Abstract: Methods, apparatus, and processor-readable storage media for machine learning-based anomaly detection using time series decomposition are provided herein. An example computer-implemented method includes processing, via machine learning techniques pertaining to time series decomposition functions, a first set of historical time series data derived from multiple systems within an enterprise; generating, based on the processed data, one or more pairs of upper bounds and lower bounds directed to system metrics; identifying system anomalies attributed to one or more of the multiple systems within the enterprise by comparing a second set of historical time series data derived from the one or more systems against the one or more pairs of upper bounds and lower bounds; prioritizing, via machine learning techniques pertaining to weighting functions, the system anomalies; and outputting, in accordance with the prioritization, the system anomalies to a user within the enterprise.

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: A method, computer program product, and computer system for identifying, by a computing device, historical data. Usage data may be forecasted for at least two frequencies of a plurality of frequencies associated with the historical data based upon, at least in part, the historical data. It may be determined that the usage data is forecasted more accurately for a first frequency of the at least two frequencies than for a second frequency of the at least two frequencies. The usage data forecasted for the first frequency of the at least two frequencies may be selected based upon, at least in part, determining that the usage data is forecasted more accurately for the first frequency. Future usage of an item may be predicted based upon, at least in part, the usage data forecasted for the first frequency.

Abstract: A method includes obtaining, in a given log processing node, at least two different types of logs associated with assets of an enterprise system. The method also includes generating, at the given log processing node, frequency scores for terms in unstructured log data of each of the different log types, the generated frequency score for a given term in unstructured log data of a given log type being based on (i) occurrence of the given term in historical logs of the given log type previously processed by log processing nodes and (ii) occurrence of the given term in the obtained logs of the given log type. The method further includes extracting, at the given log processing node, features from the obtained logs based on the frequency scores, detecting events affecting the assets utilizing the extracted features, and modifying a configuration of the assets responsive to detecting the events.

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 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 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: A method includes obtaining, in a given log processing node, at least two different types of logs associated with assets of an enterprise system. The method also includes generating, at the given log processing node, frequency scores for terms in unstructured log data of each of the different log types, the generated frequency score for a given term in unstructured log data of a given log type being based on (i) occurrence of the given term in historical logs of the given log type previously processed by log processing nodes and (ii) occurrence of the given term in the obtained logs of the given log type. The method further includes extracting, at the given log processing node, features from the obtained logs based on the frequency scores, detecting events affecting the assets utilizing the extracted features, and modifying a configuration of the assets responsive to detecting the events.

Abstract: A method includes obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems, and generating a plurality of model-specific storage resource capacity predictions utilizing the historical storage resource utilization data and respective ones of a plurality of time series capacity prediction forecasting models. The method also includes selecting a subset of the model-specific storage resource capacity predictions having one or more designated characteristics, determining an overall storage resource capacity prediction based at least in part on a combination of the selected subset of the model-specific storage resource capacity predictions, and modifying a provisioning of storage resources of the one or more storage systems based at least in part on the overall storage resource capacity prediction.

Abstract: Methods, apparatus, and processor-readable storage media for machine learning-based anomaly detection using time series decomposition are provided herein. An example computer-implemented method includes processing, via machine learning techniques pertaining to time series decomposition functions, a first set of historical time series data derived from multiple systems within an enterprise; generating, based on the processed data, one or more pairs of upper bounds and lower bounds directed to system metrics; identifying system anomalies attributed to one or more of the multiple systems within the enterprise by comparing a second set of historical time series data derived from the one or more systems against the one or more pairs of upper bounds and lower bounds; prioritizing, via machine learning techniques pertaining to weighting functions, the system anomalies; and outputting, in accordance with the prioritization, the system anomalies to a user within the enterprise.

Abstract: A method includes obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems, and generating a plurality of model-specific storage resource capacity predictions utilizing the historical storage resource utilization data and respective ones of a plurality of time series capacity prediction forecasting models. The method also includes selecting a subset of the model-specific storage resource capacity predictions having one or more designated characteristics, determining an overall storage resource capacity prediction based at least in part on a combination of the selected subset of the model-specific storage resource capacity predictions, and modifying a provisioning of storage resources of the one or more storage systems based at least in part on the overall storage resource capacity prediction.

Abstract: A method includes monitoring a given workload running on one or more storage systems to obtain performance data, detecting a given potential performance-impacting event affecting the given workload based at least in part on a given portion of the obtained performance data, and generating a visualization of at least the given portion of the obtained performance data. The method also includes providing the generated visualization as input to a machine learning algorithm, utilizing the machine learning algorithm to classify the given potential performance-impacting event as one of (i) a true positive event affecting performance of the given workload and (ii) a false positive event corresponding to one or more changes in storage resource utilization by the given workload, and modifying provisioning of storage resources of the one or more storage systems responsive to classifying the given potential performance-impacting event as a true positive event affecting performance of the given workload.

Abstract: A method, computer program product, and computer system for identifying, by a computing device, historical data. Usage data may be forecasted for at least two frequencies of a plurality of frequencies associated with the historical data based upon, at least in part, the historical data. It may be determined that the usage data is forecasted more accurately for a first frequency of the at least two frequencies than for a second frequency of the at least two frequencies. The usage data forecasted for the first frequency of the at least two frequencies may be selected based upon, at least in part, determining that the usage data is forecasted more accurately for the first frequency. Future usage of an item may be predicted based upon, at least in part, the usage data forecasted for the first frequency.