Dell Products Inc.

Vaishnavi Suchindran

Ravi Shekhar Singh

Balamurugan Gnanasambandam

Arun Muthaiyan

Douglas Evan Messick

Craig Anthony Klein

Tamilarasan Janakiraman

Santosh Kumar Sahu

Sreeram Muthuraman

Alexander J Hoganson

Charu Lata Ojha

Venkata Nagesh Babu Doddapaneni

Vineet Kumar Pandey

Sherwin Lewis

Effectively reading multiple temperature sensors on memory modules may include reading a first of a plurality of temperature sensors on each memory module of a plurality of memory modules, reading a second of the plurality of temperature sensors on each memory module, after reading the first sensors on each memory module, and, if present, reading a third of the plurality of temperature sensors on each memory module of the plurality of memory modules, after reading the second of the sensors on each memory module. The first temperature sensors may be reread prior to reading the third temperature sensors, particularly where the first temperature sensors are middle temperature sensors on each memory module. Upon failing to read a particular temperature sensor, neighboring temperature sensors of the particular temperature sensor may be polled, such as (an) other temperature sensor(s) on the same memory module and/or (a) temperature sensor(s) on adjacent memory module(s).

Systems and methods for effective reading of multiple temperature sensors on memory modules

Systems and methods for cognitive thermal and power management of Information Handling Systems (IHSs) are described. In an illustrative, non-limiting embodiment, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: receive temperature information from a plurality of Power Supply Unit (PSU) thermal zones within an enclosure of the IHS, identify, based at least in part upon the temperature information, that a first PSU thermal zone is cooler than a second PSU thermal zone, or that the second PSU thermal zone is hotter than the first PSU thermal zone, and, in response to the identification, set a first PSU located in the first PSU thermal zone to active mode and set a second PSU in the second PSU thermal zone to standby.

Cognitive thermal and power management of information handling systems (IHSs)

According to one embodiment, a computing environment having multiple Information Handling Systems (IHSs), each having one or more field replaceable units (FRUs) and a ledger of a blockchain. Each IHS includes computer-executable instructions to receive a signal indicating that one of the FRUs has been removed, obtain unique identifying information about the removed FRU, and generate a new block to the blockchain indicating that the one FRU has been removed from the IHS. Additionally, the instructions may receive a signal indicating that another FRU has been added, obtain unique identifying information about the added FRU, and generate a new block to the blockchain indicating that the one FRU has been added to the IHS.

System and method for device management of information handling systems using cryptographic blockchain technology

Methods and systems for power management are disclosed. The disclosed power management method and systems may improve the likelihood of data processing systems providing desired computer implemented services while meeting power budget goals and/or other types of goals regarding power consumption, use, and/or provisioning. To improve the likelihood of the power budgets being met, the system may dynamically update power allocations to various components of data processing systems. The power allocations may be dynamically allocated by predicting how changes in existing power allocations may impact the ability of the data processing system to service power allocation requests. If it appears that changes in one or more existing power allocations may allow a power allocation request to be serviced, then the power allocations may be dynamically reallocated to free allocable power. The freed allocable power may be used to service the power allocation.

Method for dynamic feature enablement based on power budgeting forecasting

Methods, apparatus, and processor-readable storage media for automated device power conservation using machine learning techniques are provided herein. An example computer-implemented method includes obtaining usage-related data from one or more processing devices; determining at least one usage pattern for the one or more processing devices by processing the obtained usage-related data using one or more machine learning techniques; automatically generating, based at least in part on the at least one determined usage pattern, instructions pertaining to controlling one or more power states of the one or more processing devices; and performing at least one automated action based at least in part on the generated instructions.

Automated device power conservation using machine learning techniques

Methods, systems, and computer programs encoded on computer storage medium, for determining that an update of the PSU firmware is received for a plurality of PSUs of a data center; determining that an update priority flag indicates unthrottled operation of the data center, and in response: determining that a number of PSUs of the data center is greater than a minimum number of PSUs for the unthrottled operation of the data center, and in response, applying the update to each of the PSUs; determining that the number of PSUs of the data center is less than the minimum number of PSUs for the unthrottled operation of the data center, and in response: adjusting a power capacity and maximum current state of the data center, and after adjusting the power capacity and the maximum current state of the data center, applying the update to each of the PSUs.

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Bengaluru, India

Vaishnavi Suchindran