Emc IP Holding Company LLC

Dell Products Inc.

Hugo De Oliveira Barbalho

Tiago Salviano Calmon

Vinicius Michel Gottin

Jonas Furtado Dias

Eduardo Vera Sousa

Rômulo Teixeira De Abreu Pinho

Roberto Nery Stelling Neto

Pablo Nascimento Da Silva

Vinícius Michel Gottin

Alex Laier Bordignon

Daniel Sadoc Menasché

Romulo D Pinho

Paulo Abelha Ferreira

An edge information handling system (IHS) manager includes a storage for storing a labeled data associated with a use counter; and a vehicle counter; and a processor. The processor is programmed to: update an inference module using the labeled data, determine, after the updating, whether the use counter of the labeled data has exceeded a current use threshold, and in response to the use counter of the labeled data exceeding the current use threshold, initiating replacing of the labeled data with new labeled data from a central IHS. The current use threshold is based on the vehicle counter.

System and method for decentralized distributed model adaptation

One example method includes dynamically monitoring a stream of image portions that have been classified by a segmentation model of a video surveillance system, evaluating the image portions, based on the evaluating, determining that an attack on the video surveillance system is occurring, or has occurred, and implementing, or causing the implementation of, a remedial action with regard to the attack. The image portions may be image portions that have been classified by a segmentation model.

Delayed inference attack detection for image segmentation-based video surveillance applications

One example method includes gathering, by a domain adversarial neural network model deployed in an autonomous vehicle operating in a domain, a dataset that comprises unsegmented and unlabeled image data about the domain, sampling the dataset to create an adapted domain dataset, detaching a domain classifier from the domain adversarial neural network, using the domain classifier as a domain change detector model to predict a class of the unsegmented and unlabeled image data in the adapted domain dataset, and based on the class, either: determining that the domain is changed or is unknown; or, determining that the domain has not changed.

Detecting domain changes with domain classifiers in autonomous vehicles

A method for managing data includes obtaining, by a service function chain (SFC) orchestrator, a SFC request for a SFC, wherein the SFC comprises at least one virtual network function (VNF) and one service, in response to the SFC request: determining a set of candidate local data systems (LDSs) based on a resource availability mapping, performing a LDS analysis on the set of candidate LDSs, based on the LDS analysis: assigning the VNF to a candidate LDS of the set of candidate LDSs, assigning the service to a second LDS of the set of candidate LDSs, and based on the assigning of the VNF and the assigning of the service, initiating a deployment of the VNF and the service.

Method and system for a proactive assignment of virtual network functions in local data systems

A method includes obtaining, by a local data system manager of a local data system of the local data systems, a portion of unlabeled data from a local data source, performing, using a domain classifier in the local data system manager, a domain classification analysis on the portion of the unlabeled data to identify a domain of the unlabeled data, making a first determination, based on the domain classification, that the domain classification has significantly varied from a previous domain, based on the first determination: performing an adaptive procedure on a local data system image segmentation model to obtain an adapted image segmentation model, and performing a domain reclassification on the domain classifier to obtain an updated domain classifier, and implementing the adapted image segmentation model on the local data system.

Method and system for a fast adaptation for image segmentation for autonomous edge vehicles

A method and system for assessing sequentiality of a data stream is disclosed. Specifically, the method and system disclosed herein may entail receiving an incoming request to access a page in a cache memory, wherein the page is identified by a page address of an address space in a main memory; identifying, in a memory, a bin corresponding to an address range including the page address of the page of the incoming request, wherein the bin includes k address ranges of the address space of the main memory; determining whether to update an occupation count of the bin in the memory; locating the bin in a heuristics table to obtain an estimated total number of expected proximal accesses based on an updated occupation count of the bin; and determining, based on the estimated total number of expected proximal accesses, sequentiality of the data stream to device in order to generate a policy for the cache memory.

Online heuristic sequentiality detection over input/output streams for cache systems in large address spaces

Techniques are provided for predicting a time to complete a data protection operation. One method comprises obtaining metadata for (i) a given data protection appliance, and/or (ii) a cluster of similar data protection appliances comprising the given data protection appliance; evaluating first level features using the obtained metadata; evaluating a second level feature using some of the evaluated first level features; and processing one or more of the first level features, and the second level feature, using a model that provides a predicted time to complete a data protection operation with respect to data of a protected device associated with the given data protection appliance. The predicted time may comprise a tolerance based on a robustness factor. The predicted time may be based on a number of protected devices that are concurrently undergoing a data protection operation with the protected device for one or more time intervals.

Artificial intelligence techniques for prediction of data protection operation duration

One example method includes identifying a group of asset backups to be performed, and each asset backup is associated with a respective asset and has an associated backup time and RPO, selecting an asset backup to run first, and the asset backup that will run first is chosen based on a start deadline of that asset backup relative to respective start deadlines of one or more other asset backups, and the start deadline falls within a time slot, selecting a stream from a group of streams for the selected asset backup, and the selected stream is a stream with a lowest value of first available time slot, and backing up the asset at a backup server by running the selected asset backup, and backup begins at a start time that is a time when the selected stream becomes available, and the asset backup runs on the selected stream.

