A method, a system, and a computer program product for placement or storage of data structures in memory/storage locations. A type of a data structure for storing data and a type of data access to the data structure are determined. The type of data access includes a first and a second type of data access. A frequency of each type of access to each type of data structure accessed by a query is determined. Using the determined frequency, a number of first type of data accesses to the data structure is compared to a number of second type of accesses to the data structure. The numbers of first and second types of data access are compared to a predetermined threshold percentage of a total number of data accesses to the data structure. Based on the comparisons, a physical memory location for storing data is determined.

Techniques regarding generating an ensemble of quantum kernel-based learners for one or more quantum machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that can generate an ensemble of quantum kernel-based learners by selecting a quantum kernel at multiple iterations of a boosting procedure that analyzes a range of feature maps employable by a quantum machine learning model.

In an approach to improve multi-data center environments by minimizing the environmental impact of workloads in multi-data center environments embodiments migrate at least a portion of one or more workloads between one or more data centers automatically to maximize a usage of renewable energy based on a predetermined threshold score of input power and a combination of renewal energy sources. Further, embodiments dictate, by a policy engine, where at least a portion of the one or more workloads can be hosted. Additionally, embodiments control, by a scheduling engine, how, when, and where at least a portion of the one or more workloads will migrate, and perform data replication to migrate data between a plurality of data center locations.

A capacity planning method for Always On Availability Group, AG, cluster renewal includes selecting a source AG cluster to be replaced with a target AG cluster, selecting at least one performance monitor and monitoring performance of instances and databases to obtain time series. Trends of the time series are defined and at least one benchmark value is obtained for source and target nodes and calculating at least one benchmark ratio. The time series are adjusted based on the defined trends and the at least one benchmark ratio. A logical grouping of instances and databases is constituted, and workloads of the logical groups are calculated for each node on basis of the adjusted time series. A required capacity of the target AG cluster nodes is predicted. Finally, the required capacity of the target AG cluster nodes is compared to verify, whether the target node has sufficient capacity.

Monitoring and enhancing performance of containers using a calibration technique is implemented using a computer. Performance of a new container as part of an application running on the computer is checked by comparing a current performance of the new container with baseline data corresponding to the new container. The baseline data is derived from a calibration container corresponding to the new container. The new container is categorized in a category of performance based on the checking of the performance of the new container. An alert can be sent to a device of an administrator regarding the new container meeting a threshold of performance, in response to the new container meeting the threshold of performance. The alert can be sent to the device of the administrator for the administrator to initiate an action pertaining to the new container in response to receiving the alert.

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating and searching reinforcement learning algorithms. In some implementations, a computer-implemented system generates a sequence of candidate reinforcement learning algorithms. Each candidate reinforcement learning algorithm in the sequence is configured to receive an input environment state characterizing a state of an environment and to generate an output that specifies an action to be performed by an agent interacting with the environment. For each candidate reinforcement learning algorithm in the sequence, the system performs a performance evaluation for a set of a plurality of training environments. For each training environment, the system adjusts a set of environment-specific parameters of the candidate reinforcement learning algorithm by performing training of the candidate reinforcement learning algorithm to control a corresponding agent in the training environment. The system generates an environment-specific performance metric for the candidate reinforcement learning algorithm that measures a performance of the candidate reinforcement learning algorithm in controlling the corresponding agent in the training environment as a result of the training. After performing training in the set of training environments, the system generates a summary performance metric for the candidate reinforcement learning algorithm by combining the environment-specific performance metrics generated for the set of training environments. After evaluating each of the candidate reinforcement learning algorithms in the sequence, the system selects one or more output reinforcement learning algorithms from the sequence based on the summary performance metrics of the candidate reinforcement learning algorithms.

An approach is provided in which the approach computes a set of thresholds corresponding to a set of decision performances relative to a set of classifier confidence scores. The set of decision performances include a set of user decision performances, a set of classifier decision performances, and a set of augmented decision performances. The approach selects one of the collaboration levels based on comparing the set of thresholds to a new confidence score of a new decision. The approach collaborates with a user at the selected collaboration level to generate a final decision.

Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including selecting modeling logic for an artificial intelligence (AI) model that solves a use case of a plurality of use cases; executing the AI model using holdout data to obtain a sub-result; evaluating the sub-result based on an evaluation metric; and combining the sub-result with other sub-results of the plurality of use cases to determine whether an exit criteria has been met. Other embodiments are disclosed.

A system, method and non-transitory computer readable media for effectuating ML-based fault analysis in a network (102A, 102B) comprising a plurality of nodes (104-N, 120-M). An example method (200A) comprises determining (202) that at least one of a topological configuration of the network (102 A, 102B) and one or more key performance indicator (KPI) requirements associated with the network (102A, 102B) have changed; and responsive to the determining, selecting (204) a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network (102 A, 102B) after the topological configuration or any KPI requirements of the network (102A, 102B) have changed.

Aspects relate to system and methods for determining a user specific mission operational performance, using machine-learning processes. An exemplary system includes a computing device configured to perform operations including receiving user-input structured data from at least a user device, receiving observed structured data related to the user and a mission performance metric, inputting the user-input structured data and the observed structured data to a machine-learning model, generating a user performance metric as a function of the machine-learning model, receiving a deterministic mission operational performance metric, disaggregating a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric, inputting training data to a machine-learning algorithm, where the training data includes the user-input structured data and the observed structured data correlated to the deterministic user performance metric, and training the machine-learning model as a function of the machine-learning algorithm and the training data.