Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset telemetry information; processing the data center asset telemetry information to provide a performance indicator metric; training a resource utilization machine learning model using the performance indicator metric; and, using the resource utilization machine learning model to provide a data center asset resource utilization result.

Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset telemetry information; processing the data center asset telemetry information to provide a performance indicator metric; training a resource utilization machine learning model using the performance indicator metric; and, using the resource utilization machine learning model to provide a data center asset resource utilization result.

Abstract: A system, method, and computer-readable medium for performing a data center monitoring and management operation, The data center monitoring and management operation includes receiving data center data for a plurality of data center assets associated with data center environment having a plurality of data centers; identifying workload data center data contained within the data center data; identifying a data center utilization pattern of the workload data center data; and, generating a data center configuration recommendation, the data center configuration recommendation being based upon the data center utilization pattern of the workload data center data, the data center configuration recommendation comprising a distributed data center configuration recommendation.

Abstract: Methods, systems, and devices for providing computer implemented services are disclosed. To provide the computer implemented services, inference models may generate and provide inferences used in the computer implemented services. The inference models may be obtained through training using training data. Training processes used to train the inference models may proactively to attempt to reduce the likelihood of the trained inference models exhibiting latent bias. The training process may disincentivize predictive power with respect to bias features and incentivize predictive power for labels through use of debiasing terms.

Abstract: Methods, systems, and devices for providing computer-implemented services are disclosed. To provide the computer-implemented services, inference models used by data processing systems may be managed to reduce the likelihood of the inference models providing inferences indicative of bias features. The inference models may be managed by identifying input features in the training data set used to train the inference models with a high contribution to latent bias exhibited by the inference models. The input features may be removed from the training data set and a new inference model may be trained using the remainder of the input features. The new inference model may replace the inference model and, therefore, the inferences provided by the new inference model may be less likely to include latent bias thereby reducing bias in computer-implemented services provided using the inferences.

Abstract: Methods, systems, and devices for providing computer-implemented services are disclosed. To provide the computer-implemented services, inference models used by data processing systems may be managed to reduce the likelihood of the inference models provide inferences indicative of bias features. The inference models may be managed using a divisional process to obtain multipath inference models, as part of a modified split training to reduce mutual information shared with the bias feature. The inferences provided by the inference models may be less likely to include latent bias thereby reducing bias in computer-implemented services provided using the inferences.

Abstract: Methods, systems, and devices for providing computer-implemented services are disclosed. To provide the computer-implemented services, inference models used by data processing systems may be managed to reduce the likelihood of the inference models providing inferences indicative of bias features. The inference models may be managed using modified split training. The inferences provided by the inference models may be less likely to exhibit latent bias thereby reducing bias in computer-implemented services provided using the inferences. The latent bias may be managed granularly for different bias features to manage predictive power levels for features and bias features.

Abstract: Methods, systems, and devices for providing computer-implemented services are disclosed. To provide the computer-implemented services, inference models used by data processing systems may be managed to reduce latent bias. The inference models may be managed by establishing supervised models based on the results of unsupervised learning. The supervised models may then be subjected to training to reduce the levels of latent bias, and analysis to identify features contributing to the latent bias. The supervised learning may then be performed without consideration for the identified features.

Abstract: Methods, systems, and devices for providing computer implemented services are disclosed. To provide the computer implemented services, inference models may generate and provide inferences used in the computer implemented services. The inference models may be obtained through training using training data. Training processes used to train the inference models may proactively attempt to reduce the likelihood of the trained inference models exhibiting latent bias. The training process may disincentivize predictive power with respect to bias features and incentivize predictive power for labels.

Abstract: Methods, systems, and devices for providing computer-implemented services are disclosed. To provide the computer-implemented services, inference models used by data processing systems may be managed to reduce the likelihood of the inference models provide inferences indicative of bias features. The inference models may be managed using modified split training. The inferences provided by the inference models may be less likely to include latent bias thereby reducing bias in computer-implemented services provided using the inferences.

