Emc IP Holding Company LLC

Emc Corporation

Emc International Company

Emc (benelux) B.v., S.a.r.l.

Emc Israel Development Center, Ltd.

Other

Emc B.v., S.a.r.l.

Assaf Natanzon

Saar Cohen

Jehuda Shemer

Amit Lieberman

Leehod Baruch

Kfir Wolfson

Ron Bigman

Arieh Don

Philip Derbeko

Anestis Panidis

David Zlotnick

Boris Shpilyuck

Amitai Alkalay

Zvi Schneider

Alex Solan

Steven Richard Bromling

Amihai Savir

Stephen J Todd

Anat Eyal

Lev Ayzenberg

Antony Bett

Luay Al-Alem

Yossef Saad

Shlomo Ahal

Yuval Aharoni

Michael Rhodes

Jean-Pierre Bono

Alexandr Veprinsky

Himabindu Tummala

David Meiri

Pengfei Wu

Anat Parush Tzur

Oron Golan

Oded Kedem

Raul Shnier

Man Lv

Slavik Neymer

Yuri Manusov

Yong Colin Zou

Oshry Ben-Harush

Ido Singer

Sorin Faibish

Kun Wang

Avitan Gefen

Said Tabet

Shahar Frank

Bradford B Glade

Kirill Shoikhet

Dragan Savic

Oded Peer

Helen S Raizen

Philip N Shilane

Yedidya Dotan

Bing Liu

Kenneth Durazzo

Mark J Halstead

Nickolay Alexandrovich Dalmatov

David Lionel Black

Valerie Lotosh

Erez Sharvit

Zvi Gabriel Benhanokh

Michael Trachtman

Xiali He

Brian Lake

John S Harwood

Dennis Thomas Duprey

Eitan Bachmat

Michael E Bappe

Ravi Vijayakumar Chitloor

Si Ruo Chen

Karl M Owen

Felix Shvaiger

Patrick Brian Riordan

Harold Martin Sandstrom

Erez Webman

Zhenzhen Lin

Gabriel Benhanokh

Kadir Ozdemir

Ziv Kedem

Edith Epstein

Cesareo Contreras

Ido Halevi

Atul Kabra

William C Davenport

Adnan Sahin

Kalyan Chakravarthy Gunda

Marik Marshak

Michael J Scharland

Lawrence N Friedman

Benjamin W Yoder

Dan Arnon

Sairam Veeraswamy

Dan Aharoni

Donna Barry Lewis

Colin Yong Zou

Stephen Richard Ives

Bhimsen Bhanjois

Ajay Potnis

Shiri Gaber

Paul LeMahieu

A method for data defragmentation is disclosed, including: selecting a file that is stored in a content-addressable storage system, the file including a plurality of blocks, and the storage system including a plurality of deduplication chunks; arranging the plurality of blocks in block groups, each block group including a set of consecutive blocks having a combined size that matches a deduplication chunk size of the storage system; aligning each block group with a different one of the plurality of deduplication chunks of the storage system, such that the blocks in each block group are stored in the storage system in an order that is based on the order in which the blocks are positioned within the file.

Optimizing file system defrag for deduplicated block storage

Systems and methods for performing data protection operations in a containerized application. A sidecar application is associated with or attached to a main application. The sidecar application has access to the resources of the main application. Backup requests are handled by the sidecar so that the application, which may be stateful, can be placed in a consistent state prior to performing the backup operation. The backup operation is then performed on the consistent application or on the data that is quiesced.

Application consistent snapshots as a sidecar of a containerized application

One example method includes chunking a respective disk of each of a plurality of virtual machines (VM) to create a respective plurality of chunks associated with each of the VMs, creating, based on the chunking process, a cluster comprising one or more of the VMs, creating a VM template whose data and disk structure match respective data and disk structures of each of the VMs in the cluster, and in response to a file operation involving a first one of the VM disks, defragmenting the first VM disk so that a disk structure of the first VM disk is the same as a disk structure of the VM template.

Smart de-fragmentation of file systems inside VMs for fast rehydration in the cloud and efficient deduplication to the cloud

One example method includes receiving input concerning a mobile IoT device, and the input includes information about a location of the mobile IoT device, information about whether the mobile IoT device is moving, and, when the mobile IoT device is moving, information about the range, speed, and bearing of the mobile IoT device. Next, the method includes generating a predicted location of the mobile IoT device based on the inputs received, using the predicted location of the mobile IoT device and a map of nodes in an environment where the mobile IoT device is located to make a migration decision concerning an application used by the mobile IoT device, and migrating the application from a present location to a node expected to be accessible by the mobile IoT device when the mobile IoT device reaches the predicted location, and the node and present location are physically separated by a distance.

