BBAuthors: Karn, R. R.; Advisor: -; Participants: Kudva, P.; Elfadel, I. A. M. (2019)
Cloud network monitoring data is dynamic and distributed. Signals to monitor the cloud can appear, disappear or change their importance and clarity over time. Machine learning (ML) models tuned to a given data set can therefore quickly become inadequate. A model might be highly accurate at one point in time but may lose its accuracy at a later time due to changes in input data and their features. Distributed learning with dynamic model selection is therefore often required. Under such selection, poorly performing models (although aggressively tuned for the prior data) are retired or put on standby while newor standby models are brought in. The well-known method of Ensemble ML (EML) ma... 
