Edge Computing is a distributed computing model that brings computing power and storage closer to where data arises with low-latency and bandwidth-saving processing needs.
The origins of Edge Computing are CDNs – content delivery networks created in the late 90s to deliver web and video content from the edge servers closest to the user. These networks then evolved to host applications and application components at edge servers, creating the first edge computing services such as real-time data aggregation.
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How does edge computing work?
Edge computing works by capturing and processing the information as close to the desired data source or event as possible. It relies on sensors, computing devices, and machines to collect data and feed it to edge servers or the cloud. Depending on the task and the desired outcome, this data can feed into machine learning and analytics systems, provide automation, or provide visibility into the current state of equipment, system, or product.
However, edge and cloud data centers are not the only way to process data. In some cases, IoT devices can process data on their own using built-in hardware or send data to a connected smart device.
Architectural model in edge computing
Edge Computing is closely tied to Cloud Computing and the Internet of Things (IoT). It is the middle layer in charge of providing small data processing and storage, needs a fast response from the IoT tier, and saves bandwidth for big data processing that is forwarded “to the cloud”.
In the Edge Computing ecosystem, data is collected from sensors, meters, etc. at the IoT device layer. The data is then not immediately uploaded to the cloud server, but across the edge – local storage and compute centers located near the data source are IoT devices.
A common question is where is the boundary? In many cases, the edge can be a local workstation that is connected to the Internet but is still at the edge of a LAN, but sometimes the edge is a local data center that handles processing for an area. geography. The edge does not have to be on the LAN or connected over the Internet, as long as it is closest to the data source and is available to provide real-time low-latency processing.
Finally, the big and complex data processing will be transferred to the cloud server for execution. Despite its high latency and line dependency, cloud computing is suitable for computations that require large amounts of processing power.
Essential components in the Edge computing
Cloud Server: This can be a public or private cloud, or it can be a data center. These clouds host and run applications used to coordinate and manage the various edge nodes. Workloads at the edge, at the end, and in the cloud interact with each other in the process.
Edge device: A device with built-in computing capabilities such as an ATM, digital camera, or car. Edge devices often have limited computing power, handling only transient requests that require low latency. Although there are also edge devices with strong configurations, they are the exception and not representative of the majority.
Edge node: An edge node is a generic term for any edge device, edge server, or gateway on which edge computing can be performed.
Edge server: An edge server is a general-purpose computer located in a remotely operated facility such as a factory, retail store, hotel, distribution center, or bank branch. . Edge servers are typically built using industrial PCs with CPUs with 8 – 16 cores or more, 16GB of memory, and hundreds of GB of local storage. An edge server is typically used to run enterprise application workloads and shared services.
Edge gateway: an edge gateway is typically an edge server that, in addition to handling enterprise application workloads, performs network functions such as protocol compilation, firewall protection, or connectivity wireless.
Reasons to use Edge Computing
Cloud Computing speed limit
Cloud servers can handle very large tasks, but because they have often located very far away, latency over the Internet can be in the hundreds of milliseconds. In contrast, edge devices can be much weaker, but with relatively small amounts of data from IoT devices, they can provide microsecond response rates from short distances. Think of self-driving cars, where every millisecond of delay can be at the expense of human safety.
Secure data transmission
Edge servers near or even in the same local area network can always guarantee the speed and stability of data transmission. An obvious example is that every time a fiber optic cable fails, the impact on the domestic Internet connection is negligible while the international bandwidth always drops to an annoying level.
High security
The outstanding issue of cloud computing, especially public cloud, is data privacy and security. It can be said that edge computing is somewhat similar to a hybrid cloud, where local processing – at the edge device always gives better peace of mind about security. Only unimportant data is pushed to the public server.
Reduce the bandwidth of Cloud Computing
Bandwidth to remote cloud servers is a limited resource that everyone wants to save. The workloads handled at the edge not only reduce the amount of data that has to be transmitted over the Internet to the Cloud Server but also reduce the investment cost of cloud processing capacity.
Edge computing drawbacks
Lack of on-site facilities
Edge Computing performs processing at the edge – where the closest servers are located. But sometimes there are underdeveloped areas that make it difficult to deploy edge computing infrastructure, making the advantages of this model cannot fully exploit.
Difficulty monitoring and dealing with security issues
This is a common problem of distributed computing, when there are too many edge nodes with unequal security capabilities, monitoring and control are more difficult than in much-centralized computing.
Data loss
At the edge, data that is judged to be redundant is often discarded during processing so that cloud upload workloads are optimized. However, it is also a double-edged sword because in case edge devices misjudge the importance of data, the problem of traceability and recovery becomes very complicated.
Large investment costs
Although edge computing offers many benefits and the ability to save bandwidth and cloud processing power, the initial cost of an edge network is not small. Large, geographically dispersed devices can deter even large enterprises.
Final thought
Hopefully, through this article, you have gained useful information about Edge Computing and its features. Top News wishes you the best of luck on your learning journey!