Abstract: In an approach to optimizing dynamic system reconfiguration, a computer receives an active system configuration and a target system configuration from a system administrator, where the target system configuration includes two or more logical partitions. A computer determines one or more reconfiguration actions required to transform the active system configuration to the target system configuration. A computer generates a dependency graph based on the determined reconfiguration actions. A computer divides the dependency graph along the two or more logical partitions. A computer sorts the determined reconfiguration actions by associated dependencies. A computer orders the determined reconfiguration actions based on a priority of each of the two or more logical partitions. A computer runs a first simulation of the determined reconfiguration actions for each of the two or more logical partitions. A computer performs the determined reconfiguration actions for each of the two or more logical partitions.

Abstract: In an approach to optimizing dynamic system reconfiguration, a computer receives an active system configuration and a target system configuration from a system administrator, where the target system configuration includes two or more logical partitions. A computer determines one or more reconfiguration actions required to transform the active system configuration to the target system configuration. A computer generates a dependency graph based on the determined reconfiguration actions. A computer divides the dependency graph along the two or more logical partitions. A computer sorts the determined reconfiguration actions by associated dependencies. A computer orders the determined reconfiguration actions based on a priority of each of the two or more logical partitions. A computer runs a first simulation of the determined reconfiguration actions for each of the two or more logical partitions. A computer performs the determined reconfiguration actions for each of the two or more logical partitions.

Abstract: Aspects of the invention include determining, by a machine learning model, a predicted workload for a system and a current system state of the system, determining an action to be enacted for the system based at least in part on the predicted workload and the current system state, enacting the action for the system, evaluating a state of the system after the action has been enacted, determining a reward for the machine learning model based at least in part on the state of the system after the action has been enacted, and updating the machine learning model based on the reward.

Abstract: Aspects of the invention include determining, by a machine learning model, a predicted workload for a system and a current system state of the system, determining an action to be enacted for the system based at least in part on the predicted workload and the current system state, enacting the action for the system, evaluating a state of the system after the action has been enacted, determining a reward for the machine learning model based at least in part on the state of the system after the action has been enacted, and updating the machine learning model based on the reward.