Provided are a method and an apparatus for reporting and handling an exception in computing power sharing, a storage medium, and a terminal device. The method for reporting an exception in computing power sharing includes: detecting a current hardware state and a current battery state; and reporting an exception to a network unit, in a case that the hardware state or the battery state reaches a preset exception threshold, or in a case that a change of the hardware state or a change of the battery state reaches a preset reporting threshold. The method for handling an exception in computing power sharing includes: receiving an exception reported from a cooperative computing terminal; determining a total workload assigned to the cooperative computing terminal and a remaining workload of the cooperative computing terminal; and determining, based on the exception and the remaining workload, to reassign the remaining workload or the total workload.

Processor core power management in a virtualized environment. A hypervisor, executing on a processor device of a computing host, the processor device having a plurality of processor cores, receives from a guest operating system of a virtual machine, a request to set a virtual central processing unit (VCPU) of the virtual machine to a first requested P-state level of a plurality of P-state levels. Based on the request, the hypervisor associates the VCPU with a first processor core having a P-state that corresponds to the first requested P-state level.

One example method includes performing, in an edge device that includes a power source, operations including monitoring a running process and obtaining, based on the monitoring, power consumption information associated with the running process, adjusting, based on the power consumption information, a priority of the running process, and providing, to an entity, the power consumption information and/or information concerning the priority of the running process.

Technology for estimating neural network (NN) power profiles includes obtaining a plurality of workloads for a compiled NN model, the plurality of workloads determined for a hardware execution device, determining a hardware efficiency factor for the compiled NN model, and generating, based on the hardware efficiency factor, a power profile for the compiled NN model on one or more of a per-layer basis or a per-workload basis. The hardware efficiency factor can be determined on based on a hardware efficiency measurement and a hardware utilization measurement, and can be determined on a per-workload basis. A configuration file can be provided for generating the power profile, and an output visualization of the power profile can be generated. Further, feedback information can be generated to perform one or more of selecting a hardware device, optimizing a breakdown of workloads, optimizing a scheduling of tasks, or confirming a hardware device design.

A method for off-loading tasks between a set of wireless earpieces in an embodiment of the present invention may have one or more of the following steps: (a) monitoring battery levels of the set of wireless earpieces, (b) determining the first wireless earpiece battery level and the second wireless battery level, (c) communicating the battery levels of each wireless earpiece to the other wireless earpiece of the set of wireless earpieces, (d) assigning a first task involving one or more of the following: computing tasks, background tasks, audio processing tasks, and sensor data analysis tasks from one of the set of wireless earpieces to the other wireless earpiece if the battery level of the one of the set of wireless earpieces falls below a critical threshold, (e) communicating data for use in performing a second task to the other wireless earpiece if the second task is communicated to the first wireless earpiece.

An automated improving of quality of service of a data center. Transients of a power grid fed to a power supply unit are monitored by a probe. Information on transients is provided across an interface to a server of the data center. Based on characteristics of the transients, a reliability of the data center is subjected to automated updating. A request for migration of workload requiring a higher reliability than the updated reliability can be sent to a central management. When the central management has identified another data center that can meet the required reliability, the central management migrates or relocates the workload to the another data center.

Disclosed are methods, systems and computer program products for optimizing computer system resource utilization during in-game resource farming. In some non-limiting embodiments or aspects, the present disclosure describes a method for optimizing computer system resource utilization during in-game resource farming, the method including detecting a gameplay state associated with an executing instance of a gaming application and based on the detected gameplay state selecting a gaming application mode from among a plurality of available gaming application modes. In some non-limiting embodiments or aspects, the method may also include implementing the selected gaming application mode for subsequent execution of the gaming application on the computing system.

A time-aware application task scheduling system for a green data center (GDC) that includes a task scheduling processor coupled to one or more queue processors and an energy collecting processor connected to one or more renewable energy sources and a grid power source. The systems is capable of determining a service rate for a plurality of servers to process a plurality of application tasks in the GDC and scheduling, via processing circuitry, one or more of the application tasks to be executed in one or more of the servers at a rate according to a difference in an accumulated arriving rate for the plurality of application tasks into the one or more queues and a removal rate for the plurality of application tasks from the one or more queues. The system is further capable of removing the one or more application tasks from their associated queues for execution in the scheduled one or more servers.

An operating method of a memory system including a memory device including a plurality of memory chips is provided. The operating method includes setting a parameter indicating a number of the memory chips allowed to operate in parallel for each of a plurality of operation statuses, based on information about power consumption of each of the plurality of operation statuses of a memory chip among the memory chips; obtaining information about an operation status of each of the plurality of memory chips; and scheduling data access across a plurality of channels respectively corresponding to the plurality of memory chips, based on the parameter and the information about the operation status of each of the plurality of memory chips.

A data processing system comprises a hybrid processor comprising a big TPU and a small TPU. At least one of the TPUs comprises an LP of a processing core that supports SMT. The hybrid processor further comprises hardware feedback circuitry. A machine-readable medium in the data processing system comprises instructions which, when executed, enable an OS in the data processing system to collect (a) processor topology data from the hybrid processor and (b) hardware feedback for at least one of the TPUs from the hardware feedback circuitry. The instructions also enable the OS to respond to a determination that a thread is ready to be scheduled by utilizing (a) an OP setting for the ready thread, (b) the processor topology data, and (c) the hardware feedback to make a scheduling determination for the ready thread. Other embodiments are described and claimed.