Edge Computing is Getting 'Edgier'.

Moving the location of where information is processed or is stored in a system can have positive, scalable effects that drastically improve a business’s operations. Just as warehousing produce in regional or local distribution centers ensures fresher produce at the point of sale, increasing the availability of data via edge computing can result in savings of both time and money.??

Edge computing moves processing from centralized servers located hundreds or thousands of miles away to localized servers and devices in order to more quickly process data in applications. Instead of relying entirely on cloud computing providers or data centers to process all of the data, edge computing processes data initially on a local server, whether that’s in a personal device or a small data center in the user’s geographical region.

Edge Computing Use Cases

1. Autonomous vehicles

Autonomous platooning of truck convoys will likely be one of the first use cases for autonomous vehicles. Here, a group of trucks travels close behind one another in a convoy, saving fuel costs and decreasing congestion. With edge computing, it will be possible to remove the need for drivers in all trucks except the front one, because the trucks will be able to communicate with each other with ultra-low latency.

2. Remote monitoring of assets in the oil and gas industry

Oil and gas failures can be disastrous. Their assets, therefore, need to be carefully monitored.

However, oil and gas plants are often in remote locations. Edge computing enables real-time analytics with processing much closer to the asset, meaning there is less reliance on good quality connectivity to a centralized cloud.

3. Smart grid

Edge computing will be a core technology in more widespread adoption of smart grids and can help allow enterprises to better manage their energy consumption.

Sensors and IoT devices connected to an edge platform in factories, plants and offices are being used to monitor energy use and analyse their consumption in real-time. With real-time visibility, enterprises and energy companies can strike new deals, for example where high-powered machinery is run during off-peak times for electricity demand. This can increase the amount of green energy (like wind power) an enterprise consumes.

4. Predictive maintenance

Manufacturers?want to be able to analyse and detect changes in their production lines before a failure occurs.

Edge computing helps by bringing the processing and storage of data closer to the equipment. This enables IoT sensors to monitor machine health with low latencies and perform analytics in real-time.

5. In-hospital patient monitoring

Healthcare contains?several edge opportunities. Currently, monitoring devices (e.g. glucose monitors, health tools, and other sensors) are either not connected, or where they are, large amounts of unprocessed data from devices would need to be stored on a 3rd?party cloud. This presents security concerns for healthcare providers.

An edge on the hospital site could process data locally to maintain data privacy. Edge also enables right-time notifications to practitioners of unusual patient trends or behaviors (through analytics/AI), and the creation of 360-degree view patient dashboards for full visibility.

6. Virtualised radio networks and 5G (vRAN)

Operators are increasingly looking to virtualize parts of their mobile networks (vRAN). This has both cost and flexibility benefits. The new virtualized RAN hardware needs to do complex processing with low latency. Operators will therefore need edge servers to support virtualizing their RAN close to the cell tower.

7. Cloud gaming

Cloud gaming, a new kind of gaming which streams a live feed of the game directly to devices, (the game itself is processed and hosted in data centres) is highly dependent on latency.

Cloud gaming companies are looking to build edge servers as close to gamers as possible in order to reduce latency and provide a fully responsive and immersive gaming experience.

8. Content delivery

By caching content – e.g. music, video stream, web pages – at the edge, improvements to content deliver can be greatly improved. Latency can be reduced significantly. Content providers are looking to distribute CDNs even more widely to the edge, thus guaranteeing flexibility and customisation on the network depending on user traffic demands.

9. Traffic management

Edge computing can enable more effective city traffic management. Examples of this include optimizing bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in the future, managing autonomous car flows.

With edge computing, there is no need to transport large volumes of traffic data to the centralized cloud, thus reducing the cost of bandwidth and latency.

10. Smart homes

Smart homes rely on?IoT devices?collecting and processing data from around the house. Often this data is sent to a centralized remote server, where it is processed and stored. However, this existing architecture has problems around backhaul cost, latency, and security.

By using edge compute and bringing the processing and storage closer to the smart home, backhaul and roundtrip time is reduced, and sensitive information can be processed at the edge. As an example, the time taken for voice-based assistant devices such as Amazon’s Alexa to respond would be much faster.

Edge Computing Trends

1. Edge meets more AI/ML

Until recently, pre-processing of data via near-edge technologies or gateways had its share of challenges due to the increased complexity of data solutions, especially in use cases with a high volume of events or limited connectivity. Now, AI/ML-optimized hardware, container-packaged analytics applications, frameworks such as?TensorFlow Lite?and tinyML, and open standards such as the?Open Neural Network Exchange (ONNX)?are encouraging machine learning interoperability and making on-device machine learning and data analytics at the edge a reality.

