Emc IP Holding Company LLC

Dell Products Inc.

Emc Corporation

Emc International Company

Jehuda Shemer

Assaf Natanzon

Kfir Wolfson

Saar Cohen

Amit Lieberman

Leehod Baruch

Ron Bigman

Alex Solan

Valerie Lotosh

Ofir Ezrielev

Erez Sharvit

Jawad Said

Boris Shpilyuck

Stav Sapir

Naor Radami

Arieh Don

Itay Azaria

Amihai Savir

Amos Zamir

Adam E Brenner

Ravi Vijayakumar Chitloor

Igor Dubrovsky

Steven Sadhwani

Lev Ayzenberg

Slavik Neymer

Bing Liu

Steven Richard Bromling

Nadav Azaria

Oded Peer

Yana Vaisman

Tomer Kushnir

Maxim Drobachevsky

Anand Rudrabhatla

Ilan Buyum

Ronen Rabani

Yossef Saad

Shahar Frank

Oron Golan

Ran Goldschmidt

Zion Drori

Ido Begun

George Mathew

Abhinav Duggal

Mark D Malamut

Arun Murti

Mahesh Kamat

Krishna Deepak Nuthakki

Chegu Vinod

Gabi Benhanokh

Chris E Rowen

Srinivas Kangyampeta

Aviram Fireberger

Ron Cooper

Sharon Vitek

Alexandra Solan

Anton Kucherov

William Price Dawkins

Avitan Gefen

Sumedh Wasudeo Sathaye

Dennis Thomas Duprey

Karl M Owen

Felix Shvaiger

Yaron Dar

Jennifer M Minarik

Ido Singer

Naveen Rastogi

Kelly L Lisai

Reut Kovetz

Patrick East

Maxim Grabov

Nomi Michaels

Matan Gilat

Georges Brun-Cottan

Peleg Yiftachel

Yuval Nir

Meir Pinhasov

Lehood Baruch

Jonathan Grey

Prasanna Malaiyandi

Providing content-based sensitivity classification to content data in a data processing system, by defining sensitivity designations for data objects stored in the system, and creating datasets by grouping metadata for data objects that are intended to classified with a same sensitivity designation, wherein each dataset spans multiple storage devices of different storage types, and wherein each dataset defines a single data sensitivity unit for the data objects referenced by a respective dataset. A sensitivity classifier component tags each dataset with a sensitivity tag to specify a protection or control operation on the data objects referenced by the respective dataset, and a processing component then processes data objects for each tagged dataset in accordance with the specified protection or control operation.

Data sensitivity classification using content-based datasets

A system can determine that there is a failed configuration in a microservices architecture, wherein the microservices architecture comprises a group of microservices. The system can order the microservices of the group to produce an ordered group of microservices, wherein the ordering first comprises failed microservices of the group of microservices, then dependent microservices of the group of microservices, and wherein respective failed microservices of the failed microservices depend on respective dependent microservices of the dependent microservices. The system can perform iterations of, splitting the ordered group of microservices into two subgroups of microservices, and reverting changes to one of the two subgroups of microservices. The system can determine that a microservice of the group of microservices has the failed configuration based on a result of the performing of the iterations.

Detecting configuration issues during pre-canary deployment

Disk drive reallocation or replacement is disclosed. When performing a data protection operation, a health score is determined for each of the disk drives associated with the data protection operation. Replacement is recommended for each of the disk drives with an unfavorable health score. The recommendation may also identify a drive class based in part on the write or wear pattern. This allows unhealthy drives to be replaced prior to performing the data protection operation.

Re-allocation of disks based on disk health prior to restore

A system for providing data protection services for data hosted by composed information handling systems includes a system includes a system control processor manager. The manager obtains a data protection architecture; identifies, based on the data protection architecture, first computing resources for a first pool that provides a first set of functionalities of the data protection services; identifies, based on the data protection architecture, second computing resources for a second pool that provides a second set of functionalities of the data protection services; assigns, based on hardware components of the first computing resources, a portion of the first set of functionalities to a first portion of the first computing resources; assigns, based on the hardware components of the first computing resources, a second portion of the first set of functionalities to a second portion of the first computing resources; and instantiates, based on the assignments, the data protection architecture.

System and method for dynamic data protection architecture

Compressing files is disclosed. An input file to be compressed is first aligned. Aligning the file includes splitting the file into sequences that can be aligned. The result is a compression matrix, where each row of the matrix corresponds to part of the file. The compression matrix may also serve as a warm start if additional compression is desired. Compression may be performed in stages, where an initial compression matrix is generated in a first stage using larger letter sizes for alignment and then a second compression stage is performed using smaller letter sizes. A consensus sequence id determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.

Warm start file compression using sequence alignment

One example method includes receiving backup requests, creating a respective bitmap fragment for each backup request, each bitmap fragment corresponding to a respective tracking start time, for one of the requested backups, identifying a group of bitmap fragments that correspond to at timeframe extending from a time at which a previous backup was requested to a time at which the particular backup was requested, using the bitmap fragments of the group to create a consolidated bitmap that includes all changes to data in a volume resulting from IOs during the timeframe, with the consolidated bitmap, reading the data from the volume and transmit the data to a recipient, identifying new write IOs, received in parallel with transmission of the data, that are directed to data included in the particular backup, and reading, from the volume, the data to which the new write IOs are directed, and temporarily storing that data.

