Microsoft Technology Licensing, LLC

Felipe Vieira Frujeri

Thomas Moscibroda

David A Maltz

Ricardo Gouvea Bianchini

Qingwei Lin

Vivek Gupta

Gurpreet Virdi

Yunus Mohammed

Chetan Bansal

Si Qin

Behnaz Arzani

Inigo Goiri Presa

Eli Cortez Custodio Vilarinho

Henry Lee

Jue Zhang

Ajay Kumar Karanam

Mahmoud Sayed

Intaik Park

Yandan Wang

Vijay Govind Panjeti

Ashok Sai Madala

Robert Edward Minneker

Alexander David Fischer

Preston Tapley Stephenson

Soumya Ram

Sidhanth M Panjwani

Camille Jean Couturier

Fangkai Yang

Bowen Pang

Rakesh Akkera

Mitchell Gregory Spryn

Sandeep Kumta Vishnu

Jiaqi Gao

Xiaohang Wang

A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.

System and method of dynamically adjusting virtual machines for a workload

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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Kirkland, WA, United States of America

Felipe Vieira Frujeri