Edge Vs. Cloud Vs. Fog Computing
Edge and fog computing are intermediary computing technologies that help move the data collected by IoT devices at remote locations to a company's cloud.
The exponential growth in data collected by billions of IoT and mobile devices is driving a shift from sending data to the cloud for processing and storage to a distributed model where some computing occurs at the edge of the network, closer to where the data is created.
Edge computing is a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. This proximity to data at its source can deliver strong business benefits, including faster insights, improved response times and better bandwidth availability.
Gartner estimates that by 2025, 75% of data will be processed outside the traditional data center or cloud.¹
Edge computing allows IoT devices to process and act on data in real or near-real time by processing data at the edge of the network
Edge computing allows devices in remote locations to process data at the "edge" of the network, either by the device or a local server. And when data needs to be processed in the central datacenter, only the most important data is transmitted, thereby minimizing latency.
What Is Edge, Cloud and Fog Computing?
Edge computing is the process of bringing information storage and computing abilities closer to the devices that produce that information and the users who consume it. Traditionally, applications have transmitted data from smart devices like sensors and smartphones to a central data center for processing. However, the unprecedented complexity and scale of data have outpaced network capabilities. By shifting processing capabilities closer to users and devices, edge computing systems significantly improve application performance, reduce bandwidth requirements, and give faster real-time insights.
Edge computing is a distributed IT architecture which moves computing resources from clouds and data centers as close as possible to the originating source. The main goal of edge computing is to reduce latency requirements while processing data and saving network costs.
The edge can be the router, ISP, routing switches, integrated access devices (IADs), multiplexers, etc. The most significant thing about this network edge is that it should be geographically close to the device.
Edge computing refers to processing, analyzing, and storing data closer to where it is generated to enable rapid, near real-time analysis and response.
In recent years, some companies have consolidated operations by centralizing data storage and computing in the cloud. But the demands of new use cases enabled by billions of distributed devices—from advanced warehouse and inventory management solutions to vision-enhanced robotic manufacturing lines to advanced smart cities traffic control systems—have made this model unsustainable.
Additionally, the increased use of edge devices—from Internet of Things (IoT) devices, such as smart cameras, mobile point-of-sale kiosks, medical sensors, and industrial PCs to gateways and computing infrastructure—for faster, near real-time actionable insights at the data source is driving exponential growth in the amount of data generated and collected.
Edge computing provides a path to reap the benefits of data collected from devices through high-performance processing, low-latency connectivity, and secure platforms.
It’s estimated that by 2025, 75 percent of data will be created outside of central data centers, where most processing takes place today.1
Edge computing allows the capture, processing, and analysis of data at the farthest reaches of an organization's network: the "edge." This allows organizations and industries to work with urgent data in real time, sometimes without even needing to communicate with a primary datacenter, and often by only sending the most relevant data to the primary datacenter for faster processing. This spares primary computing resources like cloud networks from being glutted with irrelevant data, which lowers the latency for the entire network. It also reduces networking costs.
Cloud computing enables companies to store, process, and otherwise work with their data on remote servers that are hosted over the internet. Commercial cloud computing providers offer digital computing platforms and collections of services that companies can use to reduce or eliminate their physical IT infrastructure and the associated costs. Cloud computing also enables organizations to deliver secure remote work capabilities to their people, more easily scale their data and apps, and take advantage of IoT.
The biggest problem of cloud computing is latency because of the distance between users and the datacenters that host the cloud services. This has led to the development of a new technology called edge computing moves computing closer to end users.
Fog computing allows data to be temporarily stored and analyzed in a compute layer between the cloud and the edge for cases where it's not possible to process edge data due to edge equipment compute limitations.
From the fog, relevant data can be sent to cloud servers for longer-term storage and future analysis and use. By not sending all of the edge device data to a central datacenter for processing, fog computing allows companies to reduce some of the load on their cloud servers, which helps to optimize IT efficiency.
For example, consider a building management company that uses smart devices to automate temperature control, ventilation, lighting, sprinklers, and fire and security alarms in all of its buildings. Rather than having these sensors constantly transmitting data to their main datacenter, the company has a server in each building's control room that manages immediate issues, and only sends aggregated data to the main datacenter when network traffic and compute resources have excess capacity. This fog computing layer allows the company to maximize its IT efficiency without sacrificing performance.
