Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.