Power neural network-based workload distribution in distributed computing systems

Abstract

Certain aspects of the present disclosure provide techniques and apparatus for distributing a workload across computing devices within a distributed computing system. An example method generally includes receiving, from at least one respective computing device of a plurality of computing devices in a distributed computing environment, information defining a respective power neural network. The respective power neural network generally is trained to predict power utilization for the respective computing device for a task to be executed on the respective computing device. For one or more computing devices, power utilization is predicted for a workload to be executed within the distributed computing environment based on respective power neural networks associated with the one or more computing devices. Instructions to execute at least a portion of the workload based on the predicted power utilizations for the one or more computing devices are transmitted to the plurality of computing devices.