A testing system and method for testing application code against various failure scenarios. The testing system and method generate a test workflow including test source code implementing a series of actions that affect an application component and or an infrastructure component included in application code. The testing system and method execute the test workflow to determine the performance of the application code during one or more failure scenarios caused by the series of actions included in the test workflow. Performance data generated by the test code is analyzed by a performance analysis service or method to identify limitations of the application code and build resiliency patterns that address the limitations and improve the performance of the application code.

Embodiments for managing an as-a-service (aaS) computing system by a processor are provided. Input provided by at least one computing device to a cloud platform is monitored. Output provided by the cloud platform to the at least one computing device in response to the input provided by the at least one computing device is monitored. At least one local model is generated based on the input provided to the cloud platform and the output provided to the at least one computing device.

Distributed ledger-based networks (DLNs) employ self-executing codes, also known as smart contracts, to manage interactions occurring on the networks, which may result in the generation of a massive amount of DLN state data representing the interactions and participants thereof. The instant disclosure discloses systems, apparatus and methods that allow interactions to occur on the DLNs without modification to stored data, thereby improving the storage capabilities of the networks.

A quality score for a computer application release is determined using a first number of unique users who have launched the computer application release on user devices and a second number of unique users who have encountered at least once an abnormal termination with the computer application release on user devices. Additionally or optionally, an application quality score can be computed for a computer application based on quality scores of computer application releases that represent different versions of the computer application. Additionally or optionally, a weighted application quality score can be computed for a computer application by further taking into consideration the average application quality score and popularity of a plurality of computer applications.

Example aspects include techniques for implementing peer group evaluation for comparative anomaly. These techniques may include determining a candidate group including a plurality of component metrics, and determining that the plurality of component metrics are a peer group based at least in part on a cluster profile of the candidate group and the candidate group exhibiting peer-like behavior of a period of time. In addition, the techniques may include detecting anomalous activity based at least in part on first performance information of a component metric deviating from second performance information for the peer group, and providing a notification of the anomalous activity.

A method for minimizing allocation failures in a cloud computing system without overprovisioning may include determining a predicted supply for a virtual machine series in a system unit of the cloud computing system during an upcoming time period. The predicted supply may be based on a shared available current capacity and a shared available future added capacity for the virtual machine series in the system unit. The method may also include predicting an available capacity for the virtual machine series in the system unit during the upcoming time period. The predicted available capacity may be based at least in part on a predicted demand for the virtual machine series in the system unit during the upcoming time period and the predicted supply. The method may also include taking at least one mitigation action in response to determining that the predicted demand exceeds the predicted supply during the upcoming time period.

A compact data structure generation engine can be used to generate a compact data structure that represents performance data for high-scale networks. The compact data structure representing the performance data can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

A system for monitoring job execution includes an interface and a processor. The interface is configured to receive an indication to start a cluster processing job. The processor is configured to determine whether processing a data instance associated with the cluster processing job satisfies a watchdog criterion; and in the event that processing the data instance satisfies the watchdog criterion, cause the processing of the data instance to be killed.

A device implementing a system to provide an application library for application discovery includes a processor configured to update an application library including ordered lists of installed applications grouped into a respective plurality of application categories. The updating may be based on an application usage history of a device. The processor is further configured to receive a first user input to open an application library user interface corresponding to the application library. The processor is further configured to display, in response to the first user input, a plurality of application launch interfaces, each of the plurality of application launch interfaces corresponding to a respective one of the ordered lists of installed applications grouped into the respective plurality of application categories within the application library user interface.

A method includes determining, by an analysis system, a system aspect of a system for an issue response analysis evaluation. The method further includes determining, by the analysis system, at least one evaluation perspective and at least one evaluation viewpoint for use in performing the issue response analysis evaluation on the system aspect. The method further includes obtaining, by the analysis system, issue response analysis data regarding the system aspect in accordance with the at least one evaluation perspective and the at least one evaluation viewpoint. The method further includes calculating, by the analysis system, an issue response analysis rating as a measure of system issue response analysis maturity for the system aspect based on the issue response analysis data, the at least one evaluation perspective, the at least one evaluation viewpoint, and at least one evaluation rating metric.