Microsoft Technology Licensing, LLC

Sandeep Kumta Vishnu

Thomas Moscibroda

Gurpreet Virdi

Yunus Mohammed

Inigo Goiri Presa

Eli Cortez Custodio Vilarinho

Felipe Vieira Frujeri

Yandan Wang

Rakesh Akkera

Systems and methods are provided for scheduling a virtual machine (VM) to host a workload in a cloud system. In particular, the disclosed technology schedules an evicted VM for redeploying an interruptible workload. The scheduling is based on capacity prediction and inference data associated with a type of the evicted VM. Capacity signal predictor generates training data for training a machine learning model using capacity signal history data of the cloud system. The machine-learning model, once trained, predicts capacity including a rate of evictions for the types of the evicted VM. The predicted data is based on at least the current status of available computing resources. Upon receiving a notice associated with a workload interruption, the intelligent scheduler prioritizes the evicted VM for scheduling and determines whether to defer redeploying the evicted VM based on the rate of eviction for the type of the evicted VM.

Machine-learning-based replenishment of interruptible workloads in cloud environment

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Bellevue, WA, United States of America

Sandeep Kumta Vishnu