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THE FOG ROLLS IN: NETWORK ARCHITECTURES FOR IoT AND EDGE COMPUTING
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For most enterprise workloads, data and applications are centralized in a company data center or cloud, and accessed by multiple client devices. The“ heavy lifting” of the storage and compute functions occurs in the data center or cloud, and only the necessary data( e. g., query and response) traverses the network to the requesting device. This centralized architecture helps the enterprise to efficiently manage corporate applications, control access, and optimize server and network utilization.
Consider, however, use cases in which massive amounts of data are collected for processing from myriad geographically-dense endpoints, as in certain Internet of Things( IoT) applications. For example: real-time temperature-sensing across climatecontrolled storage facilities. Or traffic monitoring. Or collecting shopper data via mobile location services.
In such cases, storing and analyzing all the data in a centralized, remote data center may be less than optimal. First, sufficient network capacity would be required to handle the steady stream of data. Second, for latency-sensitive applications, data transmission to a remote cloud would introduce unacceptable delay, especially when the data analysis is designed to trigger a local, real-time response( e. g., automatically deploying fire suppression equipment when sensors detect smoke). Furthermore, the network journey introduces risk of dropped or corrupted packets, potentially compromising the data.
For these cases, network and compute resources may need to be configured in a more suitable architecture, in which compute resources are split between local sites( where data is temporarily stored as it undergoes preliminary filtering or analytics) and the cloud( where it is further analyzed and stored). Such a configuration would limit network-induced delays, control network costs, and minimize risks of data loss or corruption.
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