Why are edge computing solutions needed?

Everyone is familiar with cloud computing, which has many features: it has huge computing power and massive storage capacity. Through different software tools, various applications can be built. Many of the apps we use essentially rely on various cloud computing technologies, such as video live-streaming platforms and e-commerce platforms. Edge computing solutions originated from cloud computing. It is close to the device side and has rapid response capabilities, but it cannot handle scenarios with large amounts of computing and storage. The relationship between the two can be explained by the nervous system of our bodies.
Cloud computing can handle a large amount of information and store short-term and long-term data, which is very similar to our brains. The brain is the largest and most complex structure in the central nervous system and also the highest part. It is the organ that regulates the functions of the body and the material basis for advanced neural activities such as consciousness, spirit, language, learning, memory, and intelligence. The gray matter layer of the human brain, rich in hundreds of millions of nerve cells, forms the basis of intelligence. It is not only the brain that has a gray matter layer. The human spinal cord also contains a gray matter layer and has a simple central nervous system that is responsible for reflex actions from the limbs and trunk, as well as transmitting neural information between the brain and the periphery. We all learned about the knee-jerk response in junior high school biology, which is evidence of spinal cord response-ability. Edge computing solutions to cloud computing are like the spinal cord to the brain. Edge computing has a fast response speed and does not require cloud computing support, but its low intelligence level is relatively low and it cannot adapt to the processing of complex information.
While the spinal cord issues instructions, it also transmits signals of pain to the brain, allowing people to feel the pain. Everyone, take a look at the entire process. This risk-avoidance action occurs before consciousness is generated and is very fast, avoiding harm to your body. After hundreds of millions of years of evolution, the body structure of human beings has now become very perfect. Since the structure is designed in this way, there must be a reason for it. Let’s take a look at this set of data: “For humans, in the nerve cells that connect the spinal cord to the muscles, the signal transmission speed of large-diameter neurons covered with a myelin sheath is 70-120 meters per second, while the signal transmission speed of brain neurons is 0.5-2 meters per second.” The gap between them is too large. If we let the brain process decisions such as avoiding burns and generating actions, then the most likely situation for our hands is:
Take the Boeing 787 as an example. Each round trip of its flight can generate terabytes of data. The United States collects 3.6 million flight records every month. Monitor 25,000 engines in all aircraft, with each engine generating 588GB of data per day. At this level, if all the data were uploaded to the servers of cloud computing, it would impose strict requirements on both computing power and bandwidth. Wind turbines are equipped with various sensors for measuring wind speed, pitch, oil temperature, etc. They are measured every few milliseconds to detect the wear degree of blades, gearboxes, frequency converters, etc. A wind farm with 500 wind turbines generates 2PB of data in a year.
Such petabyte-level data, if uploaded to the cloud computing center in real-time and used to make decisions, poses strict requirements from both computing power and bandwidth perspectives, not to mention the immediate response issues caused by latency. In the face of such scenarios, edge computing solutions demonstrate their advantages. As it is deployed near the device side, it can provide immediate feedback on decisions through algorithms and filter the vast majority of data, effectively reducing the load on the cloud and making massive connections and massive data processing possible. Therefore, edge computing will serve as a complement to cloud computing and coexist in the architecture of the Internet of Things in the future.
Having said so much, let’s summarize the advantages of edge computing solutions:
Low latency: Computing power is deployed near the device side, and device requests respond in real-time.
Low-bandwidth operation: The ability to move work closer to users or data collection terminals can reduce the impact of site bandwidth limitations. Especially when edge node services reduce the number of requests for sending a large amount of data processing to the central hub.
Privacy protection: Data is collected, analyzed, and processed locally, effectively reducing the chance of data exposure to public networks and protecting data privacy.