would the delay degrade the quality, but bandwidth charges might become prohibitively high.
Many of these systems benefit from edge computing hardware and services, which provide a local source of processing and storage. For example, an edge gateway can process data from an edge device and then transmit only the relevant data back to the cloud. In the event of a real-time application, it can also feed data back to the edge device.
How does Edge computing work?
The edge’s physical architecture is complex, but the core concept is that client devices connect to a nearby edge module for faster processing and smoother operations. IoT sensors, an employee’s computer, their most recent smartphone, security cameras, or even the workplace break room’s internet-connected microwave oven are examples of edge devices.
An autonomous mobile robot, such as a robot arm in an automobile plant, can be used as an edge device in an industrial context. It might be a high-end surgical technology that allows surgeons to do surgery from remote places in health care. Within an edge-computing infrastructure, edge gateways are considered edge devices.
The modules can well be referred to as edge servers or edge gateways, depending on the terminology used. While service providers will install multiple edge gateways or servers to enable an edge network (Verizon, for example, for its 5G network), organizations intending to implement a private edge network will also need to consider this gear.
In a normal configuration, data is created on a user’s PC or any other client application. It is then transferred to the server via channels such as the internet, intranet, LAN, and so on, where the data is stored and processed. This is still a tried-and-true approach to client-server computing.
The idea behind edge computing is simple: instead of moving data closer to the data center, the data center is relocated closer to the data. The storage and processing resources of the data center are located as close to the source of the data as possible (preferably in the same area).
Why is Edge computing important?
Much of today’s computing takes place at the edge, in places like hospitals, factories, and retail stores, processing the most sensitive data and powering mission-critical devices that must operate consistently and safely.
These locations necessitate low-latency solutions that do not require a network connection. Edge’s potential to disrupt a company across every sector and function, from customer engagement and marketing to manufacturing and back-office operations, is what makes it so intriguing. In these situations, edge enables proactive and adaptable business processes, frequently in real-time, resulting in new and improved user experiences.
Businesses can use Edge to bring the digital world into the real world. Improving retail experiences by bringing web data and analytics into physical establishments. Creating methods in which employees can be trained and scenarios in which robots can teach workers.
Creating intelligent settings that prioritize our safety and comfort. Edge computing, which enables enterprises to operate applications with the highest levels of dependability, real-time, and data needs immediately on-site, is similar to all of these cases. Finally, this enables businesses to innovate more swiftly, launch new goods and services more quickly, and create new income streams.
Edge computing & AI/ML
With its emphasis on data collecting and real-time processing, edge computing can help data-intensive intelligent applications succeed. Artificial intelligence/machine learning (AI/ML) operations, such as image recognition algorithms, can be conducted more effectively closer to the data source, eliminating the need to transport vast volumes of data to a centralized data center.
These apps combine a large number of data points to get higher-value information that can aid enterprises in making better decisions. This feature can help with a variety of company interactions, including customer service, preventative maintenance, fraud protection, clinical decision-making, and more.
Organizations can use decision management and AI/ML inference approaches to filter, analyze, qualify, and combine data points to derive higher-order information by considering each incoming data point as an event.
Data-intensive applications can be divided into phases, each of which is carried out at a separate location in the IT environment. When data is collected, pre-processed, and transferred, edge technology comes into action.
The data is then saved, converted, and used for machine learning model training after passing through engineering and analytics stages, which are commonly performed in a public or private cloud environment. Then it’s returned to the edge for the runtime inference step, which serves and monitors the machine learning models.
To meet these numerous objectives and offer connectivity between these distinct phases, a flexible, adaptive, and elastic infrastructure and application development platform is necessary.
The flexibility to optimally provision the data capture and intelligent inference workloads at the edge of an environment, the resource-intensive data processing and training workloads across cloud environments, and the business events and insight management systems close to business users is provided by a hybrid cloud approach, which provides a consistent experience across public and private clouds.
Edge computing is a crucial component of the hybrid cloud concept, which aims to provide a consistent application and operation experience.
Edge Computing Use cases
Edge computing is used in a lot of the technologies we use today for pleasure and business, from content delivery systems and smart technology to gaming, 5G, and predictive maintenance. Streaming music and video services, for example, frequently cache data to reduce latency and provide more network flexibility in response to user traffic needs.
