Data lifecycles.The perennial problem with today’s data glut is that so much of that data is unnecessary. Consider a medical monitoring device — it’s just the problem data that’s critical, and there’s little point in keeping days of normal patient data. Most of the data involved in real-time analytics is short-term data that isn’t kept over the long term.
There are reasons to do so utilizing other technologies, too, including license cost, data regulatory regime, differentiation, and so on. At the same time, hyperscalers provide a focal point for an ecosystem of software developers, which other technology solutions cannot provide at the same breadth. Location of compute– on-premises, near-premises (operator’s edge), or in a central data center.
This instantaneous decision making is a necessity in autonomous vehicles, for obvious safety reasons. In simplest terms, edge computing moves some portion of storage and compute resources out of the central data center and closer to the source of the data itself. Rather than transmitting raw data to a central data center for processing and analysis, that work is instead performed where the data is actually generated — whether that’s a retail store, a factory floor, a sprawling utility or across a smart city. Only the result of that computing work at the edge, such as real-time business insights, equipment maintenance predictions or other actionable answers, is sent back to the main data center for review and other human interactions. Enterprises that wish to employ AI, ML, or AR/VR/MR will require compute capacity nearby with high performance and low latency.
Next, any competitor could easily find alternative locations for their setup – including the client’s premises. And finally, telecom operators will need to partner with hyperscalers in all cases. Edge computing will evolve both on client premises as well as slightly remote. Any comprehensive edge computing portfolio must therefore include on-premises and near-premises solutions. The edge is a clear opportunity for hyperscalers to sell their cloud technology stack – and ecosystem of application developers. There is a need to have compute infrastructure outside the central data center or cloud – not in on-premises, self-built environments but somewhere in between.
Edge computing gained notice with the rise of IoT and the sudden glut of data such devices produce. But with IoT technologies still in relative infancy, the evolution of IoT devices will also have an impact on the future development of edge computing. One example of such future alternatives is the development of micro modular data centers . The MMDC is basically a data center in a box, putting a complete data center within a small mobile system that can be deployed closer to data — such as across a city or a region — to get computing much closer to data without putting the edge at the data proper.
Edge computing features such as lane-departure warning and self-parking applications are already widely available. And as the ability of vehicles to interact with their environment becomes more widespread, so will the need for a fast and responsive network. Autonomous vehicles will operate in concert with other connected vehicles, traffic management systems, roadside units and pedestrians on busy thoroughfares and at intersections.
In the case of cloud computing, synchronization refers to the process of transferring data from the edge to the center, then back to the edge. This, however, inevitably leads to latency, especially when the volume of data is large. Because 5G will power lower latency and higher speeds, it and edge computing go hand in hand to deliver key benefits in migrating network applications to the edge.
On average, most monitoring data collected by IoT sensors tends to be standard “heartbeat” data, which simply indicates that systems are functioning normally. There’s no need to transmit that kind of data to the cloud or a distant corporate data center. Edge security.Finally, edge computing offers an additional opportunity to implement andensure data security. Although cloud providers have IoT services and specialize what is edge computing with example in complex analysis, enterprises remain concerned about the safety and security of data once it leaves the edge and travels back to the cloud or data center. Autonomy.Edge computing is useful where connectivity is unreliable or bandwidth is restricted because of the site’s environmental characteristics. Examples include oil rigs, ships at sea, remote farms or other remote locations, such as a rainforest or desert.
The growth of artificial intelligence and machine learning capabilities are also expanding the range of edge computing use cases. Transportation.Autonomous vehicles require and produce anywhere from 5 TB to 20 TB per day, gathering information about location, speed, vehicle condition, road conditions, traffic conditions and other vehicles. And the data must be aggregated and analyzed in real time, while the vehicle is in motion. This requires significant onboard computing — each autonomous vehicle becomes an “edge.” In addition, the data can help authorities and businesses manage vehicle fleets based on actual conditions on the ground. Computing tasks demand suitable architectures, and the architecture that suits one type of computing task doesn’t necessarily fit all types of computing tasks. Edge computing has emerged as a viable and important architecture that supports distributed computing to deploy compute and storage resources closer to — ideally in the same physical location as — the data source.
