edge does this by bringing data processing and other computing needs as close to the sensor or other device as possible, which reduces latency, among other potential benefits. “these compute environments are what support edge computing because they are small compute units distributed away from the core (like cloud) and have the capacity to perform a variety of tasks,” mishra says. among other reasons: much of this data may be ephemeral, and a round trip to the cloud may not actually create any value.
read the blog: how to implement edge infrastructure in a maintainable and scalable way. ] “video cameras can be the ultimate sensor and can be added to edge environments as a parallel asset without requiring disruptive instrumentation changes,” mishra says. “although the concept of local processing at the edge is powerful, there is a strong need to create a lifecycle effect between edge and cloud for scaling use cases,” mishra says.
in addition, companies can save money by having the processing done locally, reducing the amount of data that needs to be sent to a centralized or cloud-based location. or it can send data back to the edge device in the case of real-time application needs. the physical architecture of the edge can be complicated, but the basic idea is that client devices connect to a nearby edge module for more responsive processing and smoother operations.
that’s a lot of work and would require a considerable amount of in-house expertise on the it side, but it could still be an attractive option for a large organization that wants a fully customized edge deployment. increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling more efficient real-time applications that are critical to companies. furthermore, differing device requirements for processing power, electricity and network connectivity can have an impact on the reliability of an edge device.
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