Edge computing allows manufacturers to keep a closer check on their operations. Edge computing allows businesses to carefully monitor equipment and manufacturing lines for efficiency and, in certain situations, predict faults before they occur, reducing downtime costs.
Edge computing is also being utilized in healthcare to better care for patients, offering doctors more real-time insight into their health without having to submit their data to a third-party database for processing. Oil and gas corporations can keep an eye on their assets and prevent costly difficulties in other places.
Edge computing technologies are also used in the creation of smart homes. More and more gadgets, particularly voice assistants, need to connect and analyze data in a confined network. Amazon Alexa and Google Assistant would take much longer to discover answers for consumers if they didn’t have access to decentralized computing power.
Another typical example of edge computing is connected automobiles. Computers are installed on buses and railways to track passenger movement and service delivery. With the technology onboard their vehicles, delivery drivers can determine the most effective routes. When employing an edge computing strategy, each vehicle runs on the same standardized platform as the rest of the fleet, improving service reliability and assuring data security across the board.
Another example of edge computing is autonomous cars, which handle a vast quantity of real-time data in an environment where connectivity may be intermittent. Autonomous vehicles, such as self-driving automobiles, analyze sensor data on board the vehicle to decrease latency due to the sheer volume of data. They can, however, connect to a central place for software upgrades over the air.
Edge computing also contributes to the continued availability of popular internet services. Content delivery networks (CDNs) place data servers near customers’ locations, allowing busy websites to load quickly and enabling rapid video streaming services.
- Edge computing can result in cheaper, quicker, and more dependable services. Edge computing provides a speedier, more consistent experience for consumers. Edge implies low-latency, highly available apps with real-time monitoring for companies and service providers.
- Edge computing can save network costs, avoid bandwidth limits, shorten transmission times, eliminate service failures, and give you more control over the sensitive data transfer. Load times are reduced, and online services are brought closer to users, allowing for both dynamic and static caching.
- Computing at the edge benefits applications that benefit from a faster reaction time, such as augmented reality and virtual reality.
- The capacity to do on-site big data analytics and aggregation, which allows for near real-time decision-making, is another advantage of edge computing. By keeping all of that processing power local, edge computing further decreases the chance of sensitive data being exposed, allowing businesses to enforce security standards and comply with regulatory rules.
- The reliability and cost savings associated with edge computing benefit enterprise customers. Regional sites can continue to operate independently of a core site by keeping processing power local, even if the core site goes down for any reason. By keeping compute processing capacity closer to its source, the cost of paying for bandwidth to transport data between core and regional sites is considerably lowered.
- An edge platform can aid with operations and app development uniformity. In contrast to a data center, it should offer interoperability to cater to a broader diversity of hardware and software environments. In an open ecosystem, a good edge approach also allows products from many suppliers to function together.
- Edge computing expands a network’s overall attack surface. Cyberattacks can use edge devices as a point of entry, allowing an attacker to inject malicious software and infect the network.
- Unfortunately, building up effective security in a distributed context is challenging. The majority of data processing occurs outside of the direct line of sight of the security team and the central server. When the corporation adds a new piece of equipment, the attack surface expands as well.
- The cost of edge computing is another major issue. Setting up the infrastructure is expensive and complicated unless a corporation works with a local edge partner. Maintenance expenses are often expensive since the team must keep many devices in excellent working order at various locations.
- It can be more difficult to scale out edge servers to a number of tiny sites than it is to add the same capacity to a single core data center. Physical sites have more overhead, which can be challenging for smaller businesses to handle.
- Edge computing installations are typically located in distant locations with little or no technological knowledge on hand. If something goes wrong on-site, you’ll need an infrastructure that can be fixed quickly by non-technical local labor and then controlled centrally by a small group of professionals.
- To ease management and enable faster troubleshooting, site management procedures must be highly repeatable across all edge computing sites. When software is implemented differently at each location, problems develop.
- Edge locations are frequently less secure than core sites in terms of physical security. An edge approach must account for the possibility of malevolent or unintentional events.
Given that the Internet of Things and edge computing are still in their infancy, their full potential is still a long way off. Simultaneously, they are hastening digital change in a variety of industries, as well as altering people’s daily lives all around the world.
By 2025, experts expect that 75% of data processing will take place outside of a typical data center or cloud. Get a head start with edge computing to discover new business possibilities, improve operational efficiency, and provide consistent consumer experiences.