Microservice throttling based on learned demand predictions

Abstract

A dynamically-managed microservices platform. The microservices platform is configured to selectively accept admission of microservices and to selectively throttle microservices based on a continually-trained machine learning model. The system is configured to implement multiple microservice lifecycle strategies, where individual ones of the multiple microservice lifecycle strategies use a prediction model having long-term and short term demand predictions. One or another of the multiple microservice lifecycle strategies are invoked at different times under different conditions. Under a first set of conditions, a prediction model is used to select a first strategy to admit or throttle a microservice based upon long-term or short-term demand predictions. Under a second set of conditions, the prediction model is used to select a different strategy to admit or throttle a microservice. The prediction model is continually trained such that new sets of observations and new sets of predicted outputs are codified into the machine learning model.