From a security standpoint, data at the edge can be troublesome, especially when it’s being handled by different devices that might not be as secure as a centralized or cloud-based system. As the number of IoT devices grow, it’s imperative that IT understand the potential security issues around these devices, and to make sure those systems can be secured. This includes making sure that data is encrypted, and that the correct access-control methods and evenVPNtunneling is utilized. As mentioned above, intelligent traffic management systems will play a key role the adoption of autonomous vehicles, where near-zero latency is critical. Manufacturing.An industrial manufacturer deployed edge computing to monitor manufacturing, enabling real-time analytics and machine learning at the edge to find production errors and improve product manufacturing quality. Edge computing supported the addition of environmental sensors throughout the manufacturing plant, providing insight into how each product component is assembled and stored — and how long the components remain in stock.
Research shows that the move toward edge computing will only increase over the next couple of years. Farming.Consider a business that grows crops indoors without sunlight, soil or pesticides. Using sensors enables the business to track water use, nutrient density and determine optimal harvest.
Bandwidth — Both the physical limits of available bandwidth and the cost of transmitting large quantities of data make edge computing an attractive alternative. Connect your devices with versatile modules and powerful single-board computers designed for rapid deployment and scalability. Security.Physical and logical security precautions are vital and should involve tools that emphasize vulnerability management and intrusion detection and prevention.
Other examples involve predictive analytics that can guide equipment maintenance and repair before actual defects or failures occur. Still other examples are often aligned with utilities, such as water treatment or electricity generation, to ensure that equipment is functioning properly and to maintain the quality of output. In traditional enterprise computing, data is produced at a client endpoint, such as a user’s computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. This remains a proven and time-tested approach to client-server computing for most typical business applications. Most likely, all three will form partnerships to fund, invent, and drive edge computing and showcase the results.
The manufacturer can now make faster and more accurate business decisions regarding the factory facility and manufacturing operations. Unlike cloud computing, edge computing allows data to exist closer to the data sources through a network of edge devices. Data is the lifeblood of modern business, providing valuable business insight and supporting real-time control over critical business processes and operations. Today’s businesses are awash in an ocean of data, and huge amounts of data can be routinely collected from sensors and IoT devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world.
Bandwidth.Bandwidth is the amount of data which a network can carry over time, usually expressed in bits per second. All networks have a limited bandwidth, and the limits are more severe for wireless communication. This means that there is a finite limit to the amount of data — or the number of devices — that can communicate data across the network. Although it’s possible to increase network bandwidth to accommodate more devices and data, the cost can be significant, there are still finite limits and it doesn’t solve other problems. This service is targeted at shared setups, so is less likely to be positioned on a customer premise but is close by, in the network. The deployment aspiration is to cover geographies rather than multiple singular or individual locations.
Discover the latest leading-edge insights on the industries, convergence, technological development, people and our visions to create more a more open world. With the addition of more smart devices into the mix, there are new opportunities for malicious actors to compromise them. That empowers operators to choose the best use of each to get the most out of a holistic network. Make sure there’s an easy way to govern and enforce the policies of your enterprise.
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. In the oil and gas industry, real-time responses facilitated by edge computing can prevent small problems from becoming catastrophic failures. Rugged edge computers are deployed as IoT gateways for smart agriculture applications. For example, edge computing solutions are deployed to gather information from sensors used to monitor the weather conditions, quality of soil, wetness of the soil, the sunlight, and other information that’s vital to improve the growth of crops. Moreover, edge computers can be used to predict the crop output, allowing farmers to better plan the distribution of their crops once they’re ready to be cultivated and sold. Also, edge computers are often used in greenhouses to gather real-time information on growing conditions, such as the lighting, temperature, soil condition, and humidity, allowing farmers to adjust the environments for optimal crop growth.
