Abstract: The present embodiments relate to updating a dataflow interactive cluster with zero downtime. A request to update a first dataflow cluster can be received, and a second dataflow cluster can be generated as a replacement cluster to execute received queries. Generating the second dataflow cluster can include identifying a second series of executor nodes that are configured to execute queries from the gateway node via a second driver node. A first update to a configuration of a host configuration node can be performed to register the second dataflow cluster as an active endpoint and identify the first dataflow cluster as an inactive endpoint. When no active queries exist, a second update to the configuration can be provided to remove the first dataflow cluster from the configuration to direct subsequent queries from the gateway node to the second dataflow cluster.

Abstract: The present embodiments relate to updating a dataflow interactive cluster with zero downtime. A request to update a first dataflow cluster can be received, and a second dataflow cluster can be generated as a replacement cluster to execute received queries. Generating the second dataflow cluster can include identifying a second series of executor nodes that are configured to execute queries from the gateway node via a second driver node. A first update to a configuration of a host configuration node can be performed to register the second dataflow cluster as an active endpoint and identify the first dataflow cluster as an inactive endpoint. When no active queries exist, a second update to the configuration can be provided to remove the first dataflow cluster from the configuration to direct subsequent queries from the gateway node to the second dataflow cluster.

Abstract: The present disclosure relates to system and techniques for prediction of failures in resources deployed in a data plane of a cloud based infrastructure. The resource are selected from a plurality of cloud based resources arranged in a hierarchical manner and allocated to a client device. A predictor employs a first prediction model to obtain a first prediction of a failure of a resource, and a second prediction model to obtain a second prediction of the failure of the resource. Weights are assigned to the first prediction and second prediction based at least in part on a criterion. The predictor computes an overall prediction of the failure of the resource based at least in part on at least one of the first prediction, the second prediction or the respective weights assigned to the predictions. The overall prediction is utilized for restoring the failure of the resource.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: The present disclosure relates to system and techniques for prediction of failures in resources deployed in a data plane of a cloud based infrastructure. The resource are selected from a plurality of cloud based resources arranged in a hierarchical manner and allocated to a client device. A predictor employs a first prediction model to obtain a first prediction of a failure of a resource, and a second prediction model to obtain a second prediction of the failure of the resource. Weights are assigned to the first prediction and second prediction based at least in part on a criterion. The predictor computes an overall prediction of the failure of the resource based at least in part on at least one of the first prediction, the second prediction or the respective weights assigned to the predictions. The overall prediction is utilized for restoring the failure of the resource.

Abstract: The present embodiments relate to updating a dataflow interactive cluster with zero downtime. A request to update a first dataflow cluster can be received, and a second dataflow cluster can be generated as a replacement cluster to execute received queries. Generating the second dataflow cluster can include identifying a second series of executor nodes that are configured to execute queries from the gateway node via a second driver node. A first update to a configuration of a host configuration node can be performed to register the second dataflow cluster as an active endpoint and identify the first dataflow cluster as an inactive endpoint. When no active queries exist, a second update to the configuration can be provided to remove the first dataflow cluster from the configuration to direct subsequent queries from the gateway node to the second dataflow cluster.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.