Location based application migration for enhancing lightweight IoT device applications

A data protection system configured to backup a time series database is provided. The data protection system may be integrated with or have access to consolidation policies of the time series database. The backup policy and backup retention policy are set by monitoring the consolidation policy and adjusting the backup policy to ensure that the data in the time series database is protected prior to being downscaled, discarded or otherwise consolidated.

Backup and tiered policy coordination in time series databases

Techniques are provided for reinforcement learning-based evaluation of software product usage. One method comprises obtaining key performance indicators indicating software product usage by a user; determining, for a predefined time window: (i) a mean and/or a median of the obtained KPIs; (ii) an amount of time that the software product was active; and (iii) an amount of interactions by the user with a user interface; evaluating possible login states of the software product using at least one reinforcement learning agent, wherein the evaluating comprises (a) observing the plurality of possible login states, including a current state comprising a current login state of the software product, and (b) obtaining an expected utility score for changing from the current login state to a different login state of the software product; and determining whether to change from the current login state to a different login state of the software product based on the expected utility score.

Evaluating software license usage using reinforcement learning agents

A containerized environment and application that are configured for component specific continuous replication and granular application level application. A key value store, which stores key values related to configuration data of the containerized application, is replicated continuously to a replicated key value store at a replica site. Persistent volumes may also be replicated to a replica site. The replication can be performed to multiple replica sites in an application specific manner.

Continuous replication and granular application level replication

A computing device of a data register includes persistent storage and a data register manager. The persistent storage stores: an instance of a primary chain that specifies an organizational structure of computing devices of the data register and a secondary chain for registering data with a first portion of the computing devices. The data register manager monitors sizes of each of the portions of the computing devices specified by the organizational structure; makes a determination, based on the monitoring, that the organizational structure fails to meet at least one criteria; and in response to the determination: reorganizes the organizational structure to meet the at least one criteria by, at least in part, modifying the secondary chain.

System and method for data registration

A replication system for data of mobile devices is disclosed. The data of a mobile device is uploaded to stations in an area. Metadata associated with the objects is stored in a centralized or decentralized system. The metadata can be accessed to identify the stations storing the device's objects and the data of the mobile device can then be retrieved from the stations and reconstructed.

Decentralized and distributed continuous replication system for moving devices

Smart de-fragmentation of file systems inside VMS for fast rehydration in the cloud and efficient deduplication to the cloud

One example method includes creating an empty reconstruction stream database, identifying a data time interval, identifying data sources in which data was stored during the data time interval, reading data from the data sources, where the data read out from the data sources are associated with respective timestamps that fall within the data time interval, inserting the read out data into the empty reconstruction stream database so as to create a high resolution data stream, where the data are ordered in the empty reconstruction stream database according to timestamp, processing the data in the high resolution data stream and, based on the processing of the data, identifying and resolving a problem relating to an operating environment in which the data was initially generated.

Tiered forensics of IoT systems in cloud and time series databases

Deduplication of files or other data or objects in a manner that is aware of a format of the file and the application. A file may be chunked based on the format. The chunks are more consistent and lead to higher deduplication ratios. The file may be presented as a single file. However, the file is stored in chunks or subfiles and deduplication is performed with respect to the chunks. When the file is read, the file is rebuilt from its respective chunks. The files may also be compressed using differential compression, which leverages the content of similar files to compress a current file.

Application aware deduplication

One example method includes creating a backup of data, creating metadata associated with the backup, hashing the backup to create a backup hash, obtaining a key from a blockchain, generating an aggregate hash of a combination that includes the key and the backup hash, and transmitting the aggregate hash to a blockchain network. Because the aggregate hash is not modifiable when stored in a blockchain, an immutable record exists that establishes when a particular backup was created.

Blockchain technology for data integrity regulation and proof of existence in data protection systems

A data manager includes persistent storage and a data register. The persistent storage stores an identity chain and a data availability chain. The data register obtains data associated with an entity registered with the identity chain using a public key associated with the entity; obtains an object identifier associated with the data by storing the data in a data storage as a record; and records, on the data availability chain, both of: the record using a private key associated with the entity, and the object identifier.

System and method for data registration and access

Managing backup copies in a right to delete personal data environment is described. A system creates a revised backup copy by deleting any data that a deletion request ledger specifies for deletion from a backup copy that was created for a data object. The system associates an updating time with the revised backup copy. The system retrieves the revised backup copy in response to receiving a restoration request corresponding to the revised backup copy. The system enables restoration of the data object by deleting any data i) that the deletion request ledger specifies for deletion from the revised backup copy and ii) that is associated with a deletion request time which is subsequent to the updating time.

Managing backup copies in a right-to-delete personal data environment

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Tel Aviv, Israel

Assaf Natanzon