Abstract: A modular information technology component includes: a modular over-rack component; and an information technology component, in which the modular over-rack component includes a set of information technology component openings, in which the set of information technology component openings enables mounting of the information technology component into the modular over-rack component.

Abstract: An information handling system may include one or more information handling resources, a main battery configured to power the one or more information handling resources, a heater thermally coupled to the main battery, a supportive battery configured to power the heater, and a control unit communicatively coupled to the supportive battery and configured to control the supportive battery and the heater to heat the main battery.

Abstract: The cause of a failure to satisfy a high priority service level objective of a storage object or storage group is localized within a storage array. Storage objects that have been assigned low priority service level objectives are analyzed to determine whether their performance paths overlap with the performance path of the high priority service level objective storage object or storage group at the location of the cause of the failure. The low priority service level objective storage objects having performance paths that overlap with the performance path of the high priority service level objective storage object or storage group at the location of the cause of the failure are targeted for IO data rate reduction in order to free resources to restore compliance with the high priority service level objective. The other low priority service level objective storage objects are not targeted.

Abstract: Systems and methods for deploying instances of Artificial Intelligence (AI) models having different levels of complexity in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS), may include a heterogeneous computing platform comprising a plurality of devices and a memory having a plurality of sets of firmware instructions that, upon execution by a respective device, enable the respective device to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: select an instance of an AI model among a plurality of instances of the AI model based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, where each of the plurality of instances of the AI model has a different level of complexity, and instruct a device to execute the selected instance of the AI model.

Abstract: Systems and methods for usage detection and notification in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices and instruct one or more selected devices to execute one or more Artificial Intelligence (AI) models configured to characterize one or more workloads executed by the IHS based, at least in part, upon the context or telemetry data.

Abstract: In a multi-tiered system, read temperature and write temperature per virtual block (VLB) can be determined based on the content referenced by each VLB. The read temperature can describe VLB read activity relative to the recency and frequency of read misses of content referenced by the VLB. The write temperature can describe VLB write activity relative to the recency and frequency of overwrites with respect to content referenced by the VLB. Read misses and overwrites can be recorded per processing core in an active tablet used for recording by only a single core. Active and analytic tablet sets across cores can be switched based on criteria. Analytic tablets can be processed and used to update metrics, including read and write temperatures, stored for VLBs. The metrics can be used to determine temperature classifications of VLBs. Content referenced by VLBs can be promoted and/or demoted using VLB temperature classifications.

Abstract: Customized updates are generated for instances of storage management software running on storage management servers in different storage systems. The filenames of class files used by each instance of the storage management software are stored in respective databases associated with those storage management software instances. Thread dumps are created and searched for class files of the storage management software to create a baseline. The baseline is updated by comparing class files used in response to UI actions and REST API calls and storing names of class files that are not already in the database. To update an instance of the storage management software, the class files of a general update are compared with the filenames in the database associated with the instance and non-matching files are omitted from the customized update foe that instance.

Abstract: Systems and methods for runtime control of Artificial Intelligence (AI) model parameters in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct a device among the plurality of devices to modify a parameter of an AI model executed by the device based, at least in part, upon the context or telemetry data.

Abstract: A display keyboard, video, mouse (KVM) switch associates video and data ports with an information handling system by communicating identifiable information between the KVM switch and information handling system through both video and data ports to confirm which of the data ports is associated with a video port that presents visual images of the information handling system. The identifiable information includes a unique token or virtual mouse inputs communicated as data to an information handling system that are detected in visual information communicated as mouse cursor movements.

Abstract: An information handling system may include a host processor module comprising a host processor field programmable gate array, a management controller communicatively coupled to the host processor field programmable gate array and configured to provide out-of-band management facilities for management of the information handling system, and an interposer configured to interface between the host processor field programmable gate array and one or more peripheral devices in order to perform power management and control of the one or more peripheral device. The interposer may include a general purpose input/output extender configured to enable and control power delivery to the one or more peripheral devices and a microcontroller unit communicatively coupled to the host processor field programmable gate array and configured to perform monitoring and discovery of the one or more peripheral devices.

Abstract: Systems and methods for Artificial Intelligence (AI) model characterization and offloading in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables it to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: receive a characterization result from a first device, where the first device is configured to execute a first AI model configured to characterize a second AI model executed by a second device, and, prior to a subsequent execution of the second AI model, assign the second AI model to a third device based, at least in part, upon the characterization result.

