According to one embodiment of the present invention, a system compares database transactions to corresponding performance information to identify conforming database transactions with an acceptable deviation from the performance information and outlier database transactions beyond the acceptable deviation from the performance information. The system aggregates information from a threshold quantity of conforming database transactions into an aggregated record, wherein the threshold quantity is dynamically adjusted, and generates a record for each outlier database transaction, wherein conditions for identifying the outlier database transactions are dynamically adjusted. Embodiments of the present invention further include a method and computer program product for managing database transactions in substantially the same manners described above.

A method includes scanning multiple incoming database transaction requests. Each transaction includes one or more operations. Operations are clustered into a set of combined operations based on type of operation constraints. Log records are prepared and written for re-performing operations upon system failures, and for undoing operations upon an operation or a transaction failing to be processed fully. Each set of combined operations are performed within a thread. Each update operation is marked for a transaction within which the update operation belongs. Recoverable update operations belonging to a plurality of transactions are performed within a single logical thread of execution.

The following description is directed to storing properties of requests to potentially block future requests having similar properties. In one example, a request can be received. A property of the request can be stored so that the property persists across an initialization sequence of a computer system. At least the property can be used to determine whether to block any future requests having similar properties.

A system and method for tracing individual transactions on method call granularity is disclosed. The system uses instrumentation based transaction tracing mechanisms to enhance thread call stack sampling mechanisms by a) only sampling threads executing monitored transactions while execution is ongoing b) tagging sampled call stacks with a transaction identifier for correlation of sampled call stacks with instrumentation bases tracing data. The combination of instrumentation based tracing with thread call stack sampling reduces sampling generated overhead by only sampling relevant thread, and reduces instrumentation generated overhead because it allows reducing instrumentation.

Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment. The method further includes replicating the transaction data in the shadow testing environment as input for testing the transaction service using the machine-learning model.

Technologies for providing predictive thermal management include a compute device. The compute device includes a compute engine and an execution assistant device to assist the compute engine in the execution of a workload. The compute engine is configured to obtain a profile that relates a utilization factor indicative of a present amount of activity of the execution assistant device to a predicted temperature of the execution assistant device, determine, as the execution assistant device assists in the execution of the workload, a value of the utilization factor of the execution assistant device, determine, as a function of the determined value of the utilization factor and the obtained profile, the predicted temperature of the execution assistant device, determine whether the predicted temperature satisfies a predefined threshold temperature, and adjust, in response to a determination that the predicted temperature satisfies the predefined threshold temperature, an operation of the compute device to reduce the predicted temperature. Other embodiments are also described and claimed.

An application performance monitoring system monitors a system having multiple components, automatically calculates a performance metric for the system, and determines a relationship between components of the software system that effect the performance metric. The system is configured to automatically generate a model of behavior of the performance metric using a genetic search process that randomly creates a set of functions and evolves those functions over multiple generations with evolution being skewed by a rule based on the determined relationship between components.

A method for managing a performance for at least one use case in a software application. The method includes: executing, for a first instance, a plurality of statements pertaining to a given use case on a target database, the plurality of statements being a part of the software application; collecting first performance metrics pertaining to the first instance of execution of the given use case; executing, for a second instance, the plurality of statements on the target database; collecting second performance metrics pertaining to the second instance of execution of the given use case; comparing the first performance metrics and the second performance metrics to determine difference therebetween; and executing at least one alarm action when the difference is greater than a predefined threshold.

A system and method for the distributed analysis of high frequency transaction trace data to constantly categorize incoming transaction data, identify relevant transaction categories, create per-category statistical reference and current data and perform statistical tests to identify transaction categories showing overall statistically relevant performance anomalies. The relevant transaction category detection considers both the relative transaction frequency of categories compared to the overall transaction frequency and the temporal stability of a transaction category over an observation duration. The statistical data generated for the anomaly tests contains next to data describing the overall performance of transactions of a category also data describing the transaction execution context, like the number of concurrently executed transactions or transaction load during an observation period. Anomaly tests consider current and reference execution context data in addition to statistic performance data to determine if detected statistical performance anomalies should be reported.