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Edge Computing
In today's digital era, we produce an immense amount of data every second, and its volume is growing exponentially. Traditional cloud computing systems are struggling to keep up with the increasing demand for processing and storage capacity, leading to delays and bottlenecks.
This is where edge computing comes in – a distributed computing architecture that enables real-time data processing and analysis closer to the source of data.
It’s all about performance and reducing latency.
In a world where everything is almost instant, to understand the benefit of cloud computing, you need to understand latency. Network latency is the time it takes for a data package to travel from one point in a network to another, from one region to another. We are talking about milliseconds here, remember we are millisecond hunters.
The metric used here is the one you may have heard when playing video games online: your ping to the main server. It may be weird for a non-initiated person, but crossing the Atlantic Ocean is really slow for a bit too!
For the record, some people call the processing time: computation latency, but that’s confusing.
You may also have encountered that when working with a CDN as well. Let’s take your profile picture as an example. It is stored in the centralized cloud storage and spread to the edge. So, from New York, your friend in Japan is not really downloading your image from the central server but from replication of that image on edge.
But CDNs just serve data as they are, they don’t compute data. What if we could do more, what if we could execute code on the edge of the world?
Edge computing is a distributed computing paradigm that allows data processing and analysis to be performed at the edge of the network, i.e., closer to the source of data.
The key is that instead of sending data to the centralized cloud server for processing, edge computing utilizes local devices such as routers, switches, gateways, and IoT devices to perform computation and data analysis.
This approach drastically reduces network latency, improves reliability, and conserves network bandwidth.
Edge computing operates on the principle of distributing data processing and analysis tasks across multiple devices within the network. This approach processes and analyzes data in real time; only the necessary results are transmitted to the centralized cloud server. This reduces the amount of data that needs to be transmitted over the network, resulting in improved performance and reduced latency.
- Reduced Latency. Edge computing reduces the time it takes to transmit data from the source to the cloud server, resulting in lower latency.
- Improved Reliability. Edge computing improves the reliability of the network by reducing the risk of single points of failure.
- Improved Security. Edge computing provides enhanced security by processing sensitive data closer to the source and reducing the risk of data breaches during transmission.
- Cost-Effective. Edge computing reduces the cost of data processing and storage by utilizing local devices instead of relying on expensive cloud servers.
- Limited Storage Capacity. Local devices used for edge computing may have limited storage capacity, which may impact the ability to store and process large amounts of data.
- Limited Processing Power. Local devices may also have limited processing power, which may impact the ability to perform complex data analysis tasks.
- Network Connectivity. Edge computing relies on network connectivity to transmit data to the centralized cloud server, which may be unreliable in some environments.
- Data consistency. probably the most important to take into consideration when you start using edge computing. Depending on the architecture, the computation may happen in parallel in multiple locations in the world on data that do not have the same freshness. It’s definitely a trade-off that you need to consider.
Edge computing has a wide range of potential applications in various industries, including:
Industrial Automation. Edge computing can be used in industrial automation to monitor and control manufacturing processes in real-time. By utilizing local devices for data processing and analysis, edge computing can reduce latency and improve the efficiency of industrial processes.
Healthcare. Edge computing can be used in healthcare to monitor patient health in real-time. By utilizing local devices for data processing and analysis, edge computing can reduce the time it takes to diagnose and treat medical conditions, resulting in improved patient outcomes.
Smart Cities. Edge computing can be used in smart cities to monitor and control various city systems, such as traffic management, public safety, and energy management. By utilizing local devices for data processing and analysis, edge computing can improve the efficiency of city systems and reduce costs.
Autonomous Vehicles. Edge computing can be used in autonomous vehicles to process and analyze sensor data in real-time. By utilizing local devices for data processing and analysis, edge computing can reduce latency and improve the safety of autonomous vehicles.
Web and eCommerce. We mentioned that CDN and image processing are easy applications of edge computing and data analytics. Security is also a very interesting application when data is analyzed early on edge, we can prevent intrusion and threat.