Fog computing is simply an additional option to help companies gain more speed, performance, and efficiency in certain edge computing scenarios.
From the Edge to the Cloud
Although edge computing provides an unprecedented opportunity for organizations to unlock the value in data, the cloud remains essential as a central data repository and processing center.
IoT and edge computing devices collect data and manage it in one of two main ways. Intelligent edge computing devices with built-in processors may offer advanced capabilities like analytics or AI onboard, while devices without processors send the data they generate to a server deployed at the on-premises edge for storage and analysis.
An on-premises edge server can then process data from the edge computing devices and return critical information needed for near real-time applications or send only the relevant portions of the data to the cloud. Data from numerous edge computing devices can be consolidated in the cloud for more extensive processing and analysis.
How Does Edge and Fog Computing Work
Drivers of Edge and Fog Computing
Benefits of Edge and Fog Computing
Drawbacks of Edge and Fog Computing
Why is Edge and Fog computing important?
Edge and Fog computing hardware and networking
What are some of the characteristics of Edge and Fog hardware?
Edge and Fog computing use cases and examples
Edge and Fog computing services
How can AWS help you with your edge computing requirements?
How Does Edge and Fog Computing Work
In a traditional setting, data is produced on a user's computer or any other client application. It is then moved to the server through channels like the internet, intranet, LAN, etc., where the data is stored and worked upon. This remains a classic and proven approach to client-server computing.
However, the exponential growth in the volume of data produced and the number of devices connected to the internet has made it difficult for traditional data center infrastructures to accommodate them. According to a study by Gartner, 75 percent of enterprise generated data will be created outside of centralized data centers by 2025. This amount of data puts an incredible strain on the internet, which in turn causes congestion and disruption.
The concept of edge computing is simple - instead of getting the data close to the data center, the data center is brought close to the data. The storage and computing resources from the data center are deployed as close as possible (ideally in the same location) to where the data is generated.
Early computing |
Applications run only on one isolated computer |
Personal computing |
Applications run locally either on the user’s device on in a data center |
Cloud computing |
Applications run in data centers and processed via the cloud |
Edge computing |
Applications run close to the user; either on the user’s device or on the network edge |
Edge computing works by bringing computation and storage closer to the producers and consumers of data. Edge deployments vary for different use cases, but can be grouped into two broad categories.
A, Upstream applications
Upstream applications prioritize collecting data from smart sensors and other devices, then transmitting it to data centers for further processing. Data collected falls into three main categories:
- Redundant or irrelevant data, like room temperature data that a sensor measures every 5 minutes
- Useful data with long-term storage requirements, like average temperature over a few hours
- Useful data with short-term implications, like room temperature values below which the heater must turn on
Edge computing in upstream use cases focuses on distinguishing between these three types of data sources, then only transmitting critical information to the data center. Edge strategies could include the following examples.
I. Local on-premises data center
Companies put storage, servers, and other edge devices next to the data source. For example, an energy company might install some server racks and a remote LAN within a wind turbine to collect and process the data it generates.
II. Compute capacity in Internet of Things (IoT) devices
The company uses sensors with enough compute capacity to process data using predetermined filtering rules before transmission.
III. Regional edge servers
A company uses cloud services to process data from several different sensors within a single region. Cloud providers can localize the cloud services so that computing takes place on edge servers local to the company’s required region.
B. Downstream applications
Downstream applications prioritize data delivery to end users. Examples include live video streaming in media and entertainment, online gaming, or virtual reality video feeds. Edge computing for downstream use cases focus on reducing network latency so users experience events as they take place.
Here are some examples of downstream edge computing.
I. Caching
A company sets up a content delivery network (CDN) that caches content on edge servers geographically closer to the users, thus reaching their computers much faster.
II. Cloud edge services
You can use a cloud computing service to run latency-sensitive portions of your application local to endpoints and resources in a specific geography.
III. Mobile edge computing
A company uses mobile edge computing infrastructure such as 5G networks and 5G-based mobile cloud computing services to develop, deploy, and scale ultra-low-latency applications.
What is the difference between edge computing and cloud computing?
Edge computing is running workloads at the edge—that is, closer to devices and end users. On the other hand, cloud computing is a broad term that includes running all types of workloads in a cloud service provider’s data center.
