Microsoft Technology Licensing, LLC

Maja Stikic

Matthew Snider

Nikola Puzovic

Drazen Sumic

Neda M Pistinjat

Milan Micić

Aleksa Brkic

Vesna Todorovic

Jelena Petrovic

Nikola Pavlovic

Ivan Nedic

Systems and methods related to prediction-based resource orchestration in a service fabric are described. An example method for reducing an interruption rate associated with a customer application offered as a collection of services includes predicting one or more quiet time periods associated with each of the service replicas associated with a service fabric, where each of the one or more quiet time periods corresponds to a low value of a predicted load specifying a consumption of a metric by a respective service replica on a given node. The method further includes during the one or more quiet time periods predicted by a trained machine learning model, performing an impact-less failover for one or more of the service replicas by scheduling a move for the one or more service replicas from a first node associated with the service fabric to a second node associated with the service fabric.

Prediction-based resource orchestration in a service fabric

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Belgrade, Republic of Serbia

Maja Stikic