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.
But this virtual flood of data is also changing the way businesses handle computing. The traditional computing paradigm built on a centralized data centre and everyday internet isn't well suited to moving endlessly growing rivers of real-world data. Bandwidth limitations, latency issues and unpredictable network disruptions can all conspire to impair such efforts. Businesses are responding to these data challenges through the use of edge computing architecture.
In simplest terms, edge computing moves some portion of the storage and compute resources out of the central data centre and closer to the source of the data itself. Rather than transmitting raw data to a central data centre for processing and analysis, that work is instead performed where the data is 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 centre for review and other human interactions.
Thus, edge computing is reshaping IT and business computing. Take a comprehensive look at what edge computing is, how it works, the influence of the cloud, edge use cases, tradeoffs and implementation considerations.
How does edge computing work?
Edge computing is all a matter of location. 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. Results of that work are then conveyed back to the client endpoint. This remains a proven and time-tested approach to client-server computing for most typical business applications.
But the number of devices connected to the internet, and the volume of data being produced by those devices and used by businesses, is growing far too quickly for traditional data centre infrastructures to accommodate. Gartner predicted that by 2025, 75% of enterprise-generated data will be created outside of centralized data centres. The prospect of moving so much data in situations that can often be time- or disruption-sensitive puts incredible strain on the global internet, which itself is often subject to congestion and disruption.
So IT architects have shifted focus from the central data centre to the logical edge of the infrastructure -- taking storage and computing resources from the data centre and moving those resources to the point where the data is generated. The principle is straightforward: If you can't get the data closer to the data centre, get the data centre closer to the data. The concept of edge computing isn't new, and it is rooted in decades-old ideas of remote computing -- such as remote offices and branch offices -- where it was more reliable and efficient to place computing resources at the desired location rather than rely on a single central location.
Edge computing puts storage and servers where the data is, often requiring little more than a partial rack of gear to operate on the remote LAN to collect and process the data locally. In many cases, the computing gear is deployed in shielded or hardened enclosures to protect the gear from extremes of temperature, moisture and other environmental conditions. Processing often involves normalizing and analyzing the data stream to look for business intelligence, and only the results of the analysis are sent back to the principal data centre.
The idea of business intelligence can vary dramatically. Some examples include retail environments where video surveillance of the showroom floor might be combined with actual sales data to determine the most desirable product configuration or consumer demand. 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.
Edge vs. Cloud vs. Fog computing:
Edge computing is closely associated with the concepts of cloud computing and fog computing. Although there is some overlap between these concepts, they aren't the same thing, and generally shouldn't be used interchangeably. It's helpful to compare the concepts and understand their differences.
One of the easiest ways to understand the differences between edge, cloud and fog computing is to highlight their common theme: All three concepts relate to distributed computing and focus on the physical deployment of computing and storage resources concerning the data that is being produced. The difference is a matter of where those resources are located.
Compare edge cloud, cloud computing and edge computing to determine which model is best for you.
Edge. Edge computing is the deployment of computing and storage resources at the location where data is produced. This ideally puts compute and storage at the same point as the data source at the network edge. For example, a small enclosure with several servers and some storage might be installed atop a wind turbine to collect and process data produced by sensors within the turbine itself. As another example, a railway station might place a modest amount of computing and storage within the station to collect and process myriad track and rail traffic sensor data. The results of any such processing can then be sent back to another data centre for human review, archiving and to be merged with other data results for broader analytics.
Cloud. Cloud computing is a huge, highly scalable deployment of computing and storage resources at one of several distributed global locations (regions). Cloud providers also incorporate an assortment of pre-packaged services for IoT operations, making the cloud a preferred centralized platform for IoT deployments. But even though cloud computing offers far more than enough resources and services to tackle complex analytics, the closest regional cloud facility can still be hundreds of miles from the point where data is collected, and connections rely on the same temperamental internet connectivity that supports traditional data centres. In practice, cloud computing is an alternative -- or sometimes a complement -- to traditional data centres. The cloud can get centralized computing much closer to a data source, but not at the network edge.
Fog. But the choice of computing and storage deployment isn't limited to the cloud or the edge. A cloud data centre might be too far away, but the edge deployment might simply be too resource-limited, or physically scattered or distributed, to make strict edge computing practical. In this case, the notion of fog computing can help. Fog computing typically takes a step back and puts compute and storage resources "within" the data, but not necessarily "at" the data.
Fog computing environments can produce bewildering amounts of sensor or IoT data generated across expansive physical areas that are just too large to define an edge. Examples include smart buildings, smart cities or even smart utility grids. Consider a smart city where data can be used to track, analyze and optimize the public transit system, municipal utilities, city services and guide long-term urban planning. A single edge deployment simply isn't enough to handle such a load, so fog computing can operate a series of fog node deployments within the scope of the environment to collect, process and analyze data.
