![]() ![]() Some regions of the world have data protection laws that require data to be stored and processed in the area where it was created. In addition, edge computing makes distributed denial of service (DDOS) attacks more difficult. If you store and process all your data in one location, that gives attackers a big, attractive target, but edge computing makes it less likely that attackers will gain a huge trove of data. This can be significant, particularly in parts of the world where mobile data fees are high. Transmitting less data can also result in lower data transmission costs. Doing more processing at the edge reduces network bandwidth loads, freeing up capacity for the most important workloads. ![]() Today’s devices are generating so much data that it can be difficult for networks to keep up. This reduction in latency results in faster performance. If you process data near where it is generated, you don’t have to wait for it to go up to the cloud and back again. What Are the Benefits of Edge Computing?Įdge computing offers a number of benefits over centralized computing models, such as faster processing speed, reduced network loads, reduced costs, and more. This combination of edge computing and cloud computing is becoming increasingly common in a variety of different use cases and industries. Administrators and business managers can then access that cloud-based data through various applications. That server processes and stores data, as well as forwarding it to various Internet-connected servers that process payments, monitor company financials, and analyze customer orders and survey responses. Those tablets then transmit all that data via Wi-Fi to a centralized server in the restaurant. These edge devices collect data input by users, such as order information, payment details, and/or survey responses. Think about the tablet-style kiosks you might see at each table in a chain restaurant. It might be easier to understand this architecture by considering a particular use case. Other devices and users can then access the processed data via the cloud. In theory, a device could store the data at the edge indefinitely, but in most deployments, the device then sends a portion of the data up to the cloud for additional processing and analytics. Then the edge device does some processing and storage locally. Those sensors might be part of the device itself (as in the case of smartphones and autonomous vehicles) or they might be separate (as in the case of gaming systems and many IIoT deployments). However, in general, most edge computing deployments do have some typical characteristics in common.įirst, edge devices usually collect data from sensors. Military and defense vehicles and weaponsīecause there are so many different kinds of edge devices, there is no single edge architecture that covers all use cases.Industrial Internet of Things (IIoT) gateways.Here’s a non-exhaustive list of edge computing devices: The smartphone you have in your pocket does edge computing. For example, if you work in a remote office or back office (ROBO) environment with your own computing infrastructure, that’s an example of edge computing. You might not realize it, but you probably interact with devices leveraging edge computing every day. The newer trend toward edge computing is a further extension of that distributed model.Įdge computing enables computing beyond the data center and cloud perimeter, which allows it to support mobile and IoT devices, including cell phones. In recent years, the trend toward cloud computing has been a move to a more diffuse, multicloud computing model. If you look back at computer history, you can see a cycle between more centralized computing (like the early mainframes) to more distributed models (like networked PCs). The most important part of edge technology is that it’s a form of distributed computing. ![]() Edge is forward-looking today in the same way that the datacenter was a leader some dozen years ago. Key to the idea of edge, whether your edge deployment supports machine learning, artificial intelligence or data analytics, is that it extends resources far outside the once-dominant datacenter. If you dig deeper into edge, you’ll see that edge computing deployments – often supported by cloud computing providers – are part of a distributed infrastructure, which enables the compute power to be closer to the people who produce or consume that data. In a nutshell, edge computing is any computing that occurs on the edge of the network rather than in a centralized server. ![]()
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