Abstract: A profile of characteristics of a normal snapshot of a storage object is computed from previous snapshots of the storage object. Characteristics of a new snapshot are compared with the characteristics in the profile to identify an anomaly indicative of a malicious attack. The characteristics include percent data changed between consecutive snapshots, write LBA dispersion, write data reducibility, number and size of writes, write workload profile, and write content profile. The new snapshot is only vaulted or air-gapped if all its characteristics are within ranges defined by the profile.

Abstract: A cover assembly may include a cover having a top surface which is generally planar and substantially void of any features formed thereon and an attach and release mechanism mechanically coupled to the top surface and configured to mechanically engage with a corresponding engagement feature of a physical object in order to mechanically retain the cover assembly in a fixed position relative to the physical object.

Abstract: A system can establish a primary file system on a block array of computer storage, wherein the block array comprises a range of addresses, wherein the primary file system is configured to address the range of addresses. The system can establish a shadowed file system on the block array, wherein the shadowed file system is configured to access portions of the block array that are unused by the primary file system. The system can, in response to receiving a request to write data to the primary file system, and in response to determining that an amount of the block array utilized by the primary file system is full, transfer a first portion of the block array utilized by the shadowed file system to the primary file system.

Abstract: In general, one or more embodiments of the invention relates to systems and methods for detecting ransomware attacks earlier and closer to the time of attack. The ransomware attack can be detect by determining a change rate of data blocks between snapshots. The ransomware attack can also be detected by determining the pattern of changes in the blocks deviates from a normal pattern. By making these determinations, a quick identification of possible ransomware attacks can be made and other methods of mitigating the attack can be deployed when they are may still be useful to mitigate potential damage to a user's data.

Abstract: Network fabric design and analysis is generally a manual process where the wiring diagram is manually created and analyzed. Because of the complexity of modern network fabric designs, it has become increasingly more difficult to manually detect potential issues with a network fabric design. Accordingly, embodiments herein help automate the analysis of network fabric designs. In one or more embodiments, a trained neural network model or network models receive as input a wiring diagram and analyzes it. In one or more embodiments, the trained model may generate a real-valued score that represents the quality of the design. In one or more embodiments, the trained neural network may classify a particular issue or issues of the network fabric design.

Abstract: Dynamic determination of a leader node during installation of a multiple node environment is implemented using a shared device and a messaging framework. The leader node is responsible for managing installation of all shared resources. When installation of a multiple node environment is initiated, each node runs a local boot script that governs its local boot process. Each node posts status updates of its boot operations to the messaging framework. Each node also dynamically determines whether it is the leader node by checking the messaging framework to learn whether installation of any of the shared resources has been started. If installation of the shared resources has not started, the node attempts to obtain a lock on the shared device. The node that obtains the lock on the shared device is the leader node. The leader node posts status updates related to installation of the shared resources to the messaging framework.

Abstract: Techniques are provided for load balancing in a storage system using a storage system-driven host connectivity management process. For example, a load balancing process comprises monitoring a distribution of input/output (I/O) workload across resources of a storage system to detect for an occurrence of an I/O workload imbalance, generating updated connection information for a host system connected to the storage system, in response to detecting the occurrence of an I/O workload imbalance, and sending a request to the host system to discover the updated connection information for connecting to the storage system based on the updated connection information.

Abstract: Embodiments of the invention relate to generating backups of applications. The user or administrator that monitors the backup is notified of those files and/or folders that have not been backed up in the most recent backup. Further, embodiments of the invention enable alerts to be initiated when a particular file or folder has not been backed up over multiple backups or over a predetermined period. As a result, the user or administrator can have a better understanding of the protection and lack of protection that the present backups are providing.

Abstract: An autonomous system for detecting primary site failure on a Replication Data Facility (RDF) and automating failover to a destination site includes a replication data facility health monitor on a destination site that monitors a plurality of health indicators of the primary site. Example health indicators include RDF session state of RDF sessions on the replication data facility, and reachability of a set of IP addresses. Example IP addresses include an IP address of a cluster master node on the primary site, IP addresses of all of the cluster nodes on the primary site, and IP addresses of Network Attached Storage (NAS) servers executing on the cluster nodes. The replication data facility health monitor generates a replication configuration and, upon detection of a failure of the replication data facility at the primary site, uses the replication configuration to automate failover of all of the NAS servers to the destination site.