Companies like Amazon, Microsoft, and Google have shown us that we can trust them with our data on Digital Solution. Now is the time to reward that trust by giving them complete control over our computers, toasters, and cars. Allow me to introduce you to edge computing.
Edge is a buzzword. Like “IoT” and “cloud” before, Edge means everything and nothing. But I’ve been watching some industry insiders on YouTube, listening to some podcasts, and occasionally reading articles on the subject. And I think I’ve come up with a helpful definition and possible applications for this modern technology.
In this article, you will understand what Edge Computing is, how it works. The influence of the cloud, edge use cases, tradeoffs, and deployment considerations.
What is Edge Computing?
Edge computing is a distributed information technology (IT) architecture in which customer data is processed. At the Edge of the network, as close as possible to the source of origin.
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Data is the lifeblood of modern business, providing valuable business insights. And supporting real-time control of critical business processes and operations.
Companies today are awash in an ocean of data. And vast 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 how companies handle computing. The traditional edge computes paradigm based on centralized edge data centers and the everyday Internet is ill-suited to moving ever-growing rivers of real-world data. Bandwidth limitations, latency issues, and unpredictable network outages can undermine such efforts. Companies are responding to these data challenges by using cutting-edge computing architecture.
In simpler terms, edge computing moves a portion of computing. And storage resources out of the central data center and closer to the data source itself. Instead of transmitting raw data to a significant data center for processing and analysis. That work is done where the data is generated, whether it’s a retail store. A manufacturing plant, a sprawling utility company, or a growing utility—a smart city.
Only the output of that edge computing—such as real-time business insights, equipment maintenance predictions, or other actionable responses—is sent back to the leading data center for review and other human interaction. Thus, edge computing is reshaping IT and enterprise computing.
How does it work?
Edge computing is all about location. Traditional business computing produces data at a client endpoint, such as a user’s computer. That data moves across a WAN like the Internet, across the corporate LAN, where a business application stores and works with the data. The results of that work are then transmitted to the client’s terminal. This is still a 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 produced by those devices and used by businesses is growing too fast for traditional data center infrastructures to accommodate. By 2025, 75% of the data generated by companies will be created outside of centralized data centers. The prospect of moving so much data in time- or outage-sensitive situations puts incredible pressure on the global Internet, which is often subject to congestion and outages.
Therefore, IT architects have shifted the focus from the core data center to the logical Edge of the infrastructure, taking compute and storage resources from the data center and moving them to the point where the data is generated. The principle is simple: if you can’t get the data closer to the data center, get the data center closer to the data.
The concept of edge computing is not new. It has roots in small computing ideas that date back decades, such as remote and branch offices. It was more reliable and efficient to place computing resources in the desired location rather than relying on a single site. Central location.
Edge computing places storage and servers where the data is, often requiring little more than a partial rack of equipment to operate on the remote LAN to collect and process data locally. In many cases, computer equipment is deployed in shielded or hardened enclosures to protect the equipment from extreme temperatures, humidity, and other environmental conditions. Processing often involves normalizing and analyzing the data stream for business intelligence, with only the analysis results sent back to the primary data center.
The idea of business intelligence can vary dramatically. Some examples include retail environments where video surveillance of the showroom floor could be combined with sales data processing to determine the most desirable product configuration or consumer demand.
Other examples involve predictive analytics guiding equipment maintenance and repair before defects or failures occur. Other models more often align with utilities, such as water treatment or electricity generation, to ensure equipment is working correctly and maintain product quality.
Edge computing is closely associated with the concepts of cloud computing and fog computing. Although there is some overlap between these concepts, they are not the same and should not be used interchangeably. It is helpful to compare the images 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 implementation of computing and storage resources for the produced data. The difference is a matter of where those resources are located.
Edge computing is the deployment of computing and storage resources at the location where data is produced. Ideally, these places 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 can be installed on top of a wind turbine to collect and process data produced by sensors inside 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 countless data from rail and track traffic sensors.
The results of such processing can be sent back to another data center for human review and archiving and merged with other data output for further analysis.
Cloud computing is a vast and highly scalable deployment of computing and storage resources in one of several distributed global locations (regions). Cloud providers also come with various 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 nearest regional cloud facility may still be hundreds of miles from where the data is collected, and connections depend. Of the same temperamental Internet connectivity that supports traditional data centers.
In practice, cloud computing is an alternative to conventional data centers, sometimes a compliment. The cloud can bring centralized computing closer to a data source but not to the network’s Edge.
But the computing and storage deployment choice isn’t limited to the cloud or the Edge. A cloud data center may be too far away. Still, the edge deployment may be too limited in resources or physically dispersed or distributed for strict edge computing to be practical. In this case, the notion of fog computing can help. Fog computing typically takes a step back and places compute and storage resources “within” the data, but not necessarily “in” the data.
