Abstract: An embodiment includes detecting an application availability metric and a performance data metric of a system by a Tracing Policy Analyzer, responsive to detecting the application availability metric and the performance data metric, training a Bayesian optimization model of the Tracing Policy Analyzer based on the application availability metric and the performance data metric where the Bayesian optimization model outputs an optimal tracing policy. The embodiment includes detecting the optimal tracing policy by a Tracing Level Determination Module of an application of the system, responsive to detecting the optimal tracing policy, computing a computed tracing level by the Tracing Level Determination Module executing a k-nearest neighbors algorithm based on the optimal tracing policy. The embodiment also includes tracing of the application of the system by a Tracer based on the computed tracing level.

Abstract: In several aspects for generation of high quality synthetic observability data for computing systems, traces and logs from a system are collected as a seed dataset. Multiple conditional variational autoencoder (VAE) models are trained using the seed dataset for learning association between the traces and the logs. Synthetic traces and logs are generated using the multiple CVAE models while retaining the association between the traces and the logs for the synthetic traces and logs.

Abstract: Embodiments monitor a target system to collect at least one data metric; pre-process the at least one data metric as a seed based on a predetermined policy; encode the pre-processed seed using a transform; post-process the encoded seed in a frequency domain; generate synthetic metrics data by applying an inverse transform to the post-processed seed; and train an artificial intelligence (AI) model using the generated synthetic metrics data.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtain factor(s) relevant to a given resource. The program code determines relationships between the factor(s). Based on parameters comprising the relationships, the program code identifies, from a search space, configuration(s) for resource(s) and configuration(s) for workload(s) in the computing environment. The program code executes, based on a pre-defined policy, a test: a workload configured according to a configuration in a system under test instance configured according to a configuration. The program code obtains performance measurements for the test in the system under test instance. The program code utilizes the performance measurements to update a known data set.

Abstract: In several aspects for generation of high quality synthetic observability data for computing systems, traces and logs from a system are collected as a seed dataset. Multiple conditional variational autoencoder (VAE) models are trained using the seed dataset for learning association between the traces and the logs. Synthetic traces and logs are generated using the multiple CVAE models while retaining the association between the traces and the logs for the synthetic traces and logs.

Abstract: Computer implemented method, systems, and computer program products include program code executing on a processor(s) monitors objects in the technical environment to collect real-time operational data. The program code obtains historical operational data and historical resource data from the technical environment and generates models to evaluate health states and resource states of the one or more objects. The program code applies the models and identifies objects as abnormal. The program code obtains a topology of the technical environment to identify objects impacted by the abnormal object(s). The program code generates a resource aware dynamic operational data collection plan.

Abstract: Disclosed embodiments provide techniques for dynamic data collection. The dynamic data collection includes determining a data generation temporal pattern. Based on the data generation temporal pattern, a data collection strategy is created. The data collection strategy can be based on one or more data collection goals. The data collection strategy can contain specific details on how data is to be collected. A data infrastructure evaluation is performed, which provides pricing models for resources such as electricity and/or network bandwidth. A data collection policy is created based on the data collection strategy and the data infrastructure evaluation. The data collection policy can contain specific details on when data is to be collected and what strategy to use for the collection. A data transfer schedule is created based on the data collection policy. The data transfer schedule determines when to collect data from one or more data source devices.

Abstract: A method for preparing a salt-resistant and detergent-resistant alginate fiber by contacting and infiltrating a finished alginate fiber with a borate-containing adjuvant solution and using selected proper raw materials in selected proportions and reaction conditions. The preparation method solves the problem that the alginate fiber and a fabric made therefrom are neither salt-resistant nor detergent-resistant. The borate-modified alginate fiber and a fabric made therefrom have excellent salt resistances, and can be washed with an alkaline detergent. The swelling degree of the alginate fiber modified by the adjuvant solution can be reduced to 39.8% after the fiber is immersed in a normal saline at 30° C. for 72 h and can be reduced to 55.3% after the fiber is immersed in a standard detergent for 24 h, while the fiber maintains its original morphology without any obvious dissolution phenomenon, and the self flame-resisting performance of the alginate fiber is also maintained.

Abstract: A method for preparing a salt-resistant and detergent-resistant alginate fiber by contacting and infiltrating a finished alginate fiber with a borate-containing adjuvant solution and using selected proper raw materials in selected proportions and reaction conditions. The preparation method solves the problem that the alginate fiber and a fabric made therefrom are neither salt-resistant nor detergent-resistant. The borate-modified alginate fiber and a fabric made therefrom have excellent salt resistances, and can be washed with an alkaline detergent. The swelling degree of the alginate fiber modified by the adjuvant solution can be reduced to 39.8% after the fiber is immersed in a normal saline at 30° C. for 72 h and can be reduced to 55.3% after the fiber is immersed in a standard detergent for 24 h, while the fiber maintains its original morphology without any obvious dissolution phenomenon, and the self flame-resisting performance of the alginate fiber is also maintained.

Abstract: Data values are exhibited based on sector-ring angles. The data values to be exhibited are first received by a processing device. The sector-ring angles are then determined for the data values to be exhibited. Using the sector-ring angles for the data values to be exhibited, the data values are exhibited in a sector-ring manner.