Dell Products Inc.

Emc IP Holding Company LLC

Emc Corporation

Paulo De Figueiredo Pires

Ana Cristina Bernardo De Oliveira

Flávia Coimbra Delicato

Evandro Luiz Cardoso Macedo

Anselmo Luiz Éden Battisti

Wagner Dos Santos Vieira

Vinicius Michel Gottin

Angelo E M Ciarlini

Alex Laier Bordignon

Fábio André Machado Porto

Bruno Carlos Da Cunha Costa

Débora Christina Muchaluat Saade

Juan Lucas Do Rosário Vieira

Rômulo Teixeira De Abreu Pinho

Jonas Furtado Dias

Edward José Pacheco Condori

Flavia Coimbra Delicato

Yania Molina Souto

Thais Vasconcelos Batista

Antonio Augusto De Aragão Rocha

Thiago Pereira Da Silva

Vinícius Michel Gottin

Adriana Bechara Prado

Julia Drummond Noce

Vitor Silva Sousa

Fabio A M Porto

Fábio A Perosi

Marina Ivanov Pereira Josué

Arthur Albuquerque Zopellaro Soares

André Ricardo De Carvalho Saraiva

Ian Vilar Bastos

Amir H K Moghadam

Franklin Jordan Ventura Quico

Franklin Ventura

Marcia Lucas Pesce

Rayan Gustavo Oliveira Jucá Lima

Eduardo Vera Sousa

Jonas F Bias

Altobelli De Brito Mantuan

Rodolpho Rosa Da Silva

Marina Ivanov

A service partitions a service function chain (SFC) into multiple virtualized network functions (VNFs). The services deploys the VNFs to a smart contract operating in a distributed ledger. The smart contract initiates a bid to service the SFC. Multiple infrastructure providers (InPs) are identified and included in a graph structure. The graph is organized to reflect the order by which the VNFs are to be serviced by the different InPs. The graph also lists the costs the InPs require to service the various different VNFs. The InPs included in a specific graph traversal having the smallest cost are selected, but the cost that is used is the cost for the graph traversal having the penultimate smallest cost. The service outputs the selected InPs as well as the penultimate smallest cost.

Distributed and DLT-based SFC partitioning over multiple providers in edge environments

The quality of decompressed data that was compressed in a lossy manner to generate lossy compressed data, is improved. Lossy compressed data may be generated at a device and transmitted to a gateway. The gateway includes a reconstruction engine that is trained to reduce the compression loss by generating a learned residue. The residue is added to the lossy decompressed data to generated corrected decompressed data that has a smaller loss than the lossy decompressed data.

Quality of lossy compressed sensor data with machine learning

Techniques are provided for logical network resource allocation and creation. One method comprises obtaining a representation of virtualized resources to allocate for a logical network within a physical network, wherein the physical network comprises physical nodes and physical links, wherein the virtualized resources comprise virtual nodes and virtual links, and wherein the physical nodes host one or more virtual nodes and one or more of the physical links host a given virtual link; obtaining a first ordered list of the plurality of virtual nodes; obtaining a second ordered list of the plurality of physical nodes; for a virtual node in the first ordered list: selecting and allocating a physical node from the second ordered list as a host for the virtual node; and allocating a physical path for the virtual links associated with the virtual node. The logical network can be created using the allocated physical nodes and physical links.

Logical network resource allocation and creation

Placing virtual network functions (VNFs) in nodes of an infrastructure are disclosed. A request for services may identify a series of VNFs. Requests may be grouped by time windows such that all requests in a time window are placed together. Placing the requests includes identifying which nodes host which VNFs. Some of the VNFs may already exist in the infrastructure. Placing the requests may include generating a placement plan that accounts, for example, for available resources, links in the infrastructure, node restrictions, service level agreements, quality of service requirements, and user priorities. Once generated, the plan is executed. Chains of VNFs can be replaced when service degrades or based on user location.

Dynamic virtual network function placement in edge computing environments

Reconstructing compressed data to reduce compression loss. Data at a device is compressed using lossy compression and metadata values are added to the compressed data. A gateway receives a package including the compressed data and the metadata values. The data is decompressed and input to a machine learning model along with the metadata values. The machine learning model is trained to reduce the compression loss. The output of the model is an improved decompressed data. Actions may be performed based on the improved decompressed data.

Reducing the compression error of lossy compressed sensor data

A method for scaling a virtual network function (VNF) by analyzing VNF metrics data, the method that includes obtaining, by a computing device, a VNF metrics data, identifying a queue in the VNF metrics data, calculating a constraint violation probability based on the queue, making a first determination that the constraint violation probability is greater than an upper threshold, and based on the first determination, scaling up the VNF.

