Emc IP Holding Company LLC

Emc Corporation

Emc International Company

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

Ido Singer

Assaf Natanzon

Patricia Gomes Soares Florissi

Sudhir Vijendra

Saar Cohen

Ofri Masad

Ehud Gudes

Amnon Meisels

Yaron Gonen

Benny Lutati

Oded Kedem

Yossi Mossel

Jehuda Shemer

Leehod Baruch

Alex Solan

Lev Ayzenberg

Shlomo Ahal

Evgeny Drukh

Asaf Natanzon

Lev Ayzenbergv

Peleg Yiftachel

Asaf Bar'el

A first portion of a distributed catalog service is implemented for a given one of a plurality of distributed processing node clusters associated with respective data zones, each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone. The first portion of the distributed catalog service receives a request to identify for each of a plurality of data resources to be utilized by an application initiated in the given cluster whether the data resource is a local or remote data resource relative to the given cluster, and provides a response to the request. The first portion of the distributed catalog service in combination with additional portions implemented for respective additional ones of the distributed processing node clusters collectively provide the distributed catalog service with capability to resolve local or remote status of data resources in each of the data zones.

Distributed catalog service for multi-cluster data processing platform

An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to distribute in-memory computations across at least first and second nodes of respective distinct data processing clusters of a plurality of data processing clusters over at least one network, and to aggregate results of the distributed in-memory computations for delivery to a requesting client device. The data processing clusters are associated with respective distinct data zones, and the first and second nodes of the respective distinct data processing clusters are configured to perform corresponding portions of the distributed in-memory computations utilizing respective ones of first and second in-memory datasets locally accessible within their respective data zones. The in-memory computations in some embodiments illustratively comprise Spark computations, such as Spark Core batch computations. The in-memory datasets in such an arrangement may comprise respective Spark resilient distributed datasets.

Scalable distributed in-memory computation

An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to receive results of intermediate statistical computations performed on respective ones of a plurality of datasets in respective ones of a plurality of distributed processing nodes configured to communicate over at least one network. The processing device is further configured to perform at least one global statistical computation based at least in part on the results of the intermediate statistical computations, and to utilize a result of the global statistical computation to perform one or more benchmarking operations for specified parameters relating to the plurality of datasets. The distributed processing nodes are associated with respective distinct data zones in which the respective datasets are locally accessible to the respective distributed processing nodes. At least a subset of the receiving, performing and utilizing are repeated in each of a plurality of iterations.

Global benchmarking and statistical analysis at scale

An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device is configured to distribute in-memory computations across a plurality of data processing clusters associated with respective data zones, and to combine local processing results of the distributed in-memory computations from the data processing clusters. The distributed in-memory computations utilize local data structures of respective ones of the data processing clusters. A given one of the local data structures in one of the data processing clusters receives local data of the corresponding data zone and is utilized to generate the local processing results of that data processing cluster that are combined with local processing results of other ones of the data processing clusters. The local data structures are configured to support batch mode extensions such as Spark SQL, Spark MLlib or Spark GraphX for performance of the distributed in-memory computations.

Scalable distributed in-memory computation utilizing batch mode extensions

A fault tolerance computer implemented method, a computer program product, and a system for allowing asynchronous replication to continue with no lag increase due to RPA failure comprising: creating a consistency group (CG) on a first replication appliance and a shadow copy of the CG on a second replication appliance; sending input/output (IO data), via a splitter, to the CG in a first replication appliance; and sending IO data, via the splitter, to shadow copy of the CG on a second replication appliance.

Consistency group fault tolerance

An apparatus comprises a multi-cluster distributed data processing platform. Each of the clusters of the platform is configured to perform processing operations utilizing local data resources accessible within a corresponding data zone. A first application is initiated in one of the clusters, and data resources to be utilized by the application are determined. For one or more of the data resources identified as local data resources for the associated cluster, processing operations are performed utilizing those local data resources in that cluster in accordance with the first application. For one or more of the data resources identified as remote data resources for the associated cluster, one or more additional applications are initiated in one or more additional ones of the clusters. This repeats recursively for each additional application until all processing required by the first application is complete. Processing results from the clusters are aggregated and provided to a client.

Multi-cluster distributed data processing platform

A method, system and program product for implementing the xcopy command in a replication environment, the replication environment having a production site, a splitter, and a replication site, wherein the replication site has a journal, comprising, determining if the source and target LUNs of the xcopy command are replicated, based on a determination that both source and target LUNs are replicated, determining if the production and replication LUNs are synchronized, based on a positive determination that the LUNs are synchronized, and performing the xcopy command on the replication.

Replication of xcopy command

In a system comprising a production environment and a replication environment, a method and program product for dynamically switching from an asynchronous replication policy to a synchronous replication policy. In a system comprising a production environment and a replication environment, a method for dynamically switching from a synchronous replication policy to an asynchronous replication policy.

Dynamically switching between synchronous and asynchronous replication

A method, system, and computer product for hardening a snapshot in a journal based replication environment comprising a production and replication site, the replicated environment running in production side protection mode, the method comprising sending notification of a start of the check from the production site to the replication site, receiving the notification at the replication site, and recording the start of the check, running a check on the replication environment, associating the check with one or more snapshots, upon completion of the check, and storing the results of the check on the replication site.

Snapshot hardening

In one aspect, a method includes using a processor to generate system configuration parameters that include using the processor to receive a business input from a user, convert an abstract model into a concrete model based on the business input and optimize the concrete model to solve for system configuration parameters of the system. The abstract model includes a business goal.

Automatically determining configuration parameters for a system based on business objectives

Method and apparatus to provide bottleneck processing in a continuous data protection system having journaling. In an exemplary embodiment, system parameter information is collected and analyzed to output bottleneck information.

Growing community of inventors

Nes-Ziona, Israel

Ido Singer