Emc IP Holding Company LLC

Dell Products Inc.

Rahul Deo Vishwakarma

Shelesh Chopra

Parmeshwr Prasad

Bing Liu

Jayanth Kumar Reddy Perneti

Hemant Ramesh Gaikwad

Gopal Singh

Mahantesh M Ambaljeri

Sharath Talkad Srinivasan

Jitendra Singh

Parminder Singh Sethi

Gururaj Reddy Kulkarni

Supriya Kannery

Girish Chandra Belmanu Sadananda

Kalyan Chakravarthy Gunda

Owen Martin

Chandrashekar Nelogal

Earl Medeiros

Anannya Roy Chowdhury

Sujan Kumar Shetty

Vaideeswaran G

Pooja Singh

Lu Chen

Kapil Sampath

Paul Hammer

Vaneeswaran N

Anushree Priyadarshini

A method for holding an anomalous privileged operation, that includes receiving, by a privileged operations monitor executing on a client device, a privileged operation request to copy data or modify data, obtaining a plurality of metrics for the privileged operation request, calculating an anomaly score for the privileged operation request, based on the plurality of metrics, making a first determination, based on the anomaly score, that the privileged operation request is anomalous, and based on the first determination, placing a hold on the privileged operation request.

Methods and systems for anomalous operation detection on a consumer device

Embodiments include a method of storing digital data in a blockchain by receiving the digital data from a user, deduplicating the data in a deduplication system of the user to form deduplicated data, and encoding the deduplicated data into an array format for storage on a blockchain. A smart contract is deployed for deduplication on the destination side of the data, and the deduplicated data is encoded into a Binary Aligned Map (BAM) format for storage on the blockchain. A process on the destination side stores the deduplicated array data as a next block in the blockchain only if the next block agrees with the smart contract, and the blockchain is stored in system storage comprising hard disk, solid state, tape, or optical devices.

Storing digital data in storage devices using smart contract and blockchain technology

Systems and methods are provided for context-aware maintenance window identification. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: estimate a completion time of a maintenance operation; predict future usage of the IHS; identify a time window for the maintenance operation based upon the estimation and the prediction.

Context-aware maintenance window identification

In general, in one aspect, the invention relates to a method for enabling enhanced logging. The method includes obtaining a log associated with a job; determining, using the log, that enhanced logging is to be enabled prior to initiating the job; enabling, in response to the determination, enhanced logging on at least one node, and initiating servicing of the job, after the enabling, on the at least one node.

Intelligently determining when to perform enhanced logging

A message broker resource monitoring service obtains message broker resource parameter data of a resource, based on communications of a message broker. Based on the resource parameter data and historical data, the message broker resource monitoring service obtains a predicted message delivery time value, which can be in association with confidence. If the predicted message delivery time value satisfies a resource deletion criterion, e.g., the predicted message delivery time value, with sufficient confidence, exceeds a threshold value, the message broker resource monitoring service triggers an action to delete the resource. To obtain the predicted value, a regression such as symmetric conformal quantile regression can be applied to the parameter data, e.g., to obtain a predicted message delivery time/latency value.

Message broker resource deletion

A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.

Method and system for predicting user involvement requirements for upgrade failures

Embodiments described herein relate to a method for probabilistically forecasting the state of hardware components. The method may include obtaining data items corresponding to a hardware component and performing an analysis of the hardware component. The analysis may include making a variety of probability predictions as to whether a label from among a set of possible labels is likely to be the correct label. The set of probabilities from the aforementioned analysis are then analyzed to determine which predicted label has the tightest range, and the prediction with the tightest range for a certain label is displayed to a user in a ranked fashion that includes a quantity of such probability prediction ranges. Such a display may allow an administrator to take action as to which hardware components should be replaced and in what order.

System and method for probabilistically forecasting health of hardware in a large-scale system

From among physical storage devices (PSDs) of a storage system, a set of two or more of the PSDs that are eligible for scrubbing may be determined; and from among the set, a relative eligibility of the PSDs may be determined. Conformance prediction analysis may be applied to determine the set and the relative eligibility of PSDs of the set. The conformance prediction analysis may determine a scrubbing eligibility classification (e.g., label), and a confidence value for the classification, which may serve as the relative eligibility of the PSD. The eligible PSDs may be ranked in an order according to determined confidence values, and may be further classified according to such order. The future workload of the storage system may be forecasted, and the scrubbing of PSDs may be scheduled based on the forecasted workload of the system and the relative eligibilities of the set of PSDs.

