Abstract: 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.

Abstract: 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.

Abstract: Multi-agent reinforcement learning-based workload placement is disclosed. A placement engine is configured to use the state of a system and actual rewards to generate expected rewards that correspond to actions. Agents can take actions for corresponding workloads based on the expected rewards output by the placement engine. This allows workloads to be placed in a manner that conservers power relative to load placement policies while helping avoid service level agreement violations.

Abstract: One example method includes determining a respective metric value for each computing resource in a group of computing infrastructures, and each metric value comprises a length of a Euclidean projection of a required resources vector in a respective vector of available resources provided by one of the computing infrastructures. Next, a list is created that includes the computing infrastructures, and the list is sorted according to one of two criteria. A restricted candidates list is created that is a subset of the list, and one of the computing infrastructures is randomly selected from the restricted candidates list. Finally, the method includes executing, with whichever computing infrastructure was randomly selected, a workload associated with the required resources vector.

Abstract: 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.

Abstract: 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.

Abstract: One example method includes deploying a discriminator, where the discriminator is trained to recognize an adversarial image received by the discriminator as adversarial, and the adversarial image is generated based upon an original image, the adversarial image including a perturbation that cannot be detected by a human eye but which is effective to deceive an image segmentation model to misclassify the original image, receiving, by the discriminator, an image captured by an autonomous vehicle, and determining, by the discriminator, whether the image received from the autonomous vehicle is adversarial.

Abstract: One example method includes creating an ILP model that includes a delay model, an energy model, and a QoS model, and modeling, using the integer linear programming model, a VNF placement problem as an ILP problem, and the modeling includes: using the delay model to identify propagation, transmission, processing, and queuing, delays implied by enabling an instance of the VNF at an edge node to accept a user VNF call; using the energy model to identify energy consumption implied by enabling an instance of the VNF at an edge node to accept a user VNF call; and using the QoS model to identify end-to-end delay, bandwidth consumption, and jitter, implied by enabling an instance of the VNF at an edge node to accept a user virtual network function call. The problem modeled by the ILP model may be resolved by a heuristic method.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An information handling system includes storage and a processor. The processor identifies an occurrence of an inference model update event; in response to identifying the inference model update event: generates an inference model update package; provides the inference model update package to an entity that generated an inference model used by the information handling system; obtains, from the entity, a hybrid data adapted inference model that is based on the inference model, the inference model update package, and labeled data used to train the inference model; and obtains an inference, using the hybrid data adapted inference model, that indicates a feature is present in collected data.

Abstract: 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.

Abstract: 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.

Abstract: 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 ‘s’ is greater than a predetermined threshold length ‘k,’ performing a first shift of an LRU position to define a protected queue of the cache, initializing a counter with a value of ‘r’, decrementing the counter each time a requested page is determined to be included in the protected queue, until ‘r’=0, and performing a second shift of the LRU position.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An efficient method for generating asset backup scheduling plans. Within a data protection environment, at least two service level metrics may be observed—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.