Fog computing environments can produce bewildering amounts of sensors or IoT data generated in expansive physical areas too large to define an edge. Examples include smart buildings, smart cities, and even intelligent power grids. Consider a smart city where data can be used to track, analyze, and optimize the public transportation system, municipal utilities, and city services and guide long-term urban planning.
A single edge implementation is not enough to handle such a load, so fog computing can operate several fog node implementations within the scope of the environment to collect, process, and analyze data. It’s important to remember that fog computing and edge computing share a nearly identical definition and architecture, and the terms are sometimes used interchangeably, even among techies.
Benefits of Edge computing
Edge computing addresses vital infrastructure challenges such as bandwidth limitations, excess latency, and network congestion. Still, edge computing has several additional potential benefits that may make the approach attractive in other situations.
Edge computing is practical when connectivity is unreliable, or bandwidth is restricted due to environmental characteristics of the site. Examples include oil rigs, ships at sea, remote farms, or other remote locations such as a rainforest or desert. Edge Computing does the computing work on-site, sometimes on the edge device itself, such as water quality sensors in water purifiers in remote villages. It can save data for transmission to a central point only when connectivity is available. By processing data locally, the amount of data being sent can be significantly reduced, requiring much less bandwidth or connectivity time than would otherwise be required.
· Data sovereignty
Moving large amounts of data is not just a technical problem. Data travel across national and regional borders can raise additional concerns for data security, privacy, and other legal issues.
Edge computing can keep data close to its source and within the confines of applicable data sovereignty laws, such as the European Union’s GDPR, which define how data should be stored, processed, and exposed. This can allow raw data to be processed locally, obscuring or securing sensitive data before sending anything to the cloud or central data center, which may be in other jurisdictions.
· Edge security
Finally, edge computing offers an additional opportunity to implement and ensure data security. Although cloud providers have IoT services and specialize in complex analytics, companies remain concerned about data security once it leaves the Edge and returns to the cloud or data center. By implementing edge computing, any data that traverses the network back to the cloud or data center can be protected through encryption, and the edge implementation itself can be hardened against hackers and other malicious activity, even when security in IoT devices is still limited.
Challenges of Edge computing
While edge computing has the potential to deliver compelling. Benefits across a multitude of use cases, the technology is far from foolproof. Beyond the traditional issues of network limitations, several vital considerations can affect the adoption of edge computing:
· Limited capacity
The variety and scale of resources and services are part of the appeal that cloud computing. Brings to edge or fog computing. Deploying an infrastructure at the Edge can be effective. But the scope and purpose of the edge deployment must be clearly defined. Even a large deployment of edge computing serves a specific purpose. At a predetermined scale using limited resources and few services.
Edge Computing overcomes typical network limitations, but even the most forgiving Edge implementation will require a minimum level of connectivity. It is critical to design an edge deployment to accommodate poor or erratic connectivity. And to consider what happens at the Edge when connectivity is lost. Autonomy, AI, and graceful failover planning for connectivity issues are essential to the success of edge computing.
IoT devices are notoriously insecure, so it is vital to design a state-of-the-art computing implementation. That emphasizes proper device management. such as enforcing policy-based configuration and security across computing and data resources. Storage including factors such as patches and software updates. with particular attention to encryption on data at rest and in flight. IoT services from leading cloud providers include secure communications, which is not automatic when creating an edge site from scratch.
· Data life cycles
The perennial problem with today’s data glut is that much of that data is unnecessary. Consider a medical monitoring device: It’s only critical problem data. And there’s little point in keeping days of average patient data. Most of the data involved in real-time analytics is short-term data that is not retained long-term. A business must decide what data to keep and what to discard once the analyzes are done. And the data included must be protected by industry and regulatory policies.
Edge computing, IoT, and 5G possibilities
Edge computing continues to evolve, using new technologies and practices to improve its capabilities and performance. The most notable trend is edge availability, with edge services expected to be available worldwide by 2028. Where edge computing is often situation-specific, the technology is expected to become more ubiquitous and change. How the Internet is used, bringing more abstraction and possible use cases for edge technology.
This can be seen in the proliferation of computing, storage, and networking products designed specifically for edge computing. More multi-vendor partnerships will enable better interoperability and flexibility of products at the Edge.
An example includes a collaboration between AWS and Verizon to provide better connectivity to the Edge.
Wireless communication technologies, such as 5G and Wi-Fi 6, will also impact edge deployments and utilization. In the coming years, enabling virtualization and automation capabilities that have yet to be explored. Such as better vehicle autonomy and workload migrations to the Edge, while making wireless networks more flexible and cost-effective.
Edge computing gained notoriety with the rise of IoT and the sudden glut of data these devices produce.
But with IoT technologies still at a relatively early stage. The evolution of IoT devices will also impact the future development of edge computing.
An example of such future alternatives is the development of micro-modular data centers (MMDCs).
The MMDC is a data center in a box, putting an entire data center inside a small mobile system. That can be deployed closer to the data, such as in a city or region. To bring computing closer to users’ data.
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