Machine learning at the edge will enable quicker, faster decision-making.Machine learning at the edge will enable faster decision-making. Moreover, the amalgamation of edge and AI will further drive real-time personalization

But without proper thresholds in place, anomalies can slowly become standards,Advanced policy controls will enable greater confidence in the actions made as a result of the data collected and interpreted from the edge.”?

2. Cloud and edge providers explore partnerships

Organizations will improve business agility by integrating edge data with applications built on cloud platforms by 2024. That will require partnerships across cloud and communications service providers, with some pairing up already beginning between wireless carriers and the major public cloud providers.

The systems that organizations can leverage to enable real-time analytics are already starting to expand beyond traditional data centers and deployment locations. Devices and computing platforms closer to end customers and/or co-located with real-world assets will become an increasingly critical component of this IT portfolio. This edge computing strategy will be part of a larger computing fabric that also includes public cloud services and on-premises locations.

In this scenario, edge provides immediacy and cloud supports big data computing.

3. Edge management takes center stage

As edge computing becomes as ubiquitous as cloud computing, there will be increased demand for scalability and centralized management. IT leaders deploying applications at scale will need to invest in tools to harness step change in their capabilities so that edge computing solutions and data can be custom-developed right from the processor level and deployed consistently and easily just like any other mainstream compute or storage platform.

The traditional approach to data center or cloud monitoring won't work at the edge.

The traditional approach to data center or cloud monitoring won’t work at the edge, notes Williams of AHEAD. Because of the rather volatile nature of edge technologies, organizations should shift from monitoring the health of devices or the applications they run to instead monitor the digital experience of their users. This user-centric approach to monitoring takes into consideration all of the components that can impact user or customer experience while avoiding the blind spots that often lie between infrastructure and the user. If there is any remaining argument that hybrid or multi-cloud is a reality, the growth of edge solidifies this truth: When we think about where data and applications live, they will be in many places.

4. IT and operational technology begin to converge

Resiliency is perhaps the business term of the year, thanks to a pandemic that revealed most organizations’ weaknesses in this area. IoT-enabled devices (and other connected equipment) drive the adoption of edge solutions where infrastructure and applications are being placed within operations facilities. This approach will be critical for real-time inference using AI models and digital twins, which can detect changes in operating conditions and automate remediation. will grow from less than 20 percent today to more than 90 percent in 2024 as IT and operational technology converge. Organizations will begin to prioritize not just extracting insight from their new sources of data, but integrating that intelligence into processes and workflows using edge capabilities.

Mobile edge computing (MEC) will be a key enabler of supply chain resilience in 2021. Through MEC, the ecosystem of supply chain enablers has the ability to deploy artificial intelligence and machine learning to access near real-time insights into consumption data and predictive analytics as well as visibility into the most granular elements of highly complex demand and supply chains. For organizations to compete and prosper, IT leaders will need to deliver MEC-based solutions that enable an end-to-end view across the supply chain available 24/7 – from the point of manufacture or service?throughout its distribution.

5. Edge eases connected ecosystem adoption

Edge not only enables and enhances the use of IoT, but it also makes it easier for organizations to participate in the connected ecosystem with minimized network latency and bandwidth issues. Enterprises can leverage edge computing’s scalability to quickly expand to other profitable businesses without incurring huge infrastructure costs. Enterprises can now move into profitable and fast-streaming markets with the power of edge and easy data processing.

6. COVID-19 drives innovation at the edge

From social distancing to thermal imaging, safety device assurance and operational changes such as daily cleaning and sanitation activities, computer vision is an essential technology to accelerate solutions that turn raw IoT data (from video/cameras) into actionable insights. Retailers, for example, can use computer vision solutions to identify when people are violating the store’s social distance policy.

7. Private 5G adoption increases

Use cases such as factory floor automation augmented and virtual reality within field service management, and autonomous vehicles will drive the adoption of private 5G networks. Expect more maturity in this area in the year ahead, Ranjan says.

8. Edge improves data security

Data efficiency is improved at the edge compared with the cloud, reducing internet and data costs. The additional layer of security at the edge enhances the user experience. Edge computing is also not dependent on a single point of application or storage Rather, it distributes processes across a vast range of devices.