A power management system including a management apparatus configured to assign divided computation processing constituting at least a part of predetermined computation processing to a distributed computing device placed in a facility, wherein the management apparatus includes a controller configured to perform assignment processing configured to assign the divided computation processing to the distributed computing device based on at least one of a prediction value of an output power of a distributed power supply placed in the facility, a prediction value of power consumption of the facility, and a prediction value of a surplus power of the facility.

A power management system including a management apparatus configured to assign divided computation processing constituting at least a part of predetermined computation processing to a distributed computing device placed in a facility, wherein the management apparatus includes a receiver configured to receive a message including an information element indicating executability of computation processing by the distributed computing device, and a controller configured to perform assignment processing configured to assign the divided computation processing to the distributed computing device based on the executability of the computation processing.

Embodiments described herein are generally directed to improving predictions regarding workload performance to facilitate dynamic auto device selection. In an example, based on telemetry samples collected from a computer system in real-time and indicative of a state of the computer system, one or more workload performance prediction models are built or updated for a heterogeneous set of computer resources of the computer system with reference to one or more optimization goals. At a time of execution of a workload, a particular computer resource of the heterogeneous set of computer resources on which to dispatch the workload is dynamically determined by: (i) generating multiple predicted performance scores each corresponding to one of the computer resources based on the state of the computer system and the one or more workload performance prediction models; and (ii) selecting the particular computer resource based on the predicted performance scores.

Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.

A master electronic apparatus, an electronic apparatus, and a controlling method thereof where the master electronic apparatus includes a communication interface and a processor. The processor receives first data and second data regarding predicted power consumption amounts corresponding to respective tasks of a first electronic apparatus and a second electronic apparatus, calculates summed-up values of the predicted power consumption amounts for respective times, and compares the summed-up values with instantaneous power amount limits for the respective times. The processor, based on the summed-up values being smaller than the instantaneous power amount limits, transmits a task approval signal to the second electronic apparatus, and based on identifying a time a summed-up value is greater than or equal to the instantaneous power amount limit, transmits a control signal controlling an operation in the identified time to at least one of the first electronic apparatus and the second electronic apparatus based on priorities.

Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.

In one embodiment, a processor includes: a plurality of cores each comprising a multi-threaded core to concurrently execute a plurality of threads; and a control circuit to concurrently enable at least one of the plurality of cores to operate in a single-threaded mode and at least one other of the plurality of cores to operate in a multi-threaded mode. Other embodiments are described and claimed.

Systems and methods are disclosed for scheduling threads on a processor that has at least two different core types, such as an asymmetric multiprocessing system. Each core type can run at a plurality of selectable voltage and frequency scaling (DVFS) states. Threads from a plurality of processes can be grouped into thread groups. Execution metrics are accumulated for threads of a thread group and fed into a plurality of tunable controllers for the thread group. A closed loop performance control (CLPC) system determines a control effort for the thread group and maps the control effort to a recommended core type and DVFS state. A closed loop thermal and power management system can limit the control effort determined by the CLPC for a thread group, and limit the power, core type, and DVFS states for the system. Deferred interrupts can be used to increase performance.

A method for managing data includes obtaining, by a first data node, a notification, wherein the first data node is associated with a first power zone group (PZG), and in response to the notification: selecting a second data node, wherein the second data node is not associated with the first PZG, sending a data processing request to the second data node, obtaining a response based on the data processing request, wherein the response specifies a confirmation by the second data node to service the data processing request, storing a ledger entry in a ledger service that indicates the confirmation, and initiating a data transfer based on the data processing request, wherein the first data node is associated with the PZG based on a primary power source of the first data node.

In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may determine first thermal attribute values associated with multiple information handling systems (IHSs) with respect to a period of time as the IHSs execute a first workload; determine multiple variance ranges respectively associated with the first thermal attributes; periodically determine second thermal attribute values associated with the IHSs as the IHSs execute a second workload; determine that a thermal attribute value of the second thermal attribute values exceeds a respective variance range of the variance ranges as a first information handling system (IHS) of the IHSs executes the second workload; generate an alert based at least on the thermal attribute value exceeding the respective variance range; and in response to the alert, transfer at least a portion of the second workload from the first IHS to a second IHS of the IHSs.