However, it is important to note that cloud service providers also provide edge computing services. For example, AWS edge services deliver data processing, analysis, and storage close to your endpoints, allowing you to deploy APIs and tools to locations outside AWS data centers.
Drivers of Edge and Fog Computing
Cloud computing is being pushed to its limits by the needs of the services and applications it supports, from data storage and processing to system responsiveness. In many cases, more bandwidth or computing power isn’t enough to deliver on the requirements to process data from connected devices more quickly and generate immediate insights and action in near real-time. These gaps are driving the adoption and use of edge computing.
Key contributing factors to challenges in the cloud include:
- Latency. More industries are implementing applications that require rapid analysis and response. Cloud computing alone can’t keep up with these demands because of the latency introduced by network distance from the data source, resulting in inefficiency, lag time, and poor customer experiences.
- Bandwidth. Adding transmission bandwidth or more processing power could overcome latency issues. However, as companies continue to increase the number of edge devices on their network and the amount of data they generate, the cost to send data to the cloud may reach impractical levels that could be alleviated if data can be processed, stored, and analyzed at the edge.
- Security and privacy. Securing sensitive data, such as private medical records, at the edge and transmitting less data across the internet could help increase security by reducing the risk of interception. In addition, some governments or customers may require that data remain in the jurisdiction where it was created. In healthcare, for example, there may even be local or regional requirements to limit the storage or transmission of personal data.
- Connectivity. Lack of persistent internet connectivity can impede cloud computing, but a variety of network connectivity options make edge-to-cloud computing feasible. For example, 5G provides a high-bandwidth, low-latency connection for rapid data transfer and service delivery from the edge.
- AI. With the need for actionable intelligence in near real-time, companies need AI at the data source to allow faster processing and to take advantage of the potential in previously untapped data.
Benefits of Edge and Fog Computing
Edge computing has emerged as one of the most effective solutions to network problems associated with moving huge volumes of data generated in today’s world.
Edge computing is becoming more popular because it allows enterprises to collect and analyze their raw data more efficiently. More than ever, organizations need instant access to their data to make informed decisions about their operational efficiency and business functions. When appropriately used, edge computing has the potential to help organizations improve safety and performance, automate processes, and improve user experience.
Here are some benefits of edge computing.
- Improved network traffic management. Minimizing the amount of data sent over the network to the cloud can reduce the bandwidth and costs of transmitting and storing large volumes of data.
- Greater reliability. The amount of data that networks can transmit at one time is limited. For locations with subpar internet connectivity, being able to store and process data at the edge improves reliability when the cloud connection is disrupted.
1. Eliminates Latency
Latency refers to the time required to transfer data between two points on a network. Large physical distances between these two points coupled with network congestion can cause delays. As edge computing brings the points closer to each other, latency issues are virtually nonexistent.
In many industries, technology demands almost instant transfer of data. Take the example of a piece of robotic machinery on a factory floor. If a production incident makes it unsafe for a robot to keep operating, it needs to receive that information as fast as possible so it can shut down.
Moving data processing and analysis to the edge helps speed system response, enabling faster transactions and better experiences that could be vital in near real-time applications, like autonomous vehicle operation.
2. Saves Bandwidth
Bandwidth refers to the rate at which data is transferred on a network. As all networks have a limited bandwidth, the volume of data that can be transferred and the number of devices that can process this is limited as well. By deploying the data servers at the points where data is generated, edge computing allows many devices to operate over a much smaller and more efficient bandwidth.
3. Reduces Congestion
Although the Internet has evolved over the years, the volume of data being produced everyday across billions of devices can cause high levels of congestion. In edge computing, there is a local storage and local servers can perform essential edge analytics in the event of a network outage.
Improved data security
With edge computing, the majority of data is processed and stored locally. Any information that needs to be sent back to the data center can be encrypted before transmission. Enterprises also use edge computing to comply with data sovereignty laws, such as the General Data Protection Regulation (GDPR), by keeping any sensitive data close to the source.
With proper implementation, an edge computing solution may increase data security by limiting the transmission of data over the internet.
Increased productivity
Enterprises improve operational and employee productivity by responding more quickly to information. By analyzing data collected at the source, organizations can improve areas of their facilities, infrastructure, or equipment that are underperforming. Edge computing can be teamed with artificial intelligence and machine learning tools to derive business intelligence and insights that helps employees and enterprises perform more productively.
