Abstract: A computer-implemented method, a computer system and a computer program product boost efficiency through energy-aware workload placement. The method includes obtaining an energy profile for a plurality of computer servers and power consumption data for each computer server in the plurality of computer servers. The method also includes determining an optimal temperature for each computer server in the plurality of computer servers based on the energy profile. The method further includes determining a target processor utilization for each computer server in the plurality of computer servers based on the optimal temperature. In addition, the method includes calculating an efficiency rank for each computer server in the plurality of computer servers based on the target processor utilization and the power consumption data. Lastly, the method includes deploying a workload on a computer server with a highest efficiency rank.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate generating and applying meta-policies for application deployment environments are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a state analyzer that can analyze a first application deployment environment to identify a first configuration of the first application deployment environment. The computer executable components can further comprise a policy generator that generates a meta-policy based on the identified first configuration.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate fault localization and alert aggregation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a graph component that employs an algorithm to generate a directed graph of computing elements having performance alerts in one or more abstraction layers of a computing environment. The computer executable components can further comprise a fault localization component that employs a topological sort algorithm to identify one or more of the computing elements causing the performance alerts based on the directed graph.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate generating and applying meta-policies for application deployment environments are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a state analyzer that can analyze a first application deployment environment to identify a first configuration of the first application deployment environment. The computer executable components can further comprise a policy generator that generates a meta-policy based on the identified first configuration.

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: 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: Deployment pattern matching is implemented by accessing a target computing environment model that captures environment modeling parameters relating to resources and resource-resource relationships of a corresponding computing environment and expressing the target computing environment model as a model graph defined by target resource elements and resource-to-resource relationship links. Deployment pattern matching is further implemented by accessing a realization pattern that captures deployment parameters relating to resources and resource-resource relationships of a deployment of interest and expressing the realization pattern as a pattern graph defined by conceptual resource elements and constraints arranged by resource-to-resource relationship links and constraint links.

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 monitor a distributed software system. In one embodiment, at least one monitoring policy associated with a distributed software system is selected. A policy type associated with the monitoring policy is identified. An installer is selected based on the policy type associated with the monitoring policy. Monitoring software is installed in a computing environment utilizing the installer. The monitoring software is configured to monitor the distributed software system based on the monitoring policy.

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.

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.