Note: It's important to repeat that fog computing and edge computing share an almost identical definition and architecture, and the terms are sometimes used interchangeably even among technology experts.
Why does edge computing matter?
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. In general, distributed computing models are hardly new, and the concepts of remote offices, branch offices, data centre colocation and cloud computing have a long and proven track record.
But decentralization can be challenging, demanding high levels of monitoring and control that are easily overlooked when moving away from a traditional centralized computing model. Edge computing has become relevant because it offers an effective solution to emerging network problems associated with moving enormous volumes of data that today's organizations produce and consume. It's not just a problem of amount. It's also a matter of time; applications depend on processing and responses that are increasingly time-sensitive.
Consider the rise of self-driving cars. They will depend on intelligent traffic control signals. Cars and traffic controls will need to produce, analyze and exchange data in real-time. Multiply this requirement by huge numbers of autonomous vehicles, and the scope of the potential problems becomes clearer. This demands a fast and responsive network. Edge -- and fog-- computing addresses three principal network limitations: bandwidth, latency and congestion or reliability.
Bandwidth. Bandwidth is the amount of data that a network can carry over time, usually expressed in bits per second. All networks have 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 (higher) finite limits and it doesn't solve other problems.
Latency. Latency is the time needed to send data between two points on a network. Although communication ideally takes place at the speed of light, large physical distances coupled with network congestion or outages can delay data movement across the network. This delays any analytics and decision-making processes and reduces the ability of a system to respond in real-time. It even cost lives in the autonomous vehicle example.
Congestion. The internet is a global "network of networks." Although it has evolved to offer good general-purpose data exchanges for most everyday computing tasks -- such as file exchanges or basic streaming -- the volume of data involved with tens of billions of devices can overwhelm the internet, causing high levels of congestion and forcing time-consuming data retransmissions. In other cases, network outages can exacerbate congestion and even sever communication to some internet users entirely - making the internet of things useless during outages.
By deploying servers and storage where the data is generated, edge computing can operate many devices over a much smaller and more efficient LAN where ample bandwidth is used exclusively by local data-generating devices, making latency and congestion virtually nonexistent. Local storage collects and protects the raw data, while local servers can perform essential edge analytics -- or at least pre-process and reduce the data -- to make decisions in real-time before sending results, or just essential data, to the cloud or central data centre.
Edge computing use cases and examples:
In principle, edge computing techniques are used to collect, filter, process and analyze data "in-place" at or near the network edge. It's a powerful means of using data that can't be first moved to a centralized location -- usually because the sheer volume of data makes such moves cost-prohibitive, technologically impractical or might otherwise violate compliance obligations, such as data sovereignty. This definition has spawned myriad real-world examples and uses cases:
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. The manufacturer can now make faster and more accurate business decisions regarding the factory facility and manufacturing operations.
Farming. Consider a business that grows crops indoors without sunlight, soil or pesticides. The process reduces grow times by more than 60%. Using sensors enables the business to track water use, nutrient density and determine optimal harvest. Data is collected and analyzed to find the effects of environmental factors and continually improve the crop growing algorithms and ensure that crops are harvested in peak condition.
Network optimization. Edge computing can help optimize network performance by measuring performance for users across the internet and then employing analytics to determine the most reliable, low-latency network path for each user's traffic. In effect, edge computing is used to "steer" traffic across the network for optimal time-sensitive traffic performance.
Workplace safety. Edge computing can combine and analyze data from on-site cameras, employee safety devices and various other sensors to help businesses oversee workplace conditions or ensure that employees follow established safety protocols -- especially when the workplace is remote or unusually dangerous, such as construction sites or oil rigs.
Improved healthcare. The healthcare industry has dramatically expanded the amount of patient data collected from devices, sensors and other medical equipment. That enormous data volume requires edge computing to apply automation and machine learning to access the data, ignore "normal" data and identify problem data so that clinicians can take immediate action to help patients avoid health incidents in real-time.
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.
Retail. Retail businesses can also produce enormous data volumes from surveillance, stock tracking, sales data and other real-time business details. Edge computing can help analyze this diverse data and identify business opportunities, such as an effective endcap or campaign, predict sales and optimize vendor ordering, and so on. Since retail businesses can vary dramatically in local environments, edge computing can be an effective solution for local processing at each store.
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The information is provided by Tecquisition for general informational and educational purposes only and is not a substitute for professional legal advice. If you have any feedback, comments, requests for technical support or other inquiries, please mail us at tecqusition@gmail.com.
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