Remote data collection
It is challenging to collect data from places with unreliable connectivity and bandwidth. Establishing compute and data storage capabilities at the network edge helps enterprises collect and transmit data from distant oil fields, industrial zones, and offshore vessels.
Reduced costs
Sending large quantities of data from its origin to centralized data centers is expensive because it requires more bandwidth. The edge computing model allows you to decrease the amount of data being sent from sites to data centers because end users only send critical data. Depending on how much data your business sends and processes, this could significantly save operating costs.
Reliable performance
Edge computing often takes place in remote areas where internet connectivity is scarce. By setting up an edge computing environment, enterprises ensure that their operations reliably process, analyze, and store data. This significantly reduces the chances of suffering from operational downtime caused by network or connectivity disruption.
Drawbacks of Edge and Fog Computing
Although edge computing offers a number of benefits, it is still a fairly new technology and far from being foolproof. Here are some of the most significant drawbacks of edge computing:
1. Implementation Costs
The costs of implementing an edge infrastructure in an organization can be both complex and expensive. It requires a clear scope and purpose before deployment as well as additional equipment and resources to function.
2. Incomplete Data
Edge computing can only process partial sets of information which should be clearly defined during implementation. Due to this, companies may end up losing valuable data and information.
3. Security
Since edge computing is a distributed system, ensuring adequate security can be challenging. There are risks involved in processing data outside the edge of the network. The addition of new IoT devices can also increase the opportunity for the attackers to infiltrate the device.
Better Outcomes Start at the Edge
Edge computing provides an unprecedented opportunity for enterprises and service providers to unlock the value in data. With the right partner, a company can make the most out of data at every point.
Intel—with tens of thousands of edge deployments generating real value, hundreds of market-ready solutions, standards-based technology, and the world’s most mature developer ecosystem—can help you make the intelligent edge real.
Why edge computing?
The explosive growth and increasing computing power of IoT devices has resulted in unprecedented volumes of data. And data volumes will continue to grow as 5G networks increase the number of connected mobile devices.
The unprecedented scale and complexity of data that’s created by connected devices has outpaced network and infrastructure capabilities.
Sending all that device-generated data to a centralized data center or to the cloud causes bandwidth and latency issues. Edge computing offers a more efficient alternative; data is processed and analyzed closer to the point where it's created. Because data does not traverse over a network to a cloud or data center to be processed, latency is significantly reduced.
Edge computing — and mobile edge computing on 5G networks — enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.
Devices at the edge: Harnessing the potential
From connected vehicles to intelligent bots on the factory floor, the amount of data from devices being generated in our world is higher than ever before, yet most of this IoT data is not exploited or used at all.
Edge computing harnesses growing in-device computing capability to provide deep insights and predictive analysis in near-real time. This increased analytics capability in edge devices can power innovation to improve quality and enhance value.
It also raises important strategic questions:
§ How do you manage the deployment of workloads that perform these types of actions in the presence of increased compute capacity?
§ How can you use the embedded intelligence in devices to influence operational processes for your employees, your customers and your business more responsively?
A well-considered approach to edge computing can keep workloads up-to-date according to predefined policies, can help maintain privacy, and will adhere to data residency laws and regulations.
But this process is not without its challenges. An effective edge computing model should address network security risks, management complexities, and the limitations of latency and bandwidth. A viable model should help you:
- Manage your workloads across all clouds and on any number of devices
- Deploy applications to all edge locations reliably and seamlessly
- Maintain openness and flexibility to adopt to evolving needs
- Operate more securely and with confidence
Discover the future of edge computing in your industry
CIOs in banking, mining, retail, or just about any other industry, are building strategies designed to personalize customer experiences, generate faster insights and actions, and maintain continuous operations. This can be achieved by adopting a massively decentralized computing architecture, otherwise known as edge computing.
Banks may need edge to analyze ATM video feeds in real-time in order to increase consumer safety. Mining companies can use their data to optimize their operations, improve worker safety, reduce energy consumption and increase productivity. Retailers can personalize the shopping experiences for their customers and rapidly communicate specialized offers. Companies that leverage kiosk services can automate the remote distribution and management of their kiosk-based applications, helping to ensure they continue to operate even when they aren’t connected or have poor network connectivity.
