Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment, a computer identifies many alternative placements that respectively assign each pluggable database to a respective container database. For each alternative placement, a respective resilience score is calculated for each pluggable database that is based on the container database of the pluggable database. Based on the resilience scores of the pluggable databases for the alternative placements, a particular placement is selected as an optimal placement that would maximize utilization of computer resources, minimize database latencies, maximize system throughput, and maximize the ability of the database cluster to avoid a service outage.

A system and method for comparative performance monitoring of software release versions is disclosed. A remote network management platform may include a computational instance for managing a network. Transactions between a server of the computational instance and a client device in the managed network may be logged to a database. Transactions may be carried out by a release version of a set of program code units executing on the server. A software application executing on a computing device may retrieve and analyze a first set of transactions carried out by a first release version of the set of program code units to determine a first set of performance metrics, and do the same for a second set of transactions carried out by a second release version of the set of program code units to determine a second set of performance metrics. A classification filter may be applied to the metrics, and a quantitative comparison of the filtered first and second sets of performance metrics may be displayed on graphical user device.

Systems and methods for tuning hyperparameters for a machine learning model using a challenger champion model are described. A set of challenger configurations are generated based on a hyperparameter for tuning and a subset of the set of challenger configurations are scheduled for evaluation based on a loss function. A loss value derived from the loss function for the challenger configurations is compared to a loss value derived from the loss function for a champion configuration, and the champion configuration is replaced with the challenger configuration based on the comparison of the loss value derived from the loss function for the challenger configuration and the loss value derived from the loss function for the champion configuration. When the champion is replaced, a new set of challenger configurations is generated based on the new champion configuration.

Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Systems for testing computing devices of a medical records system using a simulated environment are provided. The simulated environment is generated by identifying transaction traffic over the network, where the transaction traffic comprises messages of different transaction types. A computing device is configured to output a simulated status by extrapolating a performance of the computing devices upon receiving a series of messages indicating the transaction types. The output of the computing device may indicate an error in the simulated environment where the performance of a computing device exceeds a performance threshold. Based on the error indication, transaction traffic can be diverted from the computing device to another computing device having performance capacity.

An aspect of the present disclosure facilitates accelerating outlier prediction of performance metrics in performance managers deployed in new computing environments. In one embodiment, a digital processing system receives an input data specifying a business vertical to which a new computing environment is directed, a performance metric of interest, and a computing component of the new computing environment for which the performance metric is sought to be measured. In response, the system selects, from a set of prediction models, a prediction model for the performance metric, based on the input data. The selected prediction model is then used in a performance manager to predict outliers for the performance metric of interest during operation of the new computing environment.

In one embodiment, a method selects a percentage of a plurality hosts that are coupled together via a network fabric and calculates a number of workloads needed for the percentage of hosts based on a benchmark test to run. A plurality of data compute nodes are configured on one or more host pairs in the percentage of the plurality of hosts to send and receive the number of workloads through the network fabric to perform the benchmark test. A set of measurements is received for sending and receiving the workloads through the network fabric using the plurality of data compute nodes. The method increases the percentage of the plurality of hosts until the set of measurements fails a criteria or the percentage of the plurality of hosts is all of the plurality of hosts.

Disclosed embodiments are directed at systems, methods, and architecture for operating a control plan of a microservices application. The control plane corresponds with data plane proxies associated with each of a plurality of APIs that make up the microservices application. The communication between the data plane proxies and the control plane enables automatic detection of service groups of APIs and automatic repair of application performance in real-time in response to degrading service node conditions.

The present disclosure relates to a method for deploying an intelligent edge cluster model includes checking an application requirement and a key performance indicator at a first edge node, dynamically assigning a first resource from the resources in the virtual resource pool of the intelligent edge cluster model to the first edge node, instructing a command to another edge node to assign one or more resources to the first edge node. In particular, the intelligent edge cluster model includes edge nodes and master controller having corresponding one or more resources combined to form the virtual resource pool.

Machine learning techniques to optimize user interface template selection are provided. In one technique, a first set of feature values pertaining to a first entity is identified. Multiple sets of feature values are also identified, each set of feature values pertaining to a different user interface (UI) template for rendering content items on a computer screen. For each set of feature values of the multiple sets, the set of feature values and the first set of feature values are inserted into a machine-learned model to generate a score, which is added to a set of scores, which set of scores is initially empty. Based on the set of scores, a particular UI template is selected for a content item. The content item is transmitted over a computer network to be presented on a screen of a computing device of the first entity according to the particular UI template.