Abstract: Techniques for configuring the operating system (OS) of a computing device are disclosed. An example method includes receiving a plurality of machine specifications and a request for an OS configuration from a computing device. The method also includes identifying, by a processing device, a selected machine group from among a plurality of machine groups based on a comparison of the plurality of machine specifications with a set of principal machine specifications associated with each of the plurality of machine groups. The method also includes obtaining a pre-tuned OS configuration assigned to the selected machine group and sending the pre-tuned OS configuration to the computing device, wherein an operating system of the computing device is configured in accordance with the pre-tuned OS configuration.

Abstract: A computer-implemented method, system, and computer program product for estimating a carbon footprint of an incoming workload to be hosted on a cloud data center. Trained first and second machine learning models are used in combination to estimate the energy consumption for the incoming workload to be hosted on the cloud data center based on the active energy consumption and the idle energy consumption predicted by the trained first and second machine learning models. Upon estimating the energy consumption for the incoming workload, the carbon footprint for the incoming workload is estimated based on the estimated energy consumption for the incoming workload as well as the power usage effectiveness of the incoming workload and the carbon intensity of the incoming workload. In this manner, carbon emissions attributable to workloads to be deployed to a data center (e.g., cloud data center) prior to deployment may be estimated.

Abstract: A computer-implemented method, a computer system and a computer program product for boosting 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: Computer-implemented methods for deploying a workload in a cloud computing system are provided. Aspects include identifying resource utilization levels for processors and memory of each of the plurality of compute nodes, calculating, for each nodes, an idle power level, an activation power level, and a dynamic power level, and identifying characteristics of the workload to be deployed. Aspects also include identifying a plurality of locations that are suitable for deployment of the workload, wherein each of the plurality of locations is one of the plurality of compute nodes, calculating, for each of the plurality of locations based on a simulated deployment of the workload at a corresponding location, an estimated power consumption of the Cloud computing system, and deploying the workload on a first compute node, where the first compute node corresponds to a location associated with a lowest estimated power consumption of the Cloud computing system.

Abstract: Computer-implemented methods for estimating the energy consumption of a workload in a Cloud computing system are provided. Aspects include receiving a request for an estimated energy consumption of the workload and obtaining characteristics of the Cloud computing system executing the workload. Aspects also include identifying and employing a unified power consumption model from a power model database based on the characteristics and calculating the estimated energy consumption of the workload based on the unified power consumption model.

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: An embodiment for improved methods for energy efficient scaling of multi-zone container clusters is provided. The embodiment may establish a connection between an upper layer container orchestration controller associated with multiple container cluster zones and lower layer resource manager controllers corresponding to multiple datacenters. The embodiment may determine additional workers are needed to perform a task and request worker offers from the lower layer resource manager controllers. The embodiment may receive the worker offers including worker profile data at the upper layer container orchestration controller.

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.