Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

Abstract: A system may receive a directed acyclic graphic (DAG) for an application. The system may profile the DAG with a plurality of computer resource allocations to generate an end-to-end (E2E) latency model. The system may generate, based on the E2E latency model, an execution plan comprising optimized computer resource allocations and timing information. The system may cause a computing infrastructure to execute a function in a first stage of the DAG model on a first virtual machine right sized according to the execution plan. The system may cause the computing infrastructure to initialize, at a time determined specified by the execution plan, a second virtual machine for a function in a second stage in the DAG model. The system may cause the FaaS infrastructure to execute the function in the second stage on the second virtual machine after completion of the function in the first stage.

Abstract: A system may receive a first directed acyclic graphic (DAG) for an application. The system may model performance of each function in the DAG to generate a performance model. The system may generate a plurality of variant DAGs. For each of the variant DAGs, the system may obtain a configuration vector and forecast, based on the performance model and the configuration vector, a plurality of end-to-end latency distributions for the variant DAGS. The system may select the variant DAG and configuration vector based on a selection criteria. The system may cause an application to be executed according to the variant DAG and configuration vector.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine performance that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. The system may select a reconfiguration plan in response to the performance satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine performance that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. The system may select a reconfiguration plan in response to the performance satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine a performance gain that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. In addition, the system may determine a performance loss that would result from the respective databases of the nodes being inaccessible during reconfiguration with the configuration parameter sets. The system may select a reconfiguration plan in response to the performance gain and the performance loss satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine a performance gain that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. In addition, the system may determine a performance loss that would result from the respective databases of the nodes being inaccessible during reconfiguration with the configuration parameter sets. The system may select a reconfiguration plan in response to the performance gain and the performance loss satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.