Abstract: Methods, apparatus, and processor-readable storage media for determining similarity between time series using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of multiple candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each of the candidate time series; for each of the similarity measurements, assigning weights to the candidate time series based on similarity to the primary time series relative to the other candidate time series; generating, for each of the candidate time series, a similarity score based on the weights assigned to each of the candidate time series across the similarity measurements; and outputting, based on the similarity scores, identification of at least one candidate time series for use in one or more automated actions relating to at least one system.

Abstract: Recommending configuration changes may include: receiving a decision tree comprising levels of nodes, wherein the decision tree includes leaf nodes each representing a different one of a plurality of hardware configurations, wherein a first leaf represents a first hardware configuration and the first leaf node is associated with a set of I/O workload features denoting a I/O workload of a first system having the first hardware configuration, wherein the set of I/O workload features is associated with an action from the first leaf node to a second leaf node, wherein the second leaf node represents a second hardware configuration and the action represents a hardware configuration change made to transition from the first to the second hardware configuration; and performing processing that determines, using the decision tree, a recommendation for a hardware configuration change for a second system having the first hardware configuration represented by the first leaf node.

Abstract: Recommending configuration changes may include: receiving a decision tree comprising levels of nodes, wherein the decision tree includes leaf nodes each representing a different one of a plurality of hardware configurations, wherein a first leaf represents a first hardware configuration and the first leaf node is associated with a set of I/O workload features denoting a I/O workload of a first system having the first hardware configuration, wherein the set of I/O workload features is associated with an action from the first leaf node to a second leaf node, wherein the second leaf node represents a second hardware configuration and the action represents a hardware configuration change made to transition from the first to the second hardware configuration; and performing processing that determines, using the decision tree, a recommendation for a hardware configuration change for a second system having the first hardware configuration represented by the first leaf node.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of resources in contention in storage systems using machine learning techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to behavioral changes in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series in the set; for each similarity measurement, assigning weights to the candidate time series based on similarity values; generating, for each candidate time series, a similarity score based on the assigned weights; automatically identifying, based on the similarity scores, a candidate time series as contributing to an anomaly exhibited in the primary time series; and outputting identifying information of the at least one identified candidate time series for use in one or more automated actions associated with the storage system.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to system performance degradation are provided herein. An example computer-implemented method includes obtaining, in connection with a system exhibiting performance degradation, a primary time series and a set of multiple candidate time series; calculating, using machine learning, similarity measurements between the primary time series and each time series in the set; for each measurement, assigning weights to the time series based on similarity to the primary time series relative to the other time series in the set; generating, for each time series in the set, a similarity score based on the weights assigned across the similarity measurements; and outputting, based on the similarity scores, identification of a candidate time series for use in automated actions.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of resources in contention in storage systems using machine learning techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to system performance degradation are provided herein. An example computer-implemented method includes obtaining, in connection with a system exhibiting performance degradation, a primary time series and a set of multiple candidate time series; calculating, using machine learning, similarity measurements between the primary time series and each time series in the set; for each measurement, assigning weights to the time series based on similarity to the primary time series relative to the other time series in the set; generating, for each time series in the set, a similarity score based on the weights assigned across the similarity measurements; and outputting, based on the similarity scores, identification of a candidate time series for use in automated actions.

Abstract: Methods, apparatus, and processor-readable storage media for determining similarity between time series using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of multiple candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each of the candidate time series; for each of the similarity measurements, assigning weights to the candidate time series based on similarity to the primary time series relative to the other candidate time series; generating, for each of the candidate time series, a similarity score based on the weights assigned to each of the candidate time series across the similarity measurements; and outputting, based on the similarity scores, identification of at least one candidate time series for use in one or more automated actions relating to at least one system.

Abstract: Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to behavioral changes in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series in the set; for each similarity measurement, assigning weights to the candidate time series based on similarity values; generating, for each candidate time series, a similarity score based on the assigned weights; automatically identifying, based on the similarity scores, a candidate time series as contributing to an anomaly exhibited in the primary time series; and outputting identifying information of the at least one identified candidate time series for use in one or more automated actions associated with the storage system.