Dell Products Inc.

Rajan Kumar

Shaul Dar

Ramakanth Kanagovi

Guhesh Swaminathan

Sanjib Mallick

Vishnu Murty Karrotu

A method, computer program product, and computing system for identifying a plurality of headings from a document by processing a hierarchical structure associated with the document including the plurality of headings and a plurality of content portions within the plurality of headings. A plurality of respective chunks are generated using the plurality of headings and a prompt size limitation associated with a prompt of a generative artificial intelligence (AI) model. The plurality of respective chunks are provided for generating a prompt for the generative AI model.

System and method for generating optimized chunks for retrieval augmented generation using document hierarchy

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, 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

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

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.

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Nawada, India

Rajan Kumar