Edge gateways themselves are considered edge devices within an edge-computing infrastructure. Network World / IDGThink about devices that monitor manufacturing equipment on a factory floor, or an internet-connected video camera that sends live footage from a remote office. While a single device producing data can transmit it across a network quite easily, problems arise when the number of devices transmitting data at the same time grows. Instead of one video camera transmitting live footage, multiply that by hundreds or thousands of devices. Not only will quality suffer due to latency, but the costs in bandwidth can be tremendous. Rugged edge computers are often used by organizations because they can gather information from various sensors, cameras, and other devices, and they can use that information to determine when components or certain machinery fails.
But the unprecedented scale and complexity of data that’s created by connected devices has outpaced network and infrastructure capabilities. Digi Professional Services can support organizations in the implementation of virtually any edge computing use case, with everything from Python coding and BASH scripting to device configuration and on-site deployment services. Security, of course, is a critical consideration on the edge as it is everywhere.
Since the likelihood of success is relatively limited on IaaS, CaaS, PaaS, and SaaS plays related to ecosystems, we recommend that telecom operators not engage in this area. Telecom operators have mostly dropped out of the battle for these ecosystems. However, their strategy should be to endorse and support the creation of such plays to stimulate the overall market and increase margin capture from backhaul, facility, and RAN, as well as potentially moves 3 and 6 .
So far, most telecom operators have failed to capture value from computing services, and computing services providers have increased costs and CAPEX for telecom operators, without them benefiting equally . For telecom operators to succeed with edge computing, the scenarios described below would have to exist. Despite current hype around low-latency requirements being the promised land for service providers, we have not yet been able to identify use cases or business cases that are sizeable and demand low latency in the near term. In many geographically smaller https://globalcloudteam.com/ countries, latency requirements for most if not all applications are easily met when utilizing one or only very few data center locations if connected via fiber infrastructure. The technology for such real-time integration is not yet ready/ available, but with multiple equipment providers claiming to offer software-based, cloud-native, real-time networking functions, we can expect this to change. Microsoft already has announced its intent to place radio access network functionalities onto its Azure portfolio for communications service providers.
This is different from the traditional model where organizations conducted routine diagnosis and inspections, which is labor intensive and costly. Moreover, with the traditional model it is difficult to perform maintenance before a component or machine fails. With predictive maintenance, organizations can intervene and maintain machinery and equipment before the failure ever occurs. Edge computing also offers the means to process customer information locally, without data leaving the geographical region where the customer lives, which is an issue of rising concern as it pertains to privacy regulations such as the European Union’s GDPR mandates.
Management.The remote and often inhospitable locations of edge deployments make remote provisioning and management essential. IT managers must be able to see what’s happening at the edge and be able to control the deployment when necessary. Connectivity.Connectivity is another issue, and provisions must be made for access to control and reporting even when connectivity for the actual data is unavailable. Some edge deployments use a secondary connection for backup connectivity and control. Connectivity.Edge computing overcomes typical network limitations, but even the most forgiving edge deployment will require some minimum level of connectivity. It’s critical to design an edge deployment that accommodates poor or erratic connectivity and consider what happens at the edge when connectivity is lost.
Rugged edge computers can be deployed to enable passenger information systems, vehicle monitoring and tracking systems, intelligent surveillance of transportation vehicles and stations, intelligent traffic management systems, and autonomous transportation vehicles. At the heart of all of these intelligent transportation systems are edge computing devices. 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.
In public transit applications, edge computing systems installed in buses, passenger rail systems and paratransit vehicles can aggregate and send only the data needed for to support in-vehicle processes and dispatcher insights. Electric vehicles need continuous monitoring and can use edge computing for management of data to support predictive maintenance. EV batteries must be monitored, as their longevity depends on the individual habits of drivers, the congestion of the areas they travel and how often they are charged. Edge computing supports data aggregation to report the actionable data for performance and maintenance. In a nutshell, edge computing is processing that takes place as close as possible to the process or thing being monitored by an IoT device.