Methods, systems, and computer-readable storage media for receiving a capture file, the capture file holding data representative of a workload executed in a source database system, processing the capture file to provide a replay file, the replay file being in a format that is executable by a replayer to replay the workload in a target database system, the workload including a set of requests represented within the replay file, providing a set of tags associated with the replay file, the set of tags including one or more tags, each tag associated with a request in the set of requests, and during replay of the workload in the target database system: pausing replay of the workload in response to a tag, executing a request associated with the tag, providing replay results specific to the request, and selectively resuming replay of the workload in the target database system.

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.

Techniques described herein relate to a method for deploying workflows. The method may include receiving, by a global orchestrator of a device ecosystem, a request to execute a workflow; decomposing, by the global orchestrator, the workflow into a plurality of workflow portions; executing, by the global orchestrator, a metaheuristic algorithm to generate a result comprising a plurality of domains of the device ecosystem in which to execute the plurality of workflow portions; and providing, by the global orchestrator, the plurality of workflow portions to respective local orchestrators of the plurality of domains based on the result of executing the metaheuristic algorithm.

A device may receive software data identifying current logs and events associated with software products utilized by an entity and may process the software data, with a machine learning model, to generate error severity scores for the software products. The machine learning model may be trained based on historical software data identifying events and logs associated with software products utilized by the entity and based on a combination of historical health scores, historical sentiment scores, and historical dissimilarity scores for the software products. The device may process the error severity scores, with a prioritization model, to generate prioritized error scores and may process the error severity scores and the prioritized error scores, with a root cause analysis model, to generate root cause data identifying root causes associated with the error severity scores. The device may perform one or more actions based on the root cause data.

Systems and methods for generating, distributing, and using performance mode BIOS configurations are disclosed. Each performance mode BIOS configuration can be a unique set of BIOS setting values that have been established to optimize a particular performance parameter or set of performance parameters, such as boot speed or operating system installation speed. Based on a given hardware configuration and/or set of performance parameters, one or more performance mode BIOS configurations can be packaged and transferred to a memory of a BMC in the form of one or more configuration payloads. The BIOS Setup Utility can display all configuration payloads, such as listed by the type of performance mode (e.g., “Boot Speed Performance Mode” and “OS Installation Performance Mode”), that are available in the BMC memory and allow a user to overwrite the memory containing the current BIOS configuration with a selected configuration payload.

A data storage management layer comprises computing device(s), operatively connected to storage resources, which comprise data storage units and control units. The data storage management layer is operatively connected to the storage resources. They are operatively connected to host computers. A sub-set of the storage resources are assigned to each host, in order to provide storage services according to performance requirements predefined for the host, thereby generating Virtual Private Arrays (VPA). The computing device(s) are configured to perform a method of managing the data storage system comprising: (a) implement storage management strategies, comprising rules. The rules comprise conditions and actions. The actions are capable of improving VPA performance in a dynamic manner; (b) repetitively performing: (i) monitor VPA performance for detection of compliance of VPA with the condition(s); and (ii) responsive to detection of compliance of VPA with the condition(s), performing the action(s).

Each of one or more commit cycles may be associated with a predicted number of updates. A statistics collection time for a database table can be determined by estimating a sum of predicted updates included in one or more commit cycles. Whether the estimated sum of predicted updates is greater than a first threshold may be determined. In addition, a progress point for a first one of the commit cycles can be determined. A time to collect statistics may be selected based on the progress point of the first commit cycle.

Workload profiling can be used in a distributed computing environment for automatic performance tuning. For example, a computing device can receive a performance profile for a workload in a distributed computing environment. The performance profile can indicate resource usage by the workload in the distributed computing environment. The computing device can determine a performance bottleneck associated with the workload based on the resource usage specified in the performance profile. A tuning profile can be selected to reduce the performance bottleneck associate with the workload. The computing device can output a command to adjust one or more properties of the workload in accordance with the tuning profile to reduce the performance bottleneck associated with the workload.

An instruction processing device and an instruction processing method are provided. The instruction processing device includes: a first-level branch target buffer, configured to store entries of a first plurality of branch instructions; a second-level branch target buffer, configured to store entries of a second plurality of branch instructions, wherein the entries in the first-level branch target buffer are accessed faster than the entries in the second-level branch target buffer; an instruction fetch unit coupled to the first-level branch target buffer and the second-level branch target buffer, the instruction fetch unit including circuitry configured to add, for a first branch instruction, one or more entries corresponding to the first branch instruction into the first-level branch target buffer when the one or more entries corresponding to the first branch instruction are identified in the second-level branch target buffer; and an execution unit including circuitry configured to execute the first branch instruction.

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.