Manage the distribution of software at massive scale
Reduce unnecessary administrators, save the associated costs and deploy software where and when it’s needed.
Leverage open source technology
Leverage an edge computing solution that nurtures the ability to innovate and can handle the diversity of equipment and devices in today’s marketplace.
Address security concerns
Know that the right workloads are on the right machine at the right time. Make sure there’s an easy way to govern and enforce the policies of your enterprise.
Engage a trusted partner with deep industry expertise
Find a vendor with a proven multicloud platform and a comprehensive portfolio of services designed to increase scalability, accelerate performance and strengthen security in your edge deployments. Ask your vendor about extended services that maximize intelligence and performance at the edge.
Explore 5G and edge computing solutions
Edge computing with 5G creates tremendous opportunities in every industry. It brings computation and data storage closer to where data is generated, enabling better data control and reduced costs, faster insights and actions, and continuous operations. In fact, by 2025, 50% of enterprise data will be processed at the edge.
¹"What Edge Computing Means for Infrastructure and Operations Leaders," Rob van der Meulen, Gartner Research, October 2018
²"The Internet of Things: Mapping the Value Beyond the Hype," McKinsey Global Institute, McKinsey & Company, June 2015
Why do businesses use edge computing?
Businesses use edge computing to improve the response times of their remote devices and to get richer, more timely insights from device data. Edge computing makes real-time computing possible in locations where it would not normally be feasible and reduces bottlenecking on the networks and datacenters that support edge devices.
Without edge computing, the massive volume of data generated by edge devices would overwhelm most of today's business networks, hampering all operations on an affected network.
How does edge computing work?
To make real-time functionality possible for smart apps and IoT sensors, edge computing solves three interrelated challenges:
- Connecting a device to a network from a remote location.
- Slow data processing due to network or computing limitations.
- Edge devices causing network bandwidth issues.
Advancements in networking technologies, like 5G wireless, have made it possible to solve these challenges on a global, commercial scale. 5G networks can handle vast amounts of data—going to and from devices and datacenters—in near-real time. (There's even a wireless network that uses cryptocurrency to encourage users to extend coverage to harder-to-reach areas.)
But advances in wireless technology are only part of the solution for making edge computing work at scale. Being selective about which data to include and exclude in data streams over networks is also critical to reducing latency and delivering real-time results. For example:
A security camera in a remote warehouse uses AI to identify suspicious activity and only sends that specific data to the main datacenter for immediate processing. So, rather than the camera burdening the network 24 hours per day by constantly transmitting all of its footage, it only sends relevant video clips. This frees up the company's network bandwidth and compute processing resources for other uses.
More use cases made possible by edge computing:
- A retail store 1,000 miles from the company's primary datacenter uses wireless point-of-sale devices to instantly process payments.
- An oil rig in the middle of the ocean uses IoT sensors and AI to quickly detect equipment malfunctions before they worsen.
- An irrigation system in a remote farm field adjusts the amount of water it uses in real time by detecting soil moisture levels.
Why is Edge and Fog computing important?
From workplace safety to security and productivity, the benefits of edge computing are vast:
More efficient operations. Edge computing helps enterprises optimize their day-to-day operations by rapidly processing large volumes of data at or near the local sites where that data is collected. This is more efficient than sending all of the collected data to a centralized cloud or a primary datacenter several time zones away, which would cause excessive network delays and performance issues.
Faster response times. Bypassing centralized cloud and datacenter locations allows companies to process data more quickly and reliably, in real time or close to it. Edge computing enables devices at or near a network's edge to instantly alert key personnel and equipment to mechanical failures, security threats, and other critical incidents so that swift action can be taken.
Greater employee productivity. Edge computing enables businesses to more quickly deliver the data that workers need to complete their job duties as efficiently as possible. And in smart workplaces that take advantage of automation and predictive maintenance, edge computing keeps the equipment that workers need running smoothly, without interruptions or easily preventable mistakes.
Improved workplace safety. In work environments where faulty equipment or changes to working conditions can cause injuries or worse, IoT sensors and edge computing can help keep people safe. For example, on offshore oil rigs, oil pipelines, and other remote industrial use cases, predictive maintenance and real-time data analyzed at or close to the equipment site can help increase the safety of workers and minimize environmental impacts.
