Abstract: In some implementations, an ingress node may receive first traffic from a host device. The ingress node may determine and based on an ingress filter of the ingress node, that traffic from the host device is allowed. The ingress node may select, using a load balancing functionality, and based on determining that traffic from the host device is allowed, a security node, of a plurality of security nodes, to which the ingress node is to forward the first traffic. The ingress node may forward the first traffic to the selected security node. The ingress node may receive, based on forwarding the first traffic, a message that indicates that traffic from the host device is to be blocked. The ingress node may update, using the message, the ingress filter of the ingress node to indicate that traffic from the host device is to be blocked.

Abstract: Aspects described herein relate to transmitting a scheduling request (SR) for an uplink grant based at least in part on presence of data in a buffer, wherein the SR is scheduled for transmission according to a SR periodicity, and aborting, based at least in part on expiration of a discard timer corresponding to the buffer and/or the buffer being empty, at least a next SR that is scheduled for transmission according to the SR periodicity.

Abstract: A policy level controller coordinates a scheduling and policy engine using a data change metric to dynamically schedule or re-define policies in response to data change rates in data assets in a current backup session. A supervised learning process trains a model using historical data of backup operations of the system to establish past data change metrics for corresponding backups processing the saveset, and modifies policies dictating the backup schedule by determining a data change rate of received data, as expressed as a number of bytes changed per unit of time. In response to input from backup targets regarding present usage, it then modifies the backup schedule to minimize the impact on backup targets that may be at or close to overload conditions.

Abstract: A data protection system protects the data with the appropriate level of security and tiering for storage in data pools. The system includes a content awareness feature that is utilized to classify and analyze the data to be backed up and assigns the classified data to the appropriate data pool dynamically. It uses a classifying software development kit (SDK) or application program interface (API) to classify and analyze the contents. Based on this classification, data is segregated using specific data properties and is compared with a list of data pool properties. Then system then ranks and predicts the pool that is best suited for the classified data and dynamically assigns the data to the pool during backup.

Abstract: Examples disclosed herein involve a computing system configured to (i) obtain (a) a first set of sensor data captured by a first sensor system of a first vehicle that indicates the first vehicle's movement and location with a first degree of accuracy and (b) a second set of sensor data captured by a second sensor system of a second vehicle that indicates the second vehicle's movement and location with a second degree of accuracy that differs from the first degree of accuracy, (ii) based on the first set of sensor data, derive a first trajectory for the first vehicle that is defined in terms of a source-agnostic coordinate frame, (iii) based on the second set of sensor data, derive a second trajectory for the second vehicle that is defined in terms of the source-agnostic coordinate frame, and (iv) store the first and second trajectories in a database of source-agnostic trajectories.

Abstract: Disclosed are various embodiments for rate proportional scheduling to reduce packet loss in virtualized network function chains. A congestion monitor executed by a first virtual machine executed by a host computing device can detect congestion in a receive queue associated with a first virtualized network function implemented by a first virtual machine. The congestion monitor can send a pause signal to a rate controller executed by a second virtual machine executed by the host computing device. The rate controller can receive the pause signal. In response, the rate controller can pause the processing of packets by a second virtualized network function implemented by the second virtual machine to reduce congestion in the receive queue of the first virtualized network function.

Abstract: A method for monitoring cleaning of a medical room includes: receiving imaging of the medical room, the imaging capturing signatures of cleaning deposits on one or more surfaces of the medical room deposited via a cleaning process; analyzing the imaging to: identify one or more surfaces in the medical room that should be cleaned, and determine which of the one or more surfaces have been cleaned by identifying the signatures of the cleaning deposits; and displaying an indication of at least one of: (1) the surfaces that have been cleaned, and (2) one or more surfaces that have not been cleaned.

Abstract: The present disclosure generally relates to a system and method for the simultaneous transport of Crude Oil and Naphtha in a cross-country crude oil pipeline. The system includes the batch/zone of crude blend followed by lighter crude to clean and dissolve the asphaltene content of the crude blend. The naphtha batch/zone is sent securely in between the batch/zone of water. To carry out the cleaning of inner surface of the pipe various types of pigs have been used, the pigs have been designed to scrap the inner surface of the pipe and collect the foreign particle as well. The light crude oil is used to dissolve the asphaltene and resin components of crude blend, pigs are used to clean the surface and as well as a separating barrier in between the two layers.

Abstract: The present invention generally relates to a virtual reality (VR) navigation system using artificial intelligence (AI), thus enabling providing of individualized and context-aware navigation experience in VR environments. It consists of an AI-inclusive central processing unit (CPU) that receives and processes real-time data from multiple hardware sensors deployed in a wearable VR headset, e.g., inertial measurement units (IMUs), eye-tracking sensors, biometric ones. This is AI-driven CPU that dynamically changes the navigation path and sensory outputs such as visual, audio, haptic based on user input/interaction & environmental context. It consists of a hand-held controller with numerous input interfaces and a wireless communication module, enabling it to be easily linked or integrated into several VR applications.

