Systems are provided for logging transactions in heterogeneous networks that include a combination of one or more instrumented components and one or more non-instrumented components. The instrumented components are configured to generate impersonated log records for the non-instrumented components involved in the transaction processing hand-offs with the instrumented components. The impersonated log records are persisted with other log records that are generated by the instrumented components in a transaction log that is maintained by a central logging system to reflect a complete flow of the transaction processing performed on the object, including the flow through the non-instrumented component(s).

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.

Embodiments of the invention are directed to static program code analysis and detection of architectural flaws. The system provides a rule-based anomaly detection engine structured to capture application logs during construction of technology program code and dynamically detect anti-pattern conflicts to remediate defects in the technology program code. In particular, the system receives a request to perform defect analysis of a first technology program code. In response, the system constructs a first layer transition map based on mapping each of a plurality of first classes associated with the first technology program code to one or more application layers. The system may then determine, via an anomaly detection engine component, one or more anomalies associated with the first technology program code.

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.

Auditing information is captured from a processing stack of an invoked application. An annotation customized for that invocation context is processed to filter and/or add additional audition information available from the processing stack. The customized auditing information is then sent to a destination based on a processing context of the invoked application when the invoked application completes processing. In an embodiment, the customized auditing information is housed in a data store and an interface is provided for customized query processing, report processing, event processing, a notification processing.

A graph-based data multi-operation system includes a data multi-operation management subsystem coupled to an application and accelerator subsystems. The data multi-operation management subsystem receives a data multi-operation graph from the application that identifies first data and defines operations for performance on the first data to transform the first data into second data. The data multi-operation management subsystem assigns each of the operations to at least one of the accelerator systems, and configures the accelerator subsystems to perform the operations in a sequence that transforms the first data into the second data, When the data multi-operation management subsystem determine a completion status for the performance of the operations by the accelerator subsystems, it transmits a completion status communication to the application that indicates the completion status of the performance of the plurality of operations by the plurality of accelerator subsystems.

Auditing information is captured from a processing stack of an invoked application. An annotation customized for that invocation context is processed to filter and/or add additional audition information available from the processing stack. The customized auditing information is then sent to a destination based on a processing context of the invoked application when the invoked application completes processing. In an embodiment, the customized auditing information is housed in a data store and an interface is provided for customized query processing, report processing, event processing, a notification processing.

Techniques are disclosed for managing the connection assignments of a plurality of accessory devices to one or more hub devices. In one example, a user device acting as a leader device receives an assignment request from an accessory device. The user device may obtain information corresponding to hub attributes from the one or more hub devices. The user device may also obtain accessory traits from the accessory device. The user device can compare the accessory traits with the hub attributes to determine a connection score for each hub device. The user device can then assign the accessory device to the hub device with the highest connection score.

Techniques for dynamic server groups that can be patched together using stream clustering algorithms, and learning components in order to reuse the repeatable patterns using machine learning are provided herein. In one example, in response to a first risk associated with a first server device, a risk assessment component patches a server group to mitigate a vulnerability of the first server device and a second server device, wherein the server group is comprised of the first server device and the second server device. Additionally, a monitoring component monitors data associated with a second risk to the server group to mitigate the second risk to the server group.

Aspects of the disclosure relate to providing and maintaining efficient and effective processing of sets of work items in enterprise computing environments by optimizing distributed and parallelized batch data processing. A computing platform may initialize a monitoring process configured to monitor a pending workload in a work queue database. Subsequently, the computing platform may cause the monitoring process to query the work queue database and create one or more historical records indicative of a workload processing status associated with one or more processing workers. Then, the computing platform may identify one or more new parameter values for one or more processing parameters associated with the one or more processing workers based on the one or more historical records. Thereafter, the computing platform may configure the one or more processing workers based on the one or more new parameter values identified for the one or more processing parameters.