Consider an oil drilling rig operating in the middle of the ocean. Sensors that track information like drill depth, surface pressure, and fluid flow rate can help keep the machinery on a rig running smoothly and help keep workers and the environment safe. To do this without slowing down the network unnecessarily, the sensors send only the data about critical maintenance needs, equipment malfunctions, and worker safety details over the network, and this makes it possible to identify and react to issues in close to real time.
Functionality in far-flung locations. Edge computing makes it easier to utilize data collected at remote sites where internet connectivity is intermittent or network bandwidth is limited—for example, aboard a fishing vessel in the Bering Sea or at a vineyard in the Italian countryside. Operational data like water or soil quality can be constantly monitored by sensors and acted upon when needed. Once internet connectivity becomes available, the relevant data can be transmitted to a central datacenter for processing and analysis.
Heightened security. For enterprises, the security risk of adding thousands of internet-connected sensors and devices to their network is a real concern. Edge computing helps to mitigate this risk by allowing enterprises to process data locally and store it offline. This decreases the data transmitted over the network and helps enterprises be less vulnerable to security threats.
Data sovereignty. When gathering, processing, storing, and otherwise using customer data, organizations must adhere to the data privacy regulations of the country or region where that data is collected or stored—for instance, the European Union's General Data Protection Regulation (GDPR). Moving data to the cloud or to a primary datacenter across national borders can make adhering to data sovereignty regulations difficult, but with edge computing, businesses can ensure that they're honoring local data sovereignty guidelines by processing and storing data near where it was collected.
Reduced IT costs. With edge computing, businesses can optimize their IT expenses by processing data locally rather than in the cloud. Besides minimizing companies' cloud processing and storage costs, edge computing decreases transmission costs by weeding out unnecessary data at or near the location where it's collected.
Edge and Fog computing hardware and networking
Edge devices include smart cameras, thermometers, robots, drones, vibration sensors, and other IoT devices. Although some devices have built-in compute, memory, and storage capabilities, not all do.
Processors are the CPUs, GPUs, and associated memory that power edge computing systems. For example, the more CPU power an edge computing system has, the faster it can perform tasks and the more workloads it can support.
Cluster/servers are groups of servers that process data at an edge location, such as on a factory floor or at a commercial fishery. Edge cluster/servers are often tasked with running enterprise apps, enterprise workloads, and an organization's shared services.
Gateways are edge cluster/servers that perform essential network functions like enabling wireless connectivity, providing firewall protection, and processing and transmitting edge device data.
Routers are edge devices that connect networks. For example, a router at the edge may be used to connect an enterprise's LANs with a WAN or the internet.
Switches, which are also referred to as access nodes, connect several devices in order to create a network.
Nodes is a catch-all term used to describe the edge devices, servers, and gateways that enable edge computing.
What are some of the characteristics of Edge and Fog hardware?
Fanless and ventless. With reliability being key, especially in industries where equipment malfunctions can halt production and endanger workers, edge hardware must be closed off from dust, dirt, moisture, and other matter that could compromise it.
Temperature resistant. Edge hardware is often placed outside in freezing, sweltering, and wet climates. Sometimes it's even placed underwater. Being able to withstand sub-zero and near-boiling temperatures is a must in many cases.
Impervious to sudden movements. The hardware needs to be able to withstand vibrations and shocks by machinery or the natural elements. Building these components without fans, cables, and other internal parts that can easily get shaken loose or break is essential.
Small form factor. With edge computers, compact is the name of the game. They often need to fit into cramped spots. Examples include smart cameras placed on walls, shelves, and ceilings and smart thermometers packed in shipping boxes.
Equipped with ample storage. Edge computers that collect vast amounts of data from edge devices can require significant data storage. They must also be able to rapidly access and transfer large quantities of data.
Compatible with new and legacy equipment. Edge computers, particularly those operating in production or factory settings, typically feature a variety of I/O ports, including USB, COM, Ethernet, and general purpose ports. This enables them to connect with both new and legacy production equipment, machinery, devices, sensors, and alarms.
Built with multiple connectivity options. Edge computers typically support both wireless and wired connectivity. That way, if connecting to the internet wirelessly is not an option at a remote commercial site like a farm or a ship at sea, the computer can still connect to the internet to transmit data.