Abstract: Performing changed sector backups on a volume to improve the throughput of incremental backups for block-based backups by overwriting only modified data on each changed block. Latency reduction is achieved by not overwriting the changed block as a whole. The previous backup is cloned and the batch file in the clone is updated by considering the incremental block extents. The incremental data write is performed only on the changed data of each incremental block from a couple of offset values from the previous backup's clone. Dynamic disk rather than differencing disk containers are used for snapshots. Use of a previous backup clone eliminates the need for temporary virtual disk containers, and fixed-size segmentation at the target reduces fragmentation and eliminates the need to enforce variable-sized segmentation.

Abstract: A vulnerability tagging process helps prioritize backups of datasets in a network by monitoring events that affect containers in the network. The monitored events are processed by an AI-based event analyzer to characterize each event in terms of a potential for destruction or damage to the data by each event. A vulnerability measure is calculated as the product of the number of occurrences of each event based on the severity associated with each event. Once the events are analyzed and the vulnerability scores are calculated, the scores are tagged on each workload. The vulnerability tags can then be utilized by the backup server to modify protection policies and/or prioritize backup schedules for the container workloads.

Abstract: An example computer system includes a hardware platform including a processing unit and software executing on the hardware platform. The software includes a workload and a scheduler, the workload including a network function chain having network functions, the scheduler configured to schedule the network functions for execution on the processing unit. A downstream network function includes a congestion monitor configured to monitor a first receive queue supplying packets to the downstream network function, the congestion monitor configured to compare occupancy of the first receive queue against a queue threshold. An upstream network function including a rate controller configured to receive a notification from the congestion monitor generated in response to the occupancy of the first receive queue exceeding the queue threshold, the rate controller configured to modify a rate of packet flow between a second receive queue and the upstream network function in response to the notification.

Abstract: A data protection system protects the data with the appropriate level of security and tiering for storage in data pools. The system includes a content awareness feature that is utilized to classify and analyze the data to be backed up and assigns the classified data to the appropriate data pool dynamically. It uses a classifying software development kit (SDK) or application program interface (API) to classify and analyze the contents. Based on this classification, data is segregated using specific data properties and is compared with a list of data pool properties. Then system then ranks and predicts the pool that is best suited for the classified data and dynamically assigns the data to the pool during backup.

Abstract: A policy level controller coordinates a scheduling and policy engine using a data change metric to dynamically schedule or re-define policies in response to data change rates in data assets in a current backup session. A supervised learning process trains a model using historical data of backup operations of the system to establish past data change metrics for corresponding backups processing the saveset, and modifies policies dictating the backup schedule by determining a data change rate of received data, as expressed as a number of bytes changed per unit of time. In response to input from backup targets regarding present usage, it then modifies the backup schedule to minimize the impact on backup targets that may be at or close to overload conditions.

Abstract: A conflict resolution component uses data change measure to resolve conflicts when data assets with the same priority tags arrive simultaneously for protection processing. The data change measure quantifies the extent of modifications or updates made to the asset since a last backup. Assets with a higher data change measure are assigned a higher priority and processed ahead of others with the same priority tag. This reprioritization ensures that backup objects with more significant data changes are handled first. Such a system overcomes the issues associated with present methods backup queueing methods including random scheduling of data having the same priority tags or classifications.

Abstract: A dynamic replication service using a data change metric to select an optimum cloning method that reduces latency of data copying. A model is trained using historical data of backup operations of the saveset to establish past data change metrics for corresponding replication services processing the saveset. The best cloning method for the replication service is selected by using a calculated data change rate of the data saveset, as expressed as a number of bytes changed per unit of time, from among a plurality of different cloning methods based on the data change rate. The service executes the selected cloning method for the replication service to copy the data for storage or further processing.

Abstract: A data protection system utilizes certain supervised learning processes to implement a granular level prioritized protection scheme for critical data based on decision tree processing. This process provides intelligent backup protection not only at the asset or sub-asset level, but also at the molecular level to meet the desired level of priority-based operation. Such embodiments overcome disadvantages of current systems that provide this level protection only upon management by an administrator by providing automated intelligence to provide granular protection at any appropriate data object level.

Abstract: A dynamic data policy creation process utilizes certain supervised learning processes to classify data criticality for tagged clients to provide dynamic policy definitions and process adhoc (special) backup requests. Such embodiments prevent the need to manually determine criticality and create protection policies whenever a new data object into the system. A data tagger and KNN-based classifier provide an intelligent solution to data protection ecosystems to meet the dynamic request of data objects through a dynamic backup policy creation system and method that uses certain artificial intelligence (AI) and machine learning (ML) based solutions.

Abstract: The backup schedule for a group of routine or known clients and data objects is generally defined by default backup policies and priorities, and such policies and data assets demand resources of the system. The resource allocation among data assets being processed is analyzed and reallocated (tuned) as necessary to ensure that critical data assets are provided with the necessary resources for their protection operations. Embodiments use a machine learning (ML) model that would identify the resource consumption for the all the running policies to smartly allocate the optimal number of resources based on the criticality of the data object and their policies.

Abstract: A container load balancer process helps schedule backups of containerized data based on defined attributes and historical data. Containers are classified using a KNN-based classifier based on attributes. A tagger component assigns a priority tag to each container. A monitor monitors an assignment of backup tasks to proxies for the backing up step, and a load balancer determines if the assignment distributes backup loads within a defined performance tolerance, and adjusts the assignment if not. A backup server then backs up the container data in an order determined by the classifying and the assignment or adjusted assignment.