A method, system, and computer program product for detecting and monitoring a signal is provided. The method includes detecting an alignment point for a periodic signal segment of a periodic signal generated by an apparatus being monitored for standard functionality In response, the apparatus is activated from a period prior to the alignment point to an end point of the periodic signal segment and a first point of the periodic signal segment is located. Likewise, a second point of an additional periodic signal segment of the periodic signal is located. The periodic signal is normalized based on results of locating the first point and the second point.

Systems and methods for intelligent fan identification are described. In some embodiments, an Information Handling System (IHS) may include: an embedded controller (EC); and a memory coupled to the EC, the memory having program instructions stored thereon that, upon execution by the EC, cause the IHS to: detect a cooling fan configuration issue; determine that a number of cooling fans in the IHS has not changed between a previous configuration and a current configuration; and in response to the determination, abstain from identifying the cooling fan configuration issue as a cooling fan error.

Automatic part testing includes: booting a part under testing into a first operating environment; executing, via the first operating environment, one or more test patterns on the part; performing a comparison between one or more observed characteristics associated with the one or more test patterns and one or more expected characteristics; and modifying one or more operational parameters of a central processing unit of the part based on the comparison.

Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Systems and methods for intelligent fan identification are described. In some embodiments, an Information Handling System (IHS) may include: an embedded controller (EC); and a memory coupled to the EC, the memory having program instructions stored thereon that, upon execution by the EC, cause the IHS to: detect a cooling fan configuration issue; determine that a number of cooling fans in the IHS has not changed between a previous configuration and a current configuration; and in response to the determination, abstain from identifying the cooling fan configuration issue as a cooling fan error.

Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

A computer-implemented method is carried out on an IT framework and a relative apparatus including: an orchestrator module; an optimizer module; a configurator module; a load generator module; and a telemetry module. The method includes: identifying tunable parameters representing a candidate configuration for a System Under Test (SUT), and applying the candidate configuration to the SUT using the configurator module; performance testing the SUT to determine a performance indicator; supplying performance metrics to the optimizer module's machine learning model to generate an optimized candidate configuration. The model provides as output, in correspondence of a candidate set of parameters, an expected value of the performance indicator and a prediction uncertainty thereof, used by the optimizer module to build an Acquisition Function used to derive a candidate configuration and by the load generator module to build the test workload. The test workload is computed through the machine learning model.

Systems and methods for lossless restoration of a digital system are provided. A method may include creating a digital twin of the digital system. Creating the digital twin may include constructing a digital model that replicates hardware and software components and performance metrics of the digital system. The components and the performance metrics may be detected via a plurality of edge devices. The digital model may be configured to be run on a processor to simulate performance of the digital system. The method may include receiving an indication that the digital system is disconnected from a central server, syncing the digital twin with the digital system while the digital system is disconnected from the central server, and, in response to an indication that the digital system has reconnected with the central server, syncing the central server with the digital twin.

Cloud simulation or validation system allows for the simulation of a future node that may be deployed on a piece of hardware. The system may attempt to simulate the operating system for node-A on top of the hardware for node-A, including basic network connectivity. When a host is booted up with the simulated configuration, validation scripts may be run to verify that the site is correctly prepped for cloud deployment. With its pre-staged RAM-based OS temporarily loaded into the host's RAM memory, any set of OS-based scripts, tools or binaries, may be executed for simulation and validation based upon the intended role of the host onto which the cloud simulation or validation system configuration is loaded.

Cloud simulation or validation system allows for the simulation of a future node that may be deployed on a piece of hardware. The system may attempt to simulate the operating system for node-A on top of the hardware for node-A, including basic network connectivity. When a host is booted up with the simulated configuration, validation scripts may be run to verify that the site is correctly prepped for cloud deployment. With its pre-staged RAM-based OS temporarily loaded into the host's RAM memory, any set of OS-based scripts, tools or binaries, may be executed for simulation and validation based upon the intended role of the host onto which the cloud simulation or validation system configuration is loaded.