Able to support several types of power inputs. Edge computers often support a variety of power inputs to accommodate the wide range of power inputs they may encounter in remote locations. They also require surge, overvoltage, and power protection features to help prevent electrical damage.
Protected from cyberattacks. Edge devices, which often cannot be managed by network administrators as rigorously as their on-premises and cloud counterparts, tend to be more vulnerable to bad actors. To help safeguard them from malware and other cyberattacks, edge devices must be equipped with security tools like firewalls and network-based intrusion detection systems.
Tamper resistant. Because edge computing devices are often used in far-flung locations where they cannot be consistently monitored, they must be built to be kept secure from theft, vandalism, and unauthorized physical access.
Edge and Fog computing use cases and examples
Which industries use edge computing?
The high speeds and low latency of data transfer, combined with the relative ease of installing edge devices, have seen edge computing widely used across industries
Manufacturing
The proliferation of Internet of Things (IoT) devices such as sensors and gateways has made edge computing systems prevalent in the manufacturing industry. Manufacturers utilize edge computing solutions to enable automation, collect data on-site, improve production efficiency, and allow rapid machine-to-machine communication
Sensors on factory floors can be used to monitor equipment for routine maintenance issues and malfunctions, as well as keeping workers safe. In addition, smart equipment in factories and warehouses can increase productivity, reduce production costs, and provide quality control. And keeping data and analysis on the factory floor rather than sending it to a centralized datacenter can help avoid expensive and potentially dangerous delays.
Autonomous vehicles
Autonomous vehicles like self-driving cars are fitted with several IoT sensors that collect large amounts of data every second. They require real-time data processing for instant response and cannot rely on a remote server for split-second decision-making.
Additionally, autonomous vehicles interact more efficiently if they communicate with each other first, as opposed to sending data on weather conditions, traffic, accidents, or detours to a remote server. Edge computing is critical technology for ensuring their safety and ability to accurately judge road conditions.
There is almost no margin for error with self-driving cars, taxis, vans, and trucks. Edge computing makes it possible for them to respond instantly and correctly to traffic signals, road conditions, obstacles, pedestrians, and other vehicles in real time.
Energy
Energy companies use edge computing to collect and store data on oil rigs, gas fields, wind turbines, and solar farms. Rig operators commonly deploy edge artificial intelligence to detect hazards and optimize and inspect their pipelines. Edge computing helps the industry improve operational efficiency, keep its workers safe, and forecast when maintenance work needs to be undertaken.
Power and utility companies use IoT sensors and edge computing to increase efficiency, automate the power grid, simplify maintenance, and make up for shortfalls in network connectivity at remote locations. Utility towers, wind farms, oil rigs, and other remote energy sources can be equipped with IoT devices that are able to withstand harsh weather and other environmental challenges. These devices can process data at or near the energy site and send only the most relevant data to the main datacenter. In the oil and gas sectors, IoT sensors and edge computing provide essential real-time safety alerts that notify key personnel about necessary repairs and dangerous equipment malfunctions that could lead to explosions or other catastrophes.
Healthcare
Edge devices monitor critical patient functions such as temperature and blood sugar levels. Edge computing allows the healthcare sector to store this patient data locally and improve privacy protection. Medical facilities also reduce the data volume they send to central locations and cut the risk of data loss.
The uses for edge computing in the healthcare sector are vast. Temperature sensors shipped with vaccines can help ensure that they maintain their integrity throughout the supply chain. At-home medical equipment like smart CPAP machines and heart monitors can collect patient data and send relevant information to a patient's doctor and healthcare network. Hospitals can better serve patients by using IoT technology to track patients' vital signs and to more accurately track the location of equipment like wheelchairs and gurneys.
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.4
Branch offices. Smart devices and sensors reduce the number of resources needed to run a company's secondary offices. Consider internet-connected HVAC controls, sensors that detect when copiers require repairs, and security cameras. By sending only the most essential device alerts to a company's primary datacenter, edge computing helps prevent server congestion and lag time while greatly increasing response time to facility issues.
Farming. Edge computing can help boost agricultural efficiency and yields. Weather-resistant IoT sensors and drones can help farmers monitor equipment temperature and performance; analyze soil, light, and other environmental data; optimize the amount of water and nutrients used on crops; and time harvests more efficiently. Edge computing makes using IoT technology more cost-effective even in remote locations where network connectivity is limited.
