Abstract: A method for managing data includes obtaining a compression algorithm selection request for a data object, wherein the data object is generated by a production host, identifying, in response to the compression algorithm selection request, a set of production host performance objectives of the production host, performing a compression algorithm selection analysis using the set of production host performance objectives and a compression selection model to obtain a compression algorithm selection for a compression algorithm, specifying the compression algorithm to the production host using a data agent, wherein the data agent is operatively connected to the production host, initiating a compression on the data object using the data agent by applying the compression algorithm to obtain a compressed data object, and initiating a storage of the compressed data object.

Abstract: An information handling system for managing a storage system includes storage for storing profile-based cache policy performance prediction models. The information handling system also includes a storage manager that obtains an input-output profile for a workload hosted by the information handling system during a first period of time; obtains performance metrics for cache policies for the storage system using: the input-output profile, and the profile-based cache policy performance prediction models; obtains a ranking of the cache policies based on the performance metrics; selects a cache policy of the cache policies based on the rankings; and updates operation of a cache of the storage system based on the selected cache policy for a second period of time.

Abstract: Techniques are provided for anomaly detection in time-series data using state inference and machine learning. An exemplary method comprises: obtaining detected states of a plurality of data samples in temporal data, wherein each data sample in the temporal data has a corresponding detected state; obtaining a likelihood that each of the data samples belongs to the corresponding detected state; obtaining a distribution of likelihoods of the data samples indicating a number of observations of each of a plurality of likelihood values; training, using a supervised learning technique, an anomaly detection model that, given the distribution of likelihoods and one or more anomaly thresholds, generates a quality score for each of the anomaly thresholds; and selecting at least one anomaly threshold based on the quality score, wherein the trained anomaly detection model is applied to detect anomalies in new temporal data samples using the selected at least one anomaly threshold.

Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.

Abstract: A method for managing storage devices includes obtaining, by a storage device event manager, a set of storage device telemetry snapshots is associated with a set of storage devices, generating a telemetry summary correlation matrix using the set of storage device telemetry snapshots, performing, using the telemetry summary correlation matrix, a classification of each storage device in the set of storage devices to obtain a set of classification tags using a first portion of a set of features, obtaining a set of normality states for the set of storage devices using the set of classification tags and a second portion of the set of features, updating an event anomaly policy based on the set of normality states, and performing a remediation action on a storage device in the set of storage devices based on the event anomaly policy.

Abstract: A method for managing data includes obtaining a compression algorithm selection request for a data object, wherein the data object is generated by a production host, identifying, in response to the compression algorithm selection request, a set of production host performance objectives of the production host, performing a compression algorithm selection analysis using the set of production host performance objectives and a compression selection model to obtain a compression algorithm selection for a compression algorithm, specifying the compression algorithm to the production host using a data agent, wherein the data agent is operatively connected to the production host, initiating a compression on the data object using the data agent by applying the compression algorithm to obtain a compressed data object, and initiating a storage of the compressed data object.

Abstract: A method for characterizing workload sequentiality for cache policy optimization includes maintaining an IO trace data structure having a rolling window of IO traces describing access operations on addresses of a storage volume. A page count data structure is maintained that includes a list of all of the addresses of the storage volume referenced by the IO traces in the IO trace data structure. A list of sequences data structure is maintained that contains a list of all sequences of the addresses of the storage volume that were accessed by the IO traces in the IO trace data structure. A sequence lengths data structure is used to correlate each sequence in the list of sequences data structure with a length of the sequence, and a histogram data structure is used to correlate sequence lengths and a number of how many of sequences of each length are maintained in the sequence lengths data structure.

Abstract: A cache management system includes a sequentiality determination process configured to determine sequentiality profiles of a workload of IO traces as the workload dynamically changes over time. A learning process is trained to learn a correlation between workload sequentiality and cache pollution, and the trained learning process is used to predict cache pollution before the cache starts to experience symptoms of excessive pollution. The predicted pollution value is used by a cache policy adjustment process to change the prefetch policy applied to the cache, to proactively control the manner in which prefetching is used to write data to the cache. Selection of the cache policy is implemented on a per-LUN basis, so that cache performance for each LUN is individually managed by the cache management system.

Abstract: Techniques are provided for characterizing and quantifying a sequentiality of workloads using sequentiality profiles and signatures. One exemplary method comprises obtaining telemetry data for an input/output workload; evaluating a distribution over time of sequence lengths for input/output requests in the telemetry data by the input/output workload; and generating a sequentiality profile for the input/output workload to characterize the input/output workload based at least in part on the distribution over time of the sequence lengths. Multiple sequentiality profiles for one or more input/output workloads may be clustered into a plurality of clusters. A sequentiality signature may be generated to represent one or more sequentiality profiles within a given cluster. A performance of data movement policies may be evaluated with respect to the sequentiality signature of the given cluster.

Abstract: Facilitating temporal data management for anomalous state detection in data centers is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise performing a process of extraction, transformation, and loading of data from log files into a telemetry data store. The data can be loaded into the telemetry data store as telemetry data. The operations also can comprise dividing the telemetry data into first telemetry data and second telemetry data. The first telemetry data can comprise telemetry data that does not satisfy a defined quality level. The second telemetry data can comprise telemetry data that satisfies the defined quality level. Further, the operations can comprise removing the first telemetry data from the telemetry data store and outputting the second telemetry data based on a request for the second telemetry data.

Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.

Abstract: A cache management system includes a sequentiality determination process configured to determine sequentiality profiles of a workload of IO traces as the workload dynamically changes over time. A learning process is trained to learn a correlation between workload sequentiality and cache pollution, and the trained learning process is used to predict cache pollution before the cache starts to experience symptoms of excessive pollution. The predicted pollution value is used by a cache policy adjustment process to change the prefetch policy applied to the cache, to proactively control the manner in which prefetching is used to write data to the cache. Selection of the cache policy is implemented on a per-LUN basis, so that cache performance for each LUN is individually managed by the cache management system.

Abstract: A method for characterizing workload sequentiality for cache policy optimization includes maintaining an IO trace data structure having a rolling window of IO traces describing access operations on addresses of a storage volume. A page count data structure is maintained that includes a list of all of the addresses of the storage volume referenced by the IO traces in the IO trace data structure. A list of sequences data structure is maintained that contains a list of all sequences of the addresses of the storage volume that were accessed by the IO traces in the IO trace data structure. A sequence lengths data structure is used to correlate each sequence in the list of sequences data structure with a length of the sequence, and a histogram data structure is used to correlate sequence lengths and a number of how many of sequences of each length are maintained in the sequence lengths data structure.

Abstract: Techniques are provided for characterizing and quantifying a sequentiality of workloads using sequentiality profiles and signatures. One exemplary method comprises obtaining telemetry data for an input/output workload; evaluating a distribution over time of sequence lengths for input/output requests in the telemetry data by the input/output workload; and generating a sequentiality profile for the input/output workload to characterize the input/output workload based at least in part on the distribution over time of the sequence lengths. Multiple sequentiality profiles for one or more input/output workloads may be clustered into a plurality of clusters. A sequentiality signature may be generated to represent one or more sequentiality profiles within a given cluster. A performance of data movement policies may be evaluated with respect to the sequentiality signature of the given cluster.

Abstract: Methods and apparatus are provided for taking snapshots to train prediction models and improve workflow execution.

Abstract: Techniques are provided for adaptive look-ahead configuration for data prefetching based on request size and frequency. One method comprises performing the following steps: estimating an earning value for a particular portion based on an average size and frequency of past input/output requests for the particular portion; calculating a quota for the particular portion by normalizing the earning value for the particular portion of the storage system based on earning values of one or more additional portions of the storage system; obtaining a size of a look-ahead window for a new request based on the quota for the particular portion over a prefetch budget assigned to the storage system; and moving a requested data item and one or more additional data items within the look-ahead window from the storage system to the cache memory responsive to the requested data item and/or the additional data items within the look-ahead window not being in the cache memory.

Abstract: A data processing device includes persistent storage, a cache for the persistent storage, and a cache manager. The persistent storage is divided into logical units. The cache manager obtains persistent storage use data; selects model parameters for a cache prediction model based on the persistent storage use data; trains the cache prediction model based on the persistent storage use data using the selected model parameters to obtain a trained cache prediction model; and manages the cache based on logical units of the persistent storage using the trained cache prediction model.

Abstract: Techniques are provided for adaptive look-ahead configuration for data prefetching based on request size and frequency. One method comprises performing the following steps: estimating an earning value for a particular portion based on an average size and frequency of past input/output requests for the particular portion; calculating a quota for the particular portion by normalizing the earning value for the particular portion of the storage system based on earning values of one or more additional portions of the storage system; obtaining a size of a look-ahead window for a new request based on the quota for the particular portion over a prefetch budget assigned to the storage system; and moving a requested data item and one or more additional data items within the look-ahead window from the storage system to the cache memory responsive to one the requested data item and/or the additional data items within the look-ahead window not being in the cache memory.

Abstract: Techniques are provided for adaptive look-ahead configuration for data prefetching. One method comprises, in response to a request for a data item in a storage system: obtaining a size of a look-ahead window for the request based on one of multiple available caching policies; and moving the requested data item and additional data items within the look-ahead window to the cache memory when the requested data item and/or the additional data items within the look-ahead window account recovery are not in the cache memory. The multiple available caching policies comprise a caching policy based on characteristics of an input/output workload of the storage system, or a portion thereof; and/or a caching policy based on an input/output workload of at least a portion of the storage system within a prior predefined time window. The look-ahead window size may be varied over time.

Abstract: Methods and apparatus are provided for data operation pre-processing with a probabilistic estimation of operation value. An exemplary method comprises extracting feature values from a data set; identifying a set of operations that previously processed data sets comprising the extracted feature values; determining whether to execute an operation from the set of operations before an explicit request for the execution of the operation based on a probabilistic evaluation of a value of pre-processing the operation; and executing a set of instructions for the operation when it is determined that the operation is to be executed. The set of instructions comprises, e.g., stopping execution of other operations being executed, freeing resources required by the operation, and/or allocating resources required by the operation.