Efficient method to build a backup scheduling plan for a server

A backup orchestrator for providing backup services to entities includes storage for storing recovery point objectives for the entities and a backup manager. The backup manager selects an optimization periodicity based a number of backups to be generated to meet a portion of the recovery point objectives; makes a determination that at least one of the portion of the recovery point objectives has a maximum allowable unbacked up period of time that is greater than the optimization periodicity; in response to the determination: load balances the number of backups across multiple optimization periods, based on the optimization periodicity, of a balanced backup schedule; selects a backup generation time for each of the to be generated backups in each of the optimization periods of the balanced backup schedule; and generates the number of backups using the balanced backup schedule.

Iterative integer programming with load balance for cyclic workloads

One example method includes determining a fall through threshold value for a cache, computing a length 's' of a sequence that is close to LRU eviction, and the length 's' is computed when a current fall through metric value is greater than the fall through threshold value, when the sequence length &#x2018;s&#x2019; is greater than a predetermined threshold length &#x2018;k,&#x2019; performing a first shift of an LRU position to define a protected queue of the cache, initializing a counter with a value of &#x2018;r&#x2019;, decrementing the counter each time a requested page is determined to be included in the protected queue, until &#x2018;r&#x2019;=0, and performing a second shift of the LRU position.

Sequence thrashing avoidance via fall through estimation

An efficient method for generating asset backup scheduling plans. Within a data protection environment, at least two service level metrics may be observed&#x2014;a recovery point object (RPO) and a recovery time objective (RTO). In order to meet acceptable values for these metrics, on par with established service level agreements, infrastructure employed throughout the data protection environment, as well as the scheduling of asset backup operations, tend to grow in complexity. To address service distributions potentially emerging from the aforementioned complexities, the disclosed method proposes a heuristic approach to generating asset backup scheduling plans, which consider factors such as backup device limitations, RPO violation minimization, asset usage, and asset prioritization.

Efficient method for generating asset backup scheduling plans

An information handling system for managing a storage system includes storage for storing profile-based cache policy performance prediction models. The information handling system also includes a storage manager that obtains an input-output profile for a workload hosted by the information handling system during a first period of time; obtains performance metrics for cache policies for the storage system using: the input-output profile, and the profile-based cache policy performance prediction models; obtains a ranking of the cache policies based on the performance metrics; selects a cache policy of the cache policies based on the rankings; and updates operation of a cache of the storage system based on the selected cache policy for a second period of time.

System and method for cache management

A method for managing storage devices includes obtaining a storage device cluster request, and in response to the storage device cluster request: obtaining a set of storage device telemetry entries associated with a plurality of storage devices, performing a telemetry normalization on the storage device telemetry entries to obtain a set of normalized entries, performing a pairwise evaluation on the set of normalized entries to obtain a set of initial storage device clusters, wherein a storage device cluster in the set of initial storage device clusters comprises a portion of the plurality of storage devices, performing a cluster re-evaluation on the set of initial storage device cluster groups to obtain a set of updated storage device clusters, updating a backup policy based on the set of updated storage device cluster groups, and performing a backup operation on a storage device based on the backup policy.

System and method for managing backup operations of storage devices in a backup storage system using cluster evaluations based on usage telemetry

Managing a cache memory in a storage system includes maintaining a queue that stores data indictive of the read requests for a particular logical storage unit of the storage system in an order that the read requests are received by the storage system, receiving a read request for a particular page of the particular logical storage unit, and removing a number of elements in the queue and resizing the queue in response to the queue being full. Managing the cache memory also includes placing data indicative of the read request in the queue, determining a prefetch metric that varies according to a number of adjacent elements in a sorted version of the queue having a difference that is less than a predetermined value and greater than zero, and prefetching a plurality of pages that come after the particular page sequentially if the prefetch metric is greater than a predefined value.

Lifetime adaptive efficient pre-fetching on a storage system

A method for characterizing workload sequentiality for cache policy optimization includes maintaining an IO trace data structure having a rolling window of IO traces describing access operations on addresses of a storage volume. A page count data structure is maintained that includes a list of all of the addresses of the storage volume referenced by the IO traces in the IO trace data structure. A list of sequences data structure is maintained that contains a list of all sequences of the addresses of the storage volume that were accessed by the IO traces in the IO trace data structure. A sequence lengths data structure is used to correlate each sequence in the list of sequences data structure with a length of the sequence, and a histogram data structure is used to correlate sequence lengths and a number of how many of sequences of each length are maintained in the sequence lengths data structure.

Growing community of inventors

Rio de Janeiro, Brazil

Hugo De Oliveira Barbalho