Retail. Large retailers often gather massive amounts of data throughout their individual stores. By using edge computing, retailers can extract richer business insights and react to them in real time. For example, retailers can collect data on customer foot traffic, track point-of-sale numbers, and monitor the success of promotional campaigns across all of their stores and use this local data to manage inventory more effectively and make faster, more informed business decisions.
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.3
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.
One of the best ways to implement edge computing is in smart home devices. In smart homes, a number of IoT devices collect data from around the house. The data is then sent to a remote server where it is stored and processed. This architecture can cause a number of problems in the event of a network outage. Edge computing can bring the data storage and processing centers close to the smart home and reduce backhaul costs and latency.
Another use case of edge computing is in the cloud gaming industry. Cloud gaming companies are looking to deploy their servers as close to the gamers as possible. This will reduce lags and provide a fully immersive gaming experience.
Edge and Fog computing services
As edge computing has grown toward widespread adoption, the types of related services to support its use has also grown. Today's edge computing services go far beyond just devices and networking to include solutions to:
- Run AI, analytics, and other business capabilities on IoT devices.
- Consolidate edge data at scale and eliminate data silos.
- Deploy, manage, and help secure edge workloads remotely.
- Optimize the costs of running edge solutions.
- Enable devices to react faster to local changes.
- Ensure that devices operate reliably after extended offline periods.
The latest solutions include services to help incorporate edge computing with common technologies like databases, operating systems, cybersecurity, blockchain ledgers, and infrastructure management, to name just a few.
AI and analytics edge computing services
AI and analytics services for the edge are especially valuable for improving automation, productivity, maintenance, and safety. Here's just one example: Deploying predictive models to factory cameras can help detect quality control and safety issues. In this case, the solution triggers an alert and processes the data locally to execute an immediate action or sends it to the cloud for instant analysis before taking action.
What are some AWS edge computing use cases?
A large number of leading enterprises utilize AWS edge computing tools. We give three prominent examples below.
Volkswagen Group
Volkswagen, one of the world's leading automobile groups, uses AWS IoT, machine learning, and edge services to power its Industrial Cloud. It can connect data from more than 120 manufacturing plants to improve efficiency and uptime at its plant facilities, improve production flexibility, and drive vehicle quality standards.
Hulu
The streaming platform Hulu utilizes AWS edge networking services to ensure customers enjoy stellar content and user experiences, even when user traffic is high. Hulu uses AWS services to provide scalable, agile, and cost-effective infrastructure.
Riot Games
Riot Games develops, publishes, and supports the most player-focused games in the world, including League of Legends, one of the world’s most popular PC games. With the 2020 global launch of VALORANT, a team-based tactical shooter game, Riot wanted to reduce “peeker’s advantage” caused by latency and ensure competitive integrity. Riot uses AWS Outposts to rapidly deploy game servers and reduce latency by 10 to 20 milliseconds, minimizing peeker’s advantage and creating a level playing field for all players.
How can AWS help you with your edge computing requirements?
AWS for the Edge brings the world’s most capable and secure cloud closer to your endpoints and users. AWS is the only provider that extends infrastructure, services, APIs, and tools offered in the cloud as a fully managed service to virtually any on-premises data center, co-location space, or edge facility.
Take advantage of managed hardware deployed in locations outside AWS data centers— extending secure edge computing capabilities to metro areas, 5G networks, on-premises locations, and disconnected or remote locations. You can employ capabilities purpose-built for specific edge use cases, and choose from more than 200 integrated device services to deploy edge applications to billions of devices quickly and easily.
Here are ways AWS can help with edge computing:
- AWS Outposts extends your AWS infrastructure and services to virtually anywhere and enjoys a consistent hybrid experience
- AWS Storage Gateway provides on-premises access to virtually unlimited cloud storage
- AWS Snow Family devices run operations in austere, non-data-center environments, and in locations where there is a lack of consistent network connectivity
- Amazon SageMaker Edge Manager optimizes, secures, monitors, and maintains machine learning models on fleets of edge devices
Next
The adoption of edge computing has brought about data analytics to a whole new level. More and more companies are relying on this technology for data-driven operations that require lightning-fast results.
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