Microsoft Technology Licensing, LLC

Eli Cortez Custodio Vilarinho

Inigo Goiri Presa

Felipe Vieira Frujeri

Yandan Wang

Lev Novik

Thomas Moscibroda

Philip Alan Bernstein

Yeye He

Qingwei Lin

Gurpreet Virdi

Yunus Mohammed

Vivek Gupta

Chetan Bansal

Si Qin

Jue Zhang

Mahmoud Sayed

Preston Tapley Stephenson

Soumya Ram

Sidhanth M Panjwani

Camille Jean Couturier

Fangkai Yang

Bowen Pang

Rakesh Akkera

Sandeep Kumta Vishnu

Matheus De Oliveira Leao

Raphael Ghelman

Robert Edward Minneker

Alexander David Fischer

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

In examples, a requestor device requests data from a data platform. A response from the data platform may include a version identifier indicating a version of data used to process the request and a device identifier indicating the server device that processed the request. Accordingly, the requestor device may include the version identifier and device identifier in a subsequent request, such that the request is routed to the server device accordingly. In examples, the server device may evaluate the version identifier to determine whether the request is associated with a different version of the data, as may occur when the data of the server device has since been updated. Similarly, the requestor device may evaluate a version identifier from the data platform as compared to that of a previously received response to determine whether the response is associated with a different version of data than that of a previous response.

Paginated data transfer techniques

Techniques and constructs that improve annotating target columns of a target database by performing automated annotation of the target columns using sources. The techniques include calculating a similarity score between a target column and columns extracted from a table that is included in a source. The similarity score is calculated based at least in part on a similarity between a value in the target column of the target database and a column value of the extracted column from the table and on a similarity between an identity of the target column of the target database and column identities of the extracted columns from the table. In some examples, the techniques calculate similarity scores for one or more extracted columns and annotate the target column based on the similarity scores.

Automated database schema annotation

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Eli Cortez Custodio Vilarinho