Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.

At least one processor is configured to obtain measurement information comprising an indication of an amount of utilization of a hardware resource of a first server node by a plurality of processing groups and to determine that the amount of utilization of the hardware resource is above a threshold amount of utilization. The at least one processor is further configured to select a given processing group for redistribution based at least in part on the determination that the amount of utilization of the hardware resource is above the threshold amount and on an amount of utilization of the hardware resource by the given processing group. The at least one processor is further configured to determine that a second server node comprises enough available capacity of the hardware resource and to redistribute the given processing group to the second server node based at least in part on the determination.

Aspects of the present disclosure involve systems and methods for performing operations comprising providing a messaging application comprising a feature to a client device, the feature being implemented by operations having alternative complexity levels, wherein a first complexity level represents a first amount of device resources consumed by a first set of operations, and wherein a second complexity level represents a second amount of device resources consumed by a second set of operations; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing the feature to be implemented on the client device by the first set of operations having the first complexity level that consume a greater amount of device resources than the second set of operations having the second complexity level.

An example method of managing exclusive affinity for threads executing in a virtualized computing system includes: determining, by an exclusive affinity monitor executing in a hypervisor of the virtualized computing system, a set of threads eligible for exclusive affinity; determining, by the exclusive affinity monitor, for each thread in the set of threads, impact on performance of the threads for granting each thread exclusive affinity; and granting, for each thread of the set of threads having an impact on performance of the threads less than a threshold, exclusive affinity to respective physical central processing units (PCPUs) of the virtualized computing system.

Techniques described herein relate to a method for resource allocation using fingerprint representations of telemetry data. The method may include receiving, at a resource allocation device, a request to execute a workload; obtaining, by the resource allocation device, telemetry data associated with the workload; identifying, by the resource allocation device, a breakpoint based on the telemetry data; identifying, by the resource allocation device, a workload segment using the breakpoint; generating, by the resource allocation device, a fingerprint representation using the workload segment; performing, by the resource allocation device, a search in a fingerprint catalog using the fingerprint representation to identify a similar fingerprint; obtaining, by the resource allocation device, a resource allocation policy associated with the similar fingerprint; and performing, by the resource allocation device, a resource policy application action based on the resource allocation policy.

A cable distribution system includes a head end connected to a plurality of customer devices through a transmission network that provides data suitable for the plurality of customer devices. A traffic monitoring system receives from a customer support device a first data request for a parameter of one of the plurality of customer devices. The traffic monitoring system provides a second data request for the parameter of the one of said plurality of customer devices to a customer premise equipment management system in response to receiving the first data request. The traffic monitoring system receiving a first data response including the parameter from the customer premise equipment management system in response to providing the second data request to the customer premise equipment management system. The traffic monitoring system providing a second data response including the parameter from the traffic management system to the customer support device in response to receiving the first data response.

A process includes obtaining a workload and a set of candidate computing resources and predicting amounts of carbon emissions attributable to executing the workload on different members of the set of candidate computing resources. The process also includes predicting measures of computing performance in executing the workload of the different members of the set of candidate computing resources and computing a set of scores based on the amounts of carbon emissions and the measures of computing performance. The process also includes orchestrating the workload based on the scores.

The subject technology retrieves information related to a set of instances of compute service managers, each instance of a particular compute service manager being associated with a set of virtual warehouses. The subject technology filters the information to determine a set of candidates from the set of instances of compute service managers. The subject technology sorts the set of candidates based at least in part on each workload of each of the set of candidates. The subject technology selects a candidate compute service manager from the set of instances of compute service managers to issue a query restart by randomly selecting an execution node, the execution node being included in a particular virtual warehouse associated with the candidate compute service manager, the selecting facilitating improving utilization of cluster resources and improving query execution on the selected candidate compute service manager.

A system is configured to obtain a plurality of performance metrics related to performance of a software application in a current application environment and each of a plurality of model application environments. The system assigns a score to each of the performance metrics collected for the current application environment and each of the model application environments, compares the respective scores assigned to each performance metric collected for the current application environment and each of the model application environments, and detects that at least one model application environment has a higher score associated with at least one performance metric as compared to the respective score of the at least one performance metric collected for the current application environment. The system determines a recommendation to use the at least one model application environment for the software application based on the detecting.

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.