Online learning for “thing-adaptive” Fog Computing in IoT
Published: Oct 1, 2017
Abstract
The present paper deals with online convex optimization involving time-varying loss functions and time-varying constraints. The constraints are revealed after making decisions, and allow instantaneous violations yet they must be satisfied in the long term. This setting fits nicely emerging online tasks such as fog computing, where online decisions need to flexibly adapt to the temporally unpredictable availability of resources. Tailored for...
Paper Details
Title
Online learning for “thing-adaptive” Fog Computing in IoT
Published Date
Oct 1, 2017
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