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 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: 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: 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 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.