Emc IP Holding Company LLC

Dell Products Inc.

Rahul Deo Vishwakarma

Parmeshwr Prasad

Bing Liu

Parminder Singh Sethi

Shelesh Chopra

Sharath Talkad Srinivasan

Hemant Ramesh Gaikwad

Ashish Singh

Vasanth Sathyanarayanan

Suraj D Vithalkar

Techniques for intelligently routing IO to a storage class memory (SCM) namespace are disclosed. A configuration for a namespace is determined, where the configuration indicates a type of IO that the namespace is structured to handle. Details about the configuration of the namespace are stored in a repository. A forwarding rule is generated based on the namespace's stored configuration. When incoming IO having attributes similar to that type is received, implementation of the forwarding rule causes the incoming IO to be directed to the namespace. Attributes of a particular incoming IO are determined. As a result of the attributes satisfying a similarity threshold relative to the type, the forwarding rule is implemented such that the particular incoming IO is directed to the namespace.

Forwarding incoming IO to SCM namespaces

One example method includes accessing a dataset, selecting a list of attributes of the dataset, each of the attributes being selected based on a determination that the attribute is affecting growth of the dataset and affecting an amount of data storage space consumed by the dataset, assigning a SHAP score to each attribute, using the SHAP scores to assign respective weights to each attribute, deriving drift and shock information for the dataset, and based on the drift and shock information, calculating a risk score that a storage capacity of an asset where the dataset is stored will be exhausted within a particular time interval.

Stochastic risk scoring with counterfactual analysis for storage capacity

One example method includes collecting respective data for each datastore in a group of datastores, clustering the data, based on the clustering, applying one of two different mutually exclusive labels to each of the datastores, applying an uncertainty quantifier to determine, for each datastore, a confidence level that the label assigned to that datastore is the correct label, ranking the datastores according to their respective label and confidence level, and presenting, to a user, a list that includes at least a datastore with a highest confidence level.

Smart datastore selection for protection engines based on uncertainty quantification

One example method includes analyzing an IO pattern comprising IOs issued by a client application, based on the analyzing, generating a rule relating to an NFIT, using the rule to modify the NFIT, and after the NFIT is modified, changing a configuration of SCM firmware based on the NFIT. Because the modified SCM firmware may have a configuration directly related to the IO pattern, the SCM may function relatively more efficiently and effectively for that IO pattern.

Based on IO pattern influence NFIT table for persistence for SCM firmware

Techniques described herein relate to methods and systems for thermal management of a thermal environment. The method may include using thermal data items from computing devices and time series analysis to predict future thermal values for the thermal data items; performing a clustering analysis using the predicted future thermal values to assign cluster labels to the computing devices; using the cluster labels and the predicted future thermal values to assign predicted thermal status labels to the computing devices; assigning a confidence value to the predicted thermal status labels and ranking the computing devices based on the confidence values; performing an analysis to determine a thermal data item contributing to the assigned thermal status; and sending the results to a thermal environment administrator.

Systems and methods for device thermal management using dynamic ranking of device cooling needs

Techniques described herein relate to methods and systems for managing backup operations. The method may include receiving a request to perform a first backup operation for a first virtual machine (VM); making a first determination, using a vProxy preference map, that a first vProxy is assigned to the first VM based on a backup capability associated with the first vProxy; making a second determination that the first vProxy is not available to perform the first backup operation; making a third determination, based on the second determination and using a vProxy information database, that a second vProxy is available to perform the first backup operation based on the second vProxy being associated with the same backup capability as the first vProxy; and performing the first backup operation using the second vProxy.

Predictive optimal queue length management for backup sessions using distributed proxies

Embodiments for storing digital data in DNA storage by receiving input file data, encoding the input file data into an oligonucleotide sequence to produce sequence data through a transformation of text to binary to Base_3 encoding, organizing the Base_3 sequence data into chunks of a defined chunk size, and storing the chunks in a block of a blockchain. The oligonucleotide sequence may comprise metadata for the input file data that refers to actual data stored in the DNA storage, where the actual data is formed by synthesizing the oligonucleotide sequence in a DNA synthesis process. The chunks may be stored in the blockchain only if the block agrees with a smart contract defined for the oligonucleotide sequence.

Securely archiving digital data in DNA storage as blocks in a blockchain

The described technology is generally directed towards predicting the survival of a storage device (e.g., a hard disk drive or a solid state drive) to a specified time point, expressed as a confidence score, via a prediction uncertainty quantifier framework with a machine learning classifier. The confidence score corresponds to the likelihood of a storage device surviving until a specified time point (e.g., for n hours). In one implementation, a conformal prediction framework provides the confidence score for a storage device, based on survival rate data predicted using recent telemetry data collected for that storage device by an online semi-parametric Mondrian survival forest classifier. Updated confidence scores based on updated telemetry data can be obtained at various evaluation stages to reevaluate whether to take remedial action with respect to a storage device (e.g., replace the storage device). Multiple storage devices can be ranked by their respective associated confidence scores.

Growing community of inventors

Bengaluru, India

Rahul Deo Vishwakarma