Emc IP Holding Company LLC

Ricky Yuxi Sun

Junping Zhao

Yu Cao

Kenneth Durazzo

Jun Tao

Xiaoyan Guo

Accela Yilong Zhao

Fenghao Zhang

Kun Wang

Sanping Li

Kevin Xu

Randall Shain

Zhe Dong

Jie Bao

Baoyao Zhou

Demetrios Fanourgiakis

Min Zhu

Frank Junping Zhao

Mingzhao Li

Ting Liang

Qihong Gan

Yabo Su

Embodiments of the present disclosure relate to a method and an apparatus for data backup. The method comprises selecting, from a service store, a backup service for an application. The method further comprises setting, for the application, configuration information associated with the selected backup service. Additionally, the method further comprises in response to the backup service being enabled, deploying a backup service agent corresponding to the backup service to a source node associated with the application based on the configuration information, such that data of the application is backed up from the source node to a target node via the backup service agent.

Method and device for data backup

A method, device, and computer program product for fingerprint based status detection in a distributed processing system is provided. The method comprises: generating and sending, at a root node, an initial fingerprint based on the output message ID to be sent; at a middle node, generating and sending an updated fingerprint based on a received input message ID, an output message ID to be sent and a received fingerprint; sending, at the leaf node, a final updated fingerprint to a tracking task point; and obtaining, at the tracking task point, status information based on the final updated fingerprint. As the method does not generate traffic at each node but only generates tracking traffic at leaf nodes, the approach according to the present disclosure reduces tracking overhead significantly while guaranteeing the reliability of data processing.

Method and device for fingerprint based status detection in a distributed processing system

The present disclosure provides a method and apparatus for storing data. The method comprises: extracting metric information and metadata associated with the metric information from time series data, the metric information including a time value and a numerical value at the time value; and staling the metric information in an object storage device and the metadata in a database. As compared with the prior art, the present disclosure provides a flexible storage design to guarantee extensibility of storage capacity and meanwhile support fast data retrieving.

Method and apparatus for storing data

Embodiments of the present disclosure provide a method and apparatus for distributed data processing. The method comprises: receiving from an upstream node an input message attached with a shared count, the shared count being used for determining a processing status of a root message associated with the input message; processing the input message to generate one or more new messages; allocating to each of the one or more new messages a respective new shared count based on the received shared count; and transmitting the one or more new messages to one or more downstream nodes respectively. Compared with the prior art, the methods and apparatuses for distributed data processing according to the embodiments of the present disclosure can effectively reduce network traffic overheads and the consumed CPU and memory resources, and would be scalable for different topologies of various distributed data processing systems.

Method and apparatus for distributed data processing

A plurality of data sets to be moved from a source site to a target site in a cloud computing platform is received at a plurality of a containerized data ingest components located at the source site. The received plurality of data sets are provided from the plurality of data ingest components to a staging cluster comprising a plurality of containerized broker components located at the source site, wherein the plurality of containerized broker components queue the plurality of data sets. The queued plurality of data sets are provided from the plurality of containerized broker components to a processing cluster comprising a plurality of containerized data processing components, wherein the plurality of containerized data processing components process the plurality of data sets. The plurality of data sets is transmitted from the plurality of containerized data processing components to the target site.

Data processing and data movement in cloud computing environment

A method for processing a data request in a software defined storage system, wherein the software defined storage system comprises one or more nodes configured as a set of client modules operatively coupled to a set of server modules, comprises the following steps. A data request with a data set is received at one of the set of client modules. One or more data services (e.g., deduplication and/or data compression) are performed on the data set, wherein the performance of the one or more data services on the data set is dynamically shared between one or more of the set of client modules and one or more of the set of server modules.

Data services for software defined storage system

A method of controlling one or more data services in a computing environment includes the following steps. A request to one of read data from and write data to one or more storage devices in a computing environment is obtained from an application executing on a host device in the computing environment. One or more application-aware parameters associated with the data of the request are obtained. Operation of the one or more data services is controlled based on the one or more application-aware parameters.

Application-aware control for data services

A set of multidimensional data is obtained. At least a portion of the set of multidimensional data is processed to generate a set of formatted data, wherein the set of formatted data comprises at least one of attributes, attribute values and statistics on attribute values. A user is enabled to select, on a graphical user interface, an analysis task to be performed on at least a portion of the set of formatted data. One or more visualizations are generated from a set of visualization types for presentation on a graphical user interface to the user. The set of visualization types comprises a first visualization type representing a relationship between two attributes whereby attribute value pairs are represented by varying colors, a second visualization type representing thematic variations over time with respect to values of at least one attribute; and a third visualization type representing values of three attributes comprising one or more histograms. The one or more generated visualizations are based on the selected analysis task.

Visual analysis for multi-dimensional data

A method of request scheduling in a computing environment comprises the following steps. One or more requests to at least one of read data from and write data to one or more storage devices in the computing environment are obtained from a host device. The one or more requests are aligned corresponding to a segment size for which one or more data services in the computing environment are configured to process data. The one or more aligned requests are dispatched to the one or more data services prior to sending the one or more requests to the one or more storage devices.

Data service-aware input/output scheduling

A data element to be inserted into a memory data structure, represented by a key and a value, is received. A target node into which the received data element is to be inserted is determined based on the key of the received data element. A determination is made whether or not the target node is already compressed. An append-write operation to insert the data element into the target node is performed when the target node is already compressed. An evaluation is performed prior to inserting the data element when the target node is not already compressed. An in-place write operation to insert the data element into the uncompressed target node is performed when the evaluation generates a first result. The target node is compressed and then an append-write operation to insert the data element into the compressed target node is performed when the evaluation generates a second result.

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Beijing, China

Ricky Yuxi Sun