Emc IP Holding Company LLC

Dell Products Inc.

Mcafee, Inc.

Joel Evans Christner

Nicole Reineke

Vinicius Michel Gottin

Rômulo Teixeira De Abreu Pinho

Farida Shafik

George M Ericson

Shary Beshara

Trevor Scott Conn

Raul Gracia

Ratinder Paul Singh Ahuja

Pengfei Wu

Hanna Yehuda

Si Ruo Chen

William James Deninger

Amy N Seibel

Zhenzhen Lin

Robert A Lincourt

Ruixue Zhang

Aurelian Dumitru

Faizel Lakhani

Richard A Backhouse

Omar Mohammed Mohammed Abdulaal

Shary George Waheeb Beshara

Ahmad Refaat Abdelfadeel Ahmad ElRouby

Farida Ahmed Ali Shafik

Patricia Quill

Omar Abdul Aal

Ahmad Refaat Abdel Fadeel Ahmad El Rouby

Omar Abdulaal

Tomasz Podsiadlik

John Power

Executing functions in serverless platforms leveraging metadata is disclosed. Embodiments of the invention related to using metadata when placing and/or executing functions. The metadata includes request metadata, container or function metadata, node metadata, and/or data metadata. The metadata allows a function to be placed and executed in a manner that accounts for user intent, container requirements, node resources, and data location. Functions can be placed across different serverless platforms. Once the metadata is evaluated and a location is selected, a function is placed and executed at the selected location.

Intelligent function execution leveraging metadata

One example method includes selecting items to be repatriated from a cloud site to an on-premises site, and the items include a workload and a data set accessed by the workload, transmitting a repatriation request from the on-premises site to the cloud site, and the repatriation request identifies the selected items, receiving, by the on-premises site from the cloud site, a compressed data set which includes the data set in compressed form, receiving, by the on-premises site from the cloud site, a compressed workload which includes the workload in compressed form, and the compressed workload and the compressed data set have been compressed with a compression algorithm automatically selected based on content, and/or context, of data in the data set, decompressing, at the on-premises site, the compressed data set and the compressed workload, and deploying the decompressed data set and the decompressed workload locally at the on-premises site.

Opportunistic on-prem data repatriation in hybrid clouds

A method for maintaining a function registry includes: obtaining, by a function-as-a-service (FaaS) function registry manager of a FaaS environment, a user query parameter from a first client, wherein the FaaS environment includes a FaaS function registry; in response to the obtaining: comparing the user query parameter with metadata parameters of the FaaS function registry; identifying at least a first function in the FaaS function registry with metadata parameters that match the user query parameter; and providing a first function image and first function metadata associated with the at least the first function to the first client, wherein the first client performs code-ahead and real-time documentation using the first function image and the first function metadata.

Function registry in function-as-a-service environments

Integration of semantic information into an asset management catalog (e.g., using a computerized tool), is enabled. For example, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on structure data representative of a structure of data stored in a data storage asset, determining semantic metadata applicable to the data stored in the data storage asset, in response to determining the semantic metadata, generating a data entry, applicable to the semantic metadata, to be added to an asset management catalog associated with the data storage asset, and in response to generating the data entry, associating the data entry with the data stored in the data storage asset.

Integration of semantic information into an asset management catalog

One example method includes receiving, by a graph proxy, an event from an event generator, and the event includes information about an IO and information about data affected by the IO, comparing, by the graph proxy, the data to a schema, when the data is determined by the graph proxy to map to the schema, identifying, by the graph proxy, a rule that is associated with the event, and the rule specifies performance of an action when a condition is met, and when the condition is met, performing, by the graph proxy, the action, and the action is performed with respect to a graph.

System and method for hydrating graph databases from external data

One example method includes receiving a request from a user to access data stored in a filesystem, in response to the request, retrieving metadata of the data and metadata of the user, validating the metadata of the data and the metadata of the user against a data access rule, and granting the user access to the data upon successful validation of the metadata of the data and the metadata of the user.

Classifying data and enforcing data access control using a context-based hierarchical policy

One example method includes monitoring a data access pattern, registering a data access request directed to data, comparing metadata associated with the data access request to a rule, based on a result of the comparing, sending a trigger to a graph service, and using information in the trigger to generate a visual representation of the data access request, wherein the visual representation indicates an extent to which the data access request is considered to constitute a potential threat to the data.

Malicious data access as highlighted graph visualization

One example method includes integrating user space applications with kernel space events including primitives. The events are intercepted in kernel space and processed in user space. The events can be stored in a session cache that allows a holistic view of behavior to be determined with regard to resources of the computing system. The events in the session cache can be correlated to user or process behavior by provided a time-based view of the events.

System and method for correlating filesystem events into meaningful behaviors

Integration of structural information into an asset management catalog (e.g., using a computerized tool), is enabled. For example, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on schema data applicable to data stored in a data storage asset, determining structure data representative of a structure of the data stored in the data storage asset, in response to determining the structure data, generating a data entry, applicable to the structure data, to be added to an asset management catalog associated with the data storage asset, and in response to generating the data entry, associating the data entry with the data stored in the data storage asset.

Integration of structural information into an asset management catalog

Shared function execution mitigates cold start penalties for execution units. When a serverless platform receives a request, the request is performed when the serverless platform has a warm execution unit for the request. If a warm execution unit is not available or running, the serverless platform may send the request to another serverless platform rather than cold start an execution unit. The cold start is performed when the warm execution unit is not available at other platforms.

Shared function container across serverless platforms to mitigate cold start performance penalties

A method includes identifying, by a server module running at a stream data platform, a set of data to be moved to a target data storage tier of the stream data platform, querying, by the server module, service level agreement (SLA) objectives of the target data storage tier, fetching, by the server module, the set of data, selecting, by the server module, a compressor based on the SLA objectives, compressing the set of data using the selected compressor, and storing the set of data in the target data storage tier.

Multi-objective compression for data tiering in a stream data platform

One example method includes injecting processing into sessions including IO sessions. Events in a file system are intercepted and processed. During processing, policies may be applied to the events. Some of the policies are triggered such that external actions or processing is applied to the event. Once the actions have been performed, the event may be processed by the file system.

System and method for proxying IO sessions to inject external processing

One example method includes data orchestration. Data sets are available to users in an online marketplace. Based on requests for data sets, other data sets may be recommended. The recommended data sets are moved or copied to recommended locations. This ensures that the data sets are available to the user more quickly if subsequently requested.

Predictive orchestration of data sets within marketplace

One example method includes extracting content metadata from data, storing the content metadata in a data catalogue, receiving at the data catalogue, from a user, a request to access the data, transmitting, by the data catalogue to a security service provider, an access request that includes the extracted content metadata and metadata relating to the access request, accessing, by the security service provider, identity metadata concerning an identity of the user, and a data access policy, and transmitting, by the security service provider to the data catalogue, a decision as to whether or not access can be granted to the data, and the decision is based on the data access policy, the identity metadata, and the metadata in the access request.

Access control rights assignment capabilities utilizing a new context-based hierarchy of data based on new forms of metadata

One example method includes receiving stream data batches at a client node. The client node determines if the stream data batches are compressible. For the compressible stream data batches, a request is sent to a server node for an on-demand stream compression service, the request including an indicator of a stream data type for the compressible data batches. The on-demand stream compression service is deployed and launched at the client node. The on-demand stream compression service includes a compressor pool of compressors that are able to compress the stream data type of the compressible stream data batches. A compressor of the compressor pool is selected and used to compress the compressible stream data batches. The compressed stream data batches are sent to the server node.

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San Jose, CA, United States of America

Joel Evans Christner