Dell Products Inc.

Guhesh Swaminathan

Ramakanth Kanagovi

Shaul Dar

Rajan Kumar

Saheli Saha

Vamsi K Vankamamidi

Sankunny Jayaprasad

Prerit Jain

Meenakshi Parameswaran

Jason Fay

Srivats Athindran

Araiz Abdul Baqi

Swati Smita Sitha

Sanjib Mallick

Vishnu Murty Karrotu

Sailendu Kumar Patra

Saurabh Jha

Erik Reyes

Virender Sharma

A method, computer program product, and computing system for processing a plurality of input/output (IO) requests associated with a storage object in a storage system. A plurality of IO features are generated using the plurality of IO requests associated with the storage object. A time dependent IO feature is identified from the plurality of IO features. A coefficient for the time dependent IO feature for the storage system is extracted. The time dependent IO feature is calibrated using the coefficient for the time dependent IO feature for the storage system relative to the time dependent IO feature from at least one other storage system.

System and method for calibrating heterogenous workloads in a machine learning model for storage objects

A method, computer program product, and computing system for processing a plurality of historical input/output (IO) requests associated with a plurality of storage objects of a storage system from a plurality of time intervals. The plurality of storage objects may be divided into a plurality of storage activity classes using a classification-based machine learning model and the plurality of historical IO requests. A next access time for each storage object may be forecasted based upon, at least in part, the plurality of storage activity classes.

System and method for machine learning-based data archiving in storage systems

A method, computer program product, and computing system for monitoring for a potential ransomware attack on a storage object of a storage system based upon, at least in part, processing of a plurality of input/output (IO) features associated with the storage object using a machine learning model. A host computing device associated with the storage object under the potential ransomware attack is identified. A process executing on the host computing device associated with the storage object under the potential ransomware attack is identified. A remedial action is performed on the storage system in response to identifying the process executing on the host computing device.

System and method for targeted machine learning-based detection and prevention of ransomware attacks on a storage system

Techniques for performing early and adaptive IO stream sampling for ML-based optimizations in a storage system. The techniques include obtaining a sub-slice of sampled data by performing early sampling of a slice of successive operations directed to a storage object. The techniques include generating features based on the sub-slice, processing the features using an ML model, and generating a probability score based on the ML model's output. The techniques include determining that the probability score falls within an overlap range of continuous variable distributions for benign and threat classes of data. The techniques include, in response to the probability score exceeding a specified threshold, comparing a class signature of the sub-slice with a target class signature of the threat class of data to determine a similarity between the class signatures, and, in response to the similarity exceeding a predetermined similarity level, assigning a 'threat' class label to the probability score.

Early and adaptive stream sampling system and method for machine learning-based optimizations in storage systems

A method for managing an anomaly in a client includes: obtaining, by an analyzer, historical metadata (HM); obtaining, by the analyzer, an error description that is associated with the HM; analyzing, by the analyzer, the HM to generate a first data frame (DF); generating, by the analyzer, a second DF and a third DF based on the first DF, in which the second DF and the third DF are sent to an engine; generating, by the analyzer, a fourth DF based on the first DF and error description, in which the fourth DF is sent to the engine; tuning, by the engine, an anomaly detection model (ADM) to obtain a tuned ADM using: a first target parameter (TP) and the second DF; a second TP and the third DF; a third TP and the fourth DF; and initiating, by the engine, notification of an administrator about the tuned ADM.

Method and system for detecting anomalous sub-sequences in metadata

A method, computer program product, and computing system for processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system. A plurality of IO features may be generated using the plurality of IO requests including one or more of: a percentage of overwrite IO requests, a percentage of sequential read IO requests, and a percentage of sequential write IO requests. The plurality of IO features may be processed using a machine learning model. A ransomware attack may be monitored for on the storage system in real-time based upon, at least in part, the processing of the plurality of IO features using the machine learning model.

