Abstract: A system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

Abstract: A method and system of determining a health state of a microservice based application includes extracting traces of multiple variants of a microservice-based application. Topologies of the multiple variants are inferred based on the extracted traces. One or more topological differences of each of the multiple variants are identified based on the extracted traces. Each topological difference is ranked. The topological differences of the microservice-based application are displayed on a user interface, including a microservice map of the topological differences and a listing of a top ranking of the topological differences.

Abstract: A system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

Abstract: A specification of a topology of a microservices application is obtained as a plurality of nodes corresponding to a plurality of microservices of the microservices application. The plurality of nodes includes a root node, each of the plurality of nodes other than the root node has a timeout parameter and a retries parameter. Via constrained black box optimization, optimized values are selected for the timeout parameter and the retries parameter for each of the plurality of nodes other than the root node, subject to satisfying a specified end-to-end latency for the microservices application and minimizing an error rate for the microservices application. The microservices application is configured in accordance with the optimized values. At least one external request to the root node is responded to with the microservices application configured in accordance with the optimized values.

Abstract: A specification of a topology of a microservices application is obtained as a plurality of nodes corresponding to a plurality of microservices of the microservices application. the plurality of nodes includes a root node, each of the plurality of nodes other than the root node has a timeout parameter and a retries parameter. Via constrained black box optimization, optimized values are selected for the timeout parameter and the retries parameter for each of the plurality of nodes other than the root node, subject to satisfying a specified end-to-end latency for the microservices application and minimizing an error rate for the microservices application. the microservices application is configured in accordance with the optimized values. At least one external request to the root node is responded to with the microservices application configured in accordance with the optimized values.

Abstract: A method and system of determining a health state of a microservice based application includes extracting traces of multiple variants of a microservice-based application. Topologies of the multiple variants are inferred based on the extracted traces. One or more topological differences of each of the multiple variants are identified based on the extracted traces. Each topological difference is ranked. The topological differences of the microservice-based application are displayed on a user interface, including a microservice map of the topological differences and a listing of a top ranking of the topological differences.

Abstract: A method and system of optimizing parameters of a microservice-based application is provided. A microservice infrastructure of the microservice-based application is determined. One or more optimization objectives related to the microservice-based application are determined. Different combinations of timeout and retry values are tested for each microservice. A reward value is calculated for each of the different combinations of timeout and retry values. The microservice infrastructure is set to a combination of timeout and retry values having a highest reward value for the one or more optimization objectives.

Abstract: A method and system of optimizing parameters of a microservice-based application is provided. A microservice infrastructure of the microservice-based application is determined. One or more optimization objectives related to the microservice-based application are determined. Different combinations of timeout and retry values are tested for each microservice. A reward value is calculated for each of the different combinations of timeout and retry values. The microservice infrastructure is set to a combination of timeout and retry values having a highest reward value for the one or more optimization objectives.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.

Abstract: Various embodiments manage deployable computing environments. In one embodiment, a semantic model of a computing environment is analyzed. The computing environment is deployed based on the analysis of the semantic model. The deployment of the computing environment includes executing one or more automation scripts. One or more changes in a state of the computing environment are identified, for each automation script executed during the deployment of the computing environment, based on executing the automation script. The semantic model is updated based on the one or more changes in state identified for each automation script.

Abstract: Prepare an extruded polystyrene foam that is characterized by being a singular polymer foam that is free of halogenated blowing agents, having a milled primary surface, having a width of 750 millimeters or more, and further characterized by having a ?(CST/CSP) value that is 50 kilograms per cubic meter or less and a milled primary surface.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.

Abstract: Various embodiments manage deployable computing environments. In one embodiment, a semantic model of a computing environment is analyzed. The computing environment is deployed based on the analysis of the semantic model. The deployment of the computing environment includes executing one or more automation scripts. One or more changes in a state of the computing environment are identified, for each automation script executed during the deployment of the computing environment, based on executing the automation script. The semantic model is updated based on the one or more changes in state identified for each automation script.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.

Abstract: Various embodiments manage deployable computing environments. In one embodiment, a semantic model of a computing environment is analyzed. The computing environment is deployed based on the analysis of the semantic model. The deployment of the computing environment includes executing one or more automation scripts. One or more changes in a state of the computing environment are identified, for each automation script executed during the deployment of the computing environment, based on executing the automation script. The semantic model is updated based on the one or more changes in state identified for each automation script.

Abstract: Various embodiments create a cross-configuration software module for cross-configuring software entities. In one embodiment, a first set of requirements and at least a second set of requirements are obtained. Each of the first and second set of requirements identify at least one of a set of software entities and a set of hardware components required to be present on at least one system including software entities to be cross-configured. At least one set of operations is obtained. The set of operations includes at least one executable instruction that configures a first software entity with a second software entity. A first configuration definition is generated including at least the first set of requirements and the at least one set of operations. A second configuration definition is generated including at least the second set of requirements. The first and second configuration definitions are stored within a cross-configuration software module.

Abstract: Various embodiments automatically test software automation scripts. In one embodiment, at least one software automation script is obtained. The software automation script is configured to automatically place a computing system into a target state. A plurality of test cases for the software automation script is executed. Each of the plurality of test cases is a separate instance of the software automation script configured based at least on one or more different states of the computing system. The software automation script is determined to be one of idempotent and non-idempotent and/or one of convergent and non-convergent based on executing the plurality of test cases.