Wondering what is edge computing? You can understand Edge computing as a distributed computing model that strategically places processing capabilities near the data sources, such as Internet of Things (IoT) devices or localized edge servers. This nearness to the source of the data can provide considerable advantages, such as more immediate quicker response times, insights, and improved bandwidth availability.
The increasing proliferation and computing power of IoT devices have led to an extraordinary surge in data volume. This surge is only expected to rise with the adoption of 5G networks, which further amplifies the number of interconnected mobile devices.
Earlier, the trend of cloud and AI was their potential to automate and expedite innovation by deriving actionable insights from data. However, the enormous scale and complexity of data generated by connected devices have surpassed network and infrastructure abilities.
Streaming all the device-generated data to a centralized data center or the cloud can lead to bandwidth congestion and latency problems. In contrast, edge computing offers a more efficient approach by processing and analyzing data closer to its generation point. Because the data doesn’t have to be transported across a network to a cloud or data center for processing, latency is significantly decreased. Hence, edge computing, particularly mobile edge computing on 5G networks, enables quicker and more thorough data analysis. This results in deeper insights, faster response times, and improved customer experiences.
How Does Edge Computing Function?
It’s essentially about location. In traditional enterprise computing all the data being reviewed are in the user’s end. This data is then transported across a WAN like the internet, through the corporate LAN, where it’s stored and processed by an enterprise application. The results of this processing are then relayed back to the client endpoint. While this remains a tried-and-true approach to client-server computing for most typical business applications, the scenario is rapidly changing.
Existing data center infrastructures are being more overwhelmed by the proliferation of internet-connected gadgets and the massive amounts of data they generate and consume. According to Gartner, by 2025, non-DC data will account for 75% of all enterprise data. The idea of sending such a large amount of data places a significant pressure on the global internet, which is prone to congestion and interruption, especially in time-sensitive or disruption-prone scenarios.
Thus, IT architects are moving away from central data centers towards the logical edge of the infrastructure, shifting data storage and processing power, from the server farm to the source of the da zdsadf it’s impractical to move the data to a central place, then the data center should go to where the data is. The idea of “edge computing” isn’t brand new; it’s based on the time-tested principles of “remote computing,” which have long maintained that “decentralizing” edge computing devices is preferable to “centralizing” them.
Regarding edge computing adoption, though only 27% of survey respondents have already implemented edge computing technologies, 54% are intrigued by the idea. Edge computing positions storage and servers at the data source, typically requiring little more than a portion of a server rack operating on the remote LAN to collect and process data locally. Frequently, the computing equipment is housed in shielded or hardened enclosures to safeguard against extreme temperature, moisture, and other environmental conditions. The processing often involves normalizing and analyzing the data stream for business intelligence, with only the analysis results sent back to the principal data center.
What is The Difference Between Edge Computing and Fog Computing
Edge computing is closely related to, yet distinct from, cloud computing and fog computing. Despite their frequent synonymy, each of these phrases actually refers to a distinct notion in the field of distributed computing, with the primary distinction lying in the placement of the computing and storage resources in relation to the data being processed.
Let’s explore these differences in a more detailed manner.
- Edge computing refers to the placement of computing and storage resources right at the location where the data is generated. This brings edge computing devices and storage as close as possible to the data source, at the network’s edge. For instance, atop a wind turbine, a small enclosure equipped with a few servers and some storage might be installed to collect and process data from sensors within the turbine itself. Similarly, a railway station might house a modest compute and storage system to collect and process a plethora of track and rail traffic sensor data. The results from such processing are then sent to a separate data center for human review, archiving, and merging with other data for broader analysis.
- On the other hand, cloud computing involves a massive, scalable deployment of compute and storage resources distributed globally. Cloud providers also offer a variety of pre-packaged services for IoT operations, making the cloud a popular centralized platform for IoT deployments. Despite offering more than sufficient resources and services to handle complex analytics, the nearest regional cloud facility can still be hundreds of miles away from the data collection point, with connections relying on the same unpredictable internet connectivity that supports traditional data centers.
Yet the edge computing examples of compute and storage deployment isn’t confined to the cloud or the edge. There may be situations where a cloud data center is too remote, but the edge deployment is too resource-limited, or physically dispersed or distributed, to make strict edge computing feasible. Here, fog computing comes into play. Fog computing positions compute and storage resources “within” the data, but not necessarily “at” the data.
Fog computing is typically employed in environments that generate overwhelming amounts of sensor or edge computing iot IoT data across vast physical areas, which are too expansive to define an edge. Examples include smart buildings, smart cities, or even smart utility grids. Take a smart city, for instance, where data is used to track, analyze, and optimize public transit systems, municipal utilities, city services, and guide long-term urban planning.
A single edge deployment isn’t sufficient to handle such a load. So, fog computing operates a series of fog node deployments within the scope of the environment to collect, process, and analyze data.
Challenges of edge computing
Edge computing, though promising in its potential to yield significant benefits across a range of applications, isn’t without its challenges. In addition to traditional network limitations, the adoption of edge computing comes with several unique considerations:
One of the allures of cloud computing that also extends to edge and fog computing is the scale and diversity of the resources and services it offers. While deploying an infrastructure at the edge can prove effective, it’s crucial to understand that even a comprehensive edge computing deployment serves a specific purpose at a predetermined scale, utilizing limited resources and a small number of services. The scope and purpose of the edge deployment must be explicitly defined for effective utilization.
Edge computing does mitigate many typical network limitations, but even the most versatile edge deployment requires a minimum level of connectivity. When designing an edge deployment, it’s important to accommodate poor or unpredictable connectivity and consider the effects of losing connectivity at the edge. Strategies such as autonomy, artificial intelligence, and planning for graceful failure in the face of connectivity issues are crucial to successful edge computing.
IoT devices are widely known for their security vulnerabilities. Therefore, it’s essential to design an edge computing deployment that emphasizes robust device management. This may include policy-driven configuration enforcement and security for computing and storage resources, with a focus on software patching, updates, and encryption for data at rest and in transit. Major cloud providers include secure communications in their IoT services, but such security measures aren’t automatic when building an edge site from scratch.
Today, the sheer volume of data generated poses a significant challenge, particularly since much of it may be unnecessary. For example, a medical monitoring device only needs to flag problematic data as critical; maintaining days of normal patient data offers little value. Most data involved in real-time analytics is short-term and isn’t stored over the long term. Thus, businesses must decide which data to retain and which to discard once the analyses are performed.
Additionally, any data that is kept must be protected in compliance with both business regulations and broader regulatory frameworks.
In conclusion, edge computing has emerged as a transformative technology that’s shifting the landscape of data processing. By bringing computing power and storage closer to the data sources, it offers substantial advantages, such as faster insights, improved response times, and better bandwidth availability, particularly in the era of IoT and 5G networks.