Storage and method for machine learning-based detection of ransomware attacks on a storage system

Method and system for detecting anomalous sub- sequences in metadata using rolling windows

A method, computer program product, and computing system for processing a plurality of input/output (IO) requests associated with a plurality of storage objects of a storage system. A plurality of IO features are generated using the plurality of IO requests. The plurality of IO features are processed using a machine learning model. A ransomware attack on the storage system may be monitored for in real-time based upon, at least in part, the processing of the plurality of IO features using the machine learning model.

System and method for machine learning-based detection of ransomware attacks on a storage system

Methods, apparatus, and processor-readable storage media for automated address data determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes extracting address information from one or more documents using at least one artificial intelligence-based address extraction model; parsing, into one or more address components, at least a portion of the extracted address information using at least one artificial intelligence-based parsing model; comparing at least a portion of the one or more parsed address components to stored address information using at least one artificial intelligence-based scoring model; and performing one or more automated actions based at least in part on results from the comparing of at least a portion of the one or more parsed address components to stored address information.

Automated address data determinations using artificial intelligence techniques

A method, computer program product, and computing system for processing a plurality of input/output (IO) operations on a plurality of storage objects of a storage system. The plurality of storage objects may be divided into a plurality of storage object groups based upon, at least in part, the plurality of IO operations processed on the plurality of storage objects. Each storage object group may be associated with an IO machine learning model selected from a plurality of IO machine learning models, thus defining a plurality of storage object group-specific IO machine learning models. IO performance data may be forecasted for the plurality of storage objects using the plurality of storage object group-specific IO machine learning models.

System and method for distributed input/output (IO) performance forecasting across multiple machine learning models

Techniques for performing effective noise removal for biased machine learning (ML) based optimizations in storage systems. The techniques include serving, by a storage system, an IO workload, identifying, using ML from among a plurality of storage objects subject to the IO workload, storage objects with low temperatures (e.g., cold storage objects) or likely to have low temperatures in the near future, and removing them from subsequent temperature forecasting analysis, effectively treating such cold storage objects as 'noise.' The techniques further include performing the temperature forecasting analysis on remaining ones of the plurality of storage objects such as those with high temperatures (e.g., hot storage objects). In this way, temperature forecasting or prediction is performed, using ML, in a biased fashion over a relatively narrow spectrum of storage object temperatures, thereby improving tiering and data prefetching performance, reducing memory and processing overhead, and so on.

Effective noise removal techniques for biased machine learning based optimizations in storage systems

A method, computer program product, and computing system for forecasting a future temperature for a storage object within a multi-tiered cloud storage system. A cost associated with modifying a tiering of the storage object within the multi-tiered cloud storage system is determined based upon, at least in part, the future temperature forecasted for the storage object. The storage object is tiered in the multi-tiered cloud storage system based upon, at least in part, the cost associated with modifying the tiering of the storage object and a tiering policy associated with the multi-tiered cloud storage system.

System and method for machine learning-temperature forecasting for automated tiering within a cloud storage system

A method, computer program product, and computing system for processing a plurality of input/output (IO) requests for a storage object of a storage system. A sampling interval may be determined for the plurality of IO requests for the storage object based upon, at least in part, a machine learning model processing the plurality of IO requests. The plurality of IO requests may be sampled using the determined sampling interval. The plurality of sampled IO requests may be processed using the machine learning model.

System and method for input/output (IO) request stream sampling for machine learning-based optimizations in storage systems

Techniques for forecasting temperatures of storage objects in a storage system using machine learning (ML). The techniques can include forecasting at least one temperature of a storage object using at least one ML model, modifying storage of the storage object based on the at least one temperature of the storage object, and, having modified storage of the storage object, obtaining at least one performance metric associated with the storage object. The techniques can further include, based on the performance metric(s), varying a frequency of forecasting the at least one temperature of the storage object, retraining the at least one ML model used in forecasting the at least one temperature, and/or adjusting at least one operational parameter of the system. The techniques provide increased accuracy over known statistical approaches to forecasting temperatures of storage objects, leading to increased performance gains in terms of IO latency, IO operations per second, and bandwidth.

Systems and methods of forecasting temperatures of storage objects using machine learning

One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.

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Chennai, India

Guhesh Swaminathan