Systems and methods for predictive scaling of edge entities

A method for managing a service function chain (SFC) includes obtaining, by a SFC orchestrator of a first domain, a SFC creation request for a SFC, in response to the SFC creation request: generating a domain participation list of a set of potential domains that may service the SFC creation request, sending a bid request to each of the set of potential domains, receiving bid responses from the set of potential domains, ranking the set of potential domains, sending a SFC placement request to a second domain of the set of potential domains based on the ranking, and obtaining a SFC placement response from the second domain, wherein the SFC placement response specifies success of SFC placement in the second domain.

System and method for an auction-based scheme for service function chain placement

Techniques are provided for data discovery and data integration in a data lake. One method comprises obtaining data files from a data lake, wherein each data file comprises multiple records having multiple fields; selecting multiple candidate fields from a data file based on a record type; determining a relevance score for each candidate field from the data file based on multiple features extracted from the data file; and clustering the scored candidate fields into clusters of similar domains using a hashing algorithm, wherein a given cluster comprises candidate fields, wherein multiple data files can be integrated based on a domain of the candidate fields in the given cluster. The relevance score for each candidate field is based on multiple features comprising, for example, features that take into account a morphological or semantic similarity between file name, file metadata and/or file records and features that consider statistics of candidate fields in a data file.

Automatic indexing of relevant domains in a data lake for data discovery and integration

Techniques are provided for dataflow execution time estimation for distributed processing frameworks. An exemplary method comprises: obtaining an input dataset for a dataflow for execution; determining a substantially minimal data unit for a given operation of the dataflow processed by the given operation; estimating a number of rounds required to execute a number of data units in the input dataset using nodes assigned to execute the given operation; determining an execution time spent by the given operation to process one data unit; estimating the execution time for the given operation based on the execution time spent by the given operation to process one data unit and the number of rounds required to execute the number of data units in the input dataset; and executing the given operation with the input dataset. A persistent cost model is optionally employed to record the execution times of known dataflow operations.

Dataflow execution time estimation for in-memory distributed processing framework

Pattern queries are evaluated in parallel over large N-dimensional datasets to identify features of interest. Similarity-based pattern matching tasks are executed over N-dimensional input datasets comprised of numeric values by providing data representations for the N-dimensional input datasets, a pattern query and one or more candidate solutions for the pattern query, such that the pattern query specifies a pattern of an N-dimensional body that is compared to at least one candidate solution corresponding to an N-dimensional body extracted from the N-dimensional input datasets; defining a distance metric that compares the N-dimensional body formed by the candidate solution extracted from the N-dimensional input datasets and the N-dimensional body formed by the pattern query, taking into account one or more of the following criteria: differences between mapped values, differences in scale and differences in shape; and executing, in parallel, a plurality of independent instances of at least one algebraic operator to generate and score the candidate solutions based on the distance metric. The exemplary algebraic operators comprise a Trace Match operator, a Ranking operator, a Candidate Solution operator, and a Query Clustering operator.

Methods and apparatus for parallel evaluation of pattern queries over large N-dimensional datasets to identify features of interest

Techniques are provided for automatic placement of cache operations in a dataflow. An exemplary method obtains a graph representation of a dataflow of operations; determines a number of executions and a computational cost of the operations, and a computational cost of a caching operation to cache a dataset generated by an operation; establishes a dataflow state structure recording values for properties of the dataflow operations for a number of variations of caching various dataflow operations; determines a cache gain factor for dataflow operations as an estimated reduction in the accumulated cost of the dataflow by caching an output dataset of a given operation; determines changes in the dataflow state structure by caching an output dataset of a different operation in the dataflow; and searches the dataflow state structures to determine the output datasets to cache based on a total dataflow execution cost.

Automatic placement of cache operations for complex in-memory dataflows

An architecture, methods and apparatus are provided for managing sensor data. Sensor networks comprised of a plurality of sensors are managed by obtaining measurement data and context data from the plurality of sensors; storing the obtained measurement data and context data using a Massively Parallel Processing Database Management System (MPP DBMS); and managing the sensor network from outside of the sensor network using the MPP DBMS. Context-aware adaptation of sensors is based on context regarding a state of the sensor network and context regarding a state of one or more applications. The sensor nodes are optionally clustered based on semantic similarities among sensor readings from different sensor nodes and a distance among the sensor nodes. A subset of the sensor nodes is optionally selected to be active based on a residual energy of the sensor nodes and a relevance of the sensor nodes to an application. Data prediction models are generated and employed for data sensing and analytics.

Integrated wireless sensor network (WSN) and massively parallel processing database management system (MPP DBMS)

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Rio de Janeiro, Brazil

Paulo De Figueiredo Pires