Apparatuses, systems, and techniques to collect compute performance information. In at least one embodiment, an API is performed to cause two or more portions of at least one software program to be concurrently performed a plurality of times in order to generate one or more performance metrics.

According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor, may cause the processor to receive information regarding a performance characteristic of an application during predetermined time periods. The processor may calculate a rate of change in the performance characteristic over the predetermined time periods. Based on a determination that the performance characteristic of the application has changed over the predetermined time periods, the processor may tune values of a set of parameters for the application based on the calculated rate of change in the performance characteristic.

Monitoring a set of virtual users during performance testing and reporting virtual user operations data to ensure an ongoing and constant transactions per second load and providing test result data with evidence of constant transactions per second (TPS) during the test. Generating and executing performance tests under constant TPS includes restarting virtual users that are terminated during the performance tests.

Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

A system, method and apparatus for choosing a digital processing platform that is optimal for a specified type of application and satisfies a set of user-specified constraints is provided. In operation, all known parameters on all available processing platforms in a database are stored, providing this information to a computer software application run by the user by querying the database, and then allowing a remote user to specify the constraints, in terms of hardware and system software, to eliminate those entries that would not satisfy the constraints in a step-by-step filtering process. The user then chooses a set of application programs to run on the platforms that were not eliminated. The runtime performance parameters/characteristics—e.g. computational throughput, I/O bandwidth, environmental parameters, etc. are measured to select the optimal solution. The system and method also allows for a regression test to ensure consistency between test software processes running on discrete platforms.

Technologies for managing memory on a compute device are disclosed. The compute device is configured to determine the quality of a user experience of the compute device when a certain combination of applications are running on the compute device and stores an indication of the quality of the user experience that corresponds to that combination of applications. At a later time, such as when a user selects an application to be launched, the compute device may check if the current combination of applications is expected to have an acceptable quality of a user experience. If not, the compute device may kill one or more of the current combination of applications to improve the expected quality of the user experience.

According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor, may cause the processor to receive information regarding a performance characteristic of an application during predetermined time periods. The processor may calculate a rate of change in the performance characteristic over the predetermined time periods. Based on a determination that the performance characteristic of the application has changed over the predetermined time periods, the processor may tune values of a set of parameters for the application based on the calculated rate of change in the performance characteristic.

Disclosed is a method for estimating database management system performance, in which a performance change ratio of a DBMS can be determined once a first knob group, a second knob group, and a data volume of active data in data managed by the DBMS are obtained, without actually configuring the second knob group in the DBMS, executing a job by the DBMS, and then observing the execution. In other words, the performance change ratio of the DBMS can be estimated without interacting with the DBMS. DBMS security can be ensured, performance measurement approaches are provided for self-tuning and self-management of the DBMS, and reliable and stable running of the DBMS is ensured.

This document describes a framework for measuring and improving the performance of applications, such as distributed applications and web applications. In one aspect, a method includes performing a test on an application. The test includes executing the application on one or more computers and, while executing the application, simulating a set of workload scenarios for which performance of the application is measured during the test. While performing the test, a set of performance metrics that indicate performance of individual components involved in executing the application during the test is obtained. A knowledge graph is queried using the set of performance metrics. The knowledge graph links the individual components to corresponding performance metrics and defines a set of hotspot conditions that are each based on one or more of the corresponding performance metrics for the individual components. A given hotspot condition is detected based on the set of performance metrics.

A computer implemented method comprises receiving a request to provision a container as a software container on a current node of the cluster. The method further comprises accessing a performance information data store (PIDS) to obtain a record associated with the container that includes benchmarked performance metrics including container-required resources associated with a benchmark-specified node of the container. The method further comprises accessing the PIDS to obtain a record associated with the current node of the cluster that includes current-node performance metrics associated with the current node. The method further comprises comparing the benchmarked performance metrics with the current node performance metrics to determine that a difference exists, and conditioned upon the difference existing adjusting, with a resource adjustment calculator, the container-required resources based on the determination of how much of a difference exists. The method then provisions the container on the current node with adjusted container-required resources.