A computation offloading method and an apparatus. In network edge computation offloading, an edge node receives states of computational tasks sent by one or more served terminal devices and determines allocation of computing resources based on received states of one or more computational tasks. Then, the edge node broadcasts the allocation of the computing resources to the served terminal devices, and the terminal devices each determine, based on the resource allocation, whether to offload the computational task to the edge node for computing. Therefore, the edge node and the terminal device each can have a wider capability of sensing an environment in actual decision-making, thereby effectively improving decision-making benefits of the edge node and the terminal device.

A software-defined infrastructure can identify and remediate an airflow deficiency scenario on a rack device. A rack device manager can be configured to discover rack devices and create a representation of their physical locations. The rack device manager can also be configured to periodically retrieve airflow metrics of the rack devices to calculate an estimated airflow for each rack device. The rack device manager can use the estimated airflows and the airflow metrics to generate a rack device classifier for each rack device. Using these rack device classifiers, the rack device manager can detect when rack devices are experiencing airflow deficiencies and attempt to automatically remediate such deficiencies.

An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

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.

A cooling-power-consumption-based workload allocation system includes a workload allocation system coupled to at least one client device and a plurality of server devices. The workload allocation system receives a first workload request that identifies a first workload from the at least one client device, and determines a first workload priority of the first workload relative to a second workload priority of each second workload being performed by the plurality of server devices. Based on the first workload priority of the first workload relative to the second workload priority of each second workload and a cooling-power-utilization-efficiency ranking of each of the plurality of server devices, the workload allocation system identifies a first server device included in the plurality of server devices for performing the first workload, and causes the first server device to perform the first workload.

A non-transitory computer-readable recording medium stores a program that causes a computer to execute a process that includes receiving load arrangement of jobs in a case where a compute server mounted in a compute rack in a server room executes the jobs, the server room being a room where the compute rack in which the compute server is mounted and a storage rack in which a storage is mounted are arranged, and estimating a time at which a predetermined job of the compute server is to be offloaded to the storage that generates less heat than the compute server and estimating setting temperature and an air volume of an air conditioner, based on the load arrangement and time-series data of temperature and power of the server room, such that the power of the server room is reduced within limitation conditions of the compute server, the storage, and the air conditioner.

A power management method for an electronic device is provided. The electronic device includes a processing unit with a core and configured to execute an application program and a functional element. The power management method includes the following steps: determining a maximum frame count per second of a scene; down-tuning a frequency setting value of the core; detecting an actual frame count per second of the scene; determining a change in power consumption of the processing unit and a temperature of the functional element when the actual frame count per second is equal to the maximum frame count per second; and down-tuning the frequency setting value when the power consumption does not increase and the temperature is lower than a preset temperature value, and restoring the frequency setting value when the power consumption increases or the temperature is higher than or equal to the preset temperature value.

In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may determine a temperature value within a chassis of an information handling system (IHS); may determine an ambient temperature value outside the chassis based at least on the temperature value within the chassis; may provide the ambient temperature value outside the chassis to a process executing on the IHS; may determine a threshold temperature value associated with at least one processor of the IHS based at least on the ambient temperature value outside the chassis and based at least on a target skin temperature value of the IHS; and may configure the at least one processor based at least on the threshold temperature value associated with the at least one processor.

A datacenter includes a datacenter efficiency management system coupled to node devices. For each of the node devices and based on a power consumption associated with that node device and a performance associated with that node device, the datacenter efficiency management system generates a node group ranking that it uses to group subsets of the node devices into respective homogenous node groups, and then deploys a respective workload on at least one node device in each of the homogenous node groups. Based on at least one of a node workload bandwidth, a node power consumption, and a node health of each node device on which a workload was deployed, the datacenter efficiency management system then generates a workload performance efficiency ranking of the node devices that it then uses to migrate at least one workload between the node devices.

A datacenter includes a heterogeneous node group efficiency management system that is coupled to node devices and that, based on a power consumption and performance associated with each node device, generates node group rankings that it uses to group subsets of the node devices into respective heterogeneous node groups. The heterogeneous node group efficiency management system then identifies workload characteristic(s) and performance requirement(s) for a workload provided for deployment, identifies a first heterogeneous node group that satisfies the performance requirement for the workload, and identifies first node device(s) that are included in the first heterogeneous node group and that are configured to perform the first workload having the workload characteristic(s) with a higher power efficiency than second node device(s) that are included in the first heterogeneous node group. The heterogeneous node group efficiency management system then deploys the workload on the first node device(s) in the first heterogeneous node group.