Abstract: A mechanism is provided for determining a predicted performance of a database com. A first model for a database is determined using machine learning and training data based on monitoring the database operating in a production environment. A second model for the database is determined by combining the first model and a knee of curve formula for the database. The second model is stored for use in determining the predicted performance of the database in response to a database query.

Abstract: A computer-implemented method includes: monitoring, by a computing device, resource usage data for a hypervisor; monitoring, by the computing device, resource usage data for a guest machine; identifying, by the computing device, outliers in the resource usage data for the hypervisor and outliers in the resource usage data for the guest machine; executing, by the computing device, a root cause model based on the resource usage data for the hypervisor and the resource usage data for the guest machine; generating, by the computing device, a root cause report identifying results of the executed root cause model; and providing, by the computing device, the root cause report for display.

Abstract: A computer-implemented method includes: monitoring, by a computing device, resource usage data for a hypervisor; monitoring, by the computing device, resource usage data for a guest machine; identifying, by the computing device, outliers in the resource usage data for the hypervisor and outliers in the resource usage data for the guest machine; executing, by the computing device, a root cause model based on the resource usage data for the hypervisor and the resource usage data for the guest machine; generating, by the computing device, a root cause report identifying results of the executed root cause model; and providing, by the computing device, the root cause report for display.

Abstract: A computer-implemented method includes: monitoring, by a computing device, resource usage data for a hypervisor; monitoring, by the computing device, resource usage data for a guest machine; identifying, by the computing device, outliers in the resource usage data for the hypervisor and outliers in the resource usage data for the guest machine; executing, by the computing device, a root cause model based on the resource usage data for the hypervisor and the resource usage data for the guest machine; generating, by the computing device, a root cause report identifying results of the executed root cause model; and providing, by the computing device, the root cause report for display.

Abstract: A mechanism is provided for determining a predicted performance of a database com. A first model for a database is determined using machine learning and training data based on monitoring the database operating in a production environment. A second model for the database is determined by combining the first model and a knee of curve formula for the database. The second model is stored for use in determining the predicted performance of the database in response to a database query.

Abstract: A computer-implemented method includes: monitoring, by a computing device, resource usage data for a hypervisor; monitoring, by the computing device, resource usage data for a guest machine; identifying, by the computing device, outliers in the resource usage data for the hypervisor and outliers in the resource usage data for the guest machine; executing, by the computing device, a root cause model based on the resource usage data for the hypervisor and the resource usage data for the guest machine; generating, by the computing device, a root cause report identifying results of the executed root cause model; and providing, by the computing device, the root cause report for display.

Abstract: An electrochemical gas sensor has a working electrode having a gas porous membrane and a catalyst layer formed on one side of the membrane; a counter electrode, electrolyte in contact with the catalyst both of the working electrode and of the counter electrode; and a support that is in contact with, and presses against the side of the working electrode remote from the electrolyte and that compresses the electrodes and the electrolyte together. The support includes open areas enabling gas to contact the membrane. The support provides a faster response and provides greater efficiency of catalyst usage.

Abstract: An electrochemical gas sensor has a working electrode having a gas porous membrane and a catalyst layer formed on one side of the membrane; a counter electrode electrolyte in contact with the catalyst both of the working electrode and of the counter electrode; and a support that is in contact with, and presses against the side of the working electrode remote from the electrolyte and that compresses the electrodes and the electrolyte together. The support includes open areas enabling gas to contact the membrane. The support provides a faster response and provides greater efficiency of catalyst usage.