Efficient multiple concurrent backup LWD filter

One example method includes, in connection with receipt of a backup request, using a consolidated bitmap, that is associated either with multiple bitmap fragments or with multiple backup streams, to read data from a volume and transmitting the data to a recipient, identifying new write IOs, received in parallel with transmission of the data, that are directed to data included in the backup request, reading from the volume, only once, the data to which the new write IOs are directed, writing the data read from the volume to multiple temporary logs, where each of the temporary logs is associated either with a respective one of the bitmap fragments, or with a respective one of the multiple backup streams, and transmitting the data from the temporary log to the recipient, wherein the transmitted data comprises the backup.

One example method includes encoding data as a polysaccharide structure, synthesizing the polysaccharide structure to create polysaccharide storage media that comprises the data, and storing the polysaccharide storage media. The example method may also include receiving a read request directed to the polysaccharide storage media, mapping the polysaccharide structure to create a map in response to the read request, traversing the map of the polysaccharide structure to determine an X-base number, and obtaining the data by converting the X-base number to a binary form.

Polysaccharide archival storage

One example method includes receiving, at an IO journal, a new entry that identifies a respective disk location L, and data X written at that disk location L, and determining whether a location specified in an oldest entry of the IO journal is specified in any other entries in the IO journal. When the location specified in the oldest entry is not specified in any other entries in the IO journal, adding the new entry to the IO journal, and augmenting the new entry with undo data. Or, when the location specified in the oldest entry is specified in at least one other entry in the journal, setting data specified in the oldest entry as undo data for the next entry that identifies that location, and adding the new entry to the IO journal, and deleting the oldest entry from the IO journal.

System and a method for generating live VM images with an extended recovery range

Methods and systems for managing inference models are disclosed. An inference model may be divided into any number of sub-network units and each sub-network unit of the sub-network units may be located in a different geographical location. Data privacy regulations may apply to the geographical locations. Therefore, a first sub-network unit may be subject to a first data privacy regulation and a third sub-network unit may be subject to a second data privacy regulation. If an output of a sub-network unit is not usable by a downstream sub-network unit due to differences in data privacy regulations, the first sub-network unit may be replaced with a second sub-network unit, the second sub-network unit generating an output that does meet the data privacy regulations of the downstream sub-network unit. Doing so may reduce interruptions to inference generation by the inference model.

Managing inference models to meet data privacy regulations

A data protection system includes a splitter configured to reduce latencies when splitting writes in a computing environment. The splitter captures a write and adds metadata to augment the write with virtual related information. The augmented data is provided to a smartNIC while the write is then processed in the IO stack. The smartNIC may have a volume only visible to the splitter. The smartNIC also includes processing power that allows data protection operations to be performed at the smartNIC rather than with the processing resources of the host.

Smartnic based virtual splitter ensuring microsecond latencies

Methods and systems for authenticating data processing systems throughout a distributed environment without user intervention are disclosed. To authenticate data processing systems without user intervention, a system may include a network core and one or more data processing systems. Data processing systems may provide telemetry data to the network core for use in future authentication processes. To increase security throughout the system, the network core may ensure that low security data points are not retained for authentication. A low security data point may be a data point that matches a data point obtained from another data processing system. If a root of trust is lost between a data processing system and the network core, the network core may use retained telemetry data points to generate a security questionnaire. If the data processing system successfully answers security questions of the security questionnaire, the root of trust may be restored.

System and method for data retention based on commonalities between data processing systems

Compressing files is disclosed. An input, which is associated with an original file and new content, is to be compressed. The input includes a consensus sequence of the original file and the new content. The new content is aligned based using the consensus sequence of the original file in order to generate a new consensus sequence that reflects both the original content and the new content. The compression engine generates a new compression matrix and a new consensus sequence. Using the new consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The new compressed file includes the pointer pairs and the new consensus sequence.

Adding content to compressed files using sequence alignment

One example method includes performing a filtering process that identifies one or more candidate hosts for scheduling of a pod, wherein the candidacy of a host is determined based in part upon an association rule, generating an overall host score for each of the candidate hosts, and scheduling the pod to one of the candidate hosts based on the overall host score of that candidate host. A host risk score and/or pod risk score may be used in the generating of the overall host score.

Kubernetes security isolation based on classified information and risk levels

Compressing files is disclosed. An input file to be compressed is first aligned. When the file has multiple axes or dimensions, the file is aligned along each of the axes. Aligning the file includes splitting the file into sequences that can be aligned along each of the axes or dimensions. Aligning the file generates a compression tensor, where each row or dimensional space of the compression tensor corresponds to part of the file. A consensus tensor is determined from the compression tensor. Using the consensus tensor, pointer lists are generated. Each pointer lists identifies a subsequence or portion of the consensus tensor. The compressed file includes the pointer lists and the consensus tensor.

Compressing multiple dimension files using sequence alignment

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Kfar Saba, Israel

Jehuda Shemer