Introduction
Edge computing is the process of moving computation and storage closer to the source of data. The goal is to increase the amount of processing that can take place in real time, and reduce latency by reducing distance between users and IoT devices.
What is Edge Computing?
Edge computing is a distributed computing model, the opposite of cloud computing. It allows for faster processing and better latency with lower cost. Edge computing uses a combination of cloud, fog and edge computing to get data closer to where it needs to be processed.
The typical use cases for edge computing include:
- Real-time analytics (e.g., monitoring sensors)
- Industrial automation (e.g., autonomous vehicles)
- Smart cities/roads/buildings
Why is it important?
Edge computing is important because it can help reduce latency, improve data security, and optimize bandwidth.
Latency reduction: Latency is the time it takes for a message to travel from one place to another. For example, if you were sending a text message from New York City to Los Angeles using your cell phone’s network connection (which has limited bandwidth), your latency would be the amount of time between when you hit send and when your friend received it in LA–a few seconds at most. The problem with this scenario is that there’s no guarantee how long it will take for them to get their reply back-in fact, it could be hours or even days!
It’s also worth noting that not all types of applications require low-latency connections; some can function just fine with higher latencies because other factors like bandwidth are more important than speed at certain points in their operation. But if your company relies heavily on real-time communication between its employees (such as instant messaging) then reducing latency may become critical over time.*
Conclusion
Edge Computing is the next step in the evolution of computing, and it’s an important one. With edge computing, companies can process data at the source rather than sending it to a central location first where it can be analyzed before being sent back. This allows businesses to save time and money by not having to invest in expensive servers or storage solutions when all they need is something simple like data analysis or machine learning capabilities.