Managing storage device scrubbing

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

Non-volatile Random Access Memory (NVR) on a storage system may be dynamically converted between use as temporary memory in a memory context and use as persistent memory in a storage context. NVR (e.g., embodied as DIMM) may be utilized in a hybrid capacity, where some of the NVR is used as memory and some of the NVR is used as storage, and where NVR memory is converted to memory as needed, dynamically as I/O is being processed using the NVR. A host system may be directly connected to an internal switching fabric of the data storage system without an intervening component of the storage system (e.g., a director) controlling access of the host system to the internal fabric or to the memory. The host system may provision and use the NVR as storage by directly communicating with the NVR over the internal fabric, for example, using RDMA.

Dynamic use of non-volatile ram as memory and storage on a storage system

Techniques described herein relate to methods for managing backup and restore operations. Such a method may include performing a vProxy health analysis to obtain vProxies assigned a healthy label; performing a confidence analysis to assign a health confidence score to each separate healthy label for each vProxy of the plurality of vProxies; filtering the plurality of vProxies to obtain a set of vProxies, each having a separate health confidence score over a confidence score threshold; calculating average throughput for each vProxy in the set of vProxies; assigning an optimal label to a vProxy of the set of vProxies based on the vProxy having an average throughput over a throughput threshold; and presenting a list of optimal vProxies comprising the vProxy to an entity configuring a backup job. The entity may select the vProxy for use in the backup job in response to being presented the optimal label of the vProxy.

Systems and methods for selecting optimal proxy devices for backup and restore operations for virtual machines

A method and system for health rank based virtual machine restoration using a conformal framework. With respect to enterprise data protection, solutions need to address two primary responsibilities: at the onset of failure or disaster, restore any running applications, as well as any lost or damaged data; and minimize (if not eliminate) the future occurrence of such failures or disasters. In tackling the latter responsibility, the disclosed method and system leverage health-score assessments to ensure the restoration of virtual machines onto healthy infrastructure. The aforementioned health-score assessments employ clustering to identify, and a conformal framework to rank, healthy hosts onto which virtual machines may be restored.

Method and system for health rank based virtual machine restoration using a conformal framework

A method for processing backup policy generation requests includes receiving, by a production agent manager, a backup policy generation request, in response to receiving the backup policy generation request: identifying a backup to transfer to a backup storage, wherein the backup comprises a plurality of data blocks, performing a data storage device evaluation on a set of data storage devices to obtain a set of health scores, wherein each health score of the set of health scores corresponds to a data storage device of the set of data storage devices, assigning, based on the set of health scores, a production agent to a data storage device of the set of data storage devices to generate a backup policy, and initiating, based on the backup policy, a deployment of the production agent to the data storage device.

System and method for ranking data storage devices for efficient production agent deployment

One example method includes gathering respective performance data concerning each asset in a group of assets, clustering the performance data so as to define a first cluster and a second cluster, and data assets in the first cluster are assigned a HIGH RISK label and data assets in the second cluster are assigned a LOW RISK label, assigning a respective risk score to each of the assets, and the risk score includes a quantified risk level for the asset to which the risk score has been assigned, ranking the assets with the HIGH RISK label according to their respective risk scores, and backing up a ranked asset based on an IO volume associated with that ranked asset.

Intelligent protection of virtual machine by identifying the degree of risk at a granular level

A kexec-based system update process wherein user-specific data is transferred on reboot of the second kernel. Upon initializing kexec load, buffer memory is assigned to the second kernel and the system loads control pages of fixed size for the second kernel boot, and also loads user-specific data onto extended control pages of variable size. Upon boot of the second kernel, the user-specific data is extracted from the extended control pages and transferred to the corresponding applications.

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Long Beach, CA, United States of America

Rahul Deo Vishwakarma