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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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