公开(公告)号：US12001932B2

公开(公告)日：2024-06-04

申请号：US16939237

申请日：2020-07-27

Applicant: Intel Corporation

Inventor： Zhu Zhou ,  Xiaotian Gao ,  Chris MacNamara ,  Stephen Doyle ,  Atul Kwatra

IPC: G06N3/006 ,  G06F1/3287 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06N3/006 ,  G06F1/3287 ,  G06N5/04 ,  G06N20/00

Abstract: Methods and apparatus for hierarchical reinforcement learning (RL) algorithm for network function virtualization (NFV) server power management. A first RL model at a first layer is trained by adjusting a frequency of the core of processor while performing a workload to obtain a first trained RL model. The trained RL model is operated in an inference mode while training a second RL model at a second level in the RL hierarchy by adjusting a frequency of the core and a frequency of processor circuitry external to the core to obtain a second trained RL model. Training may be performed online or offline. The first and second RL models are operated in inference modes during online operations to adjust the frequency of the core and the frequency of the circuitry external to the core while executing software on the plurality of cores of to perform a workload, such as an NFV workload.

公开(公告)号：US12210434B2

公开(公告)日：2025-01-28

申请号：US16914305

申请日：2020-06-27

Applicant: Intel Corporation

Inventor： Bin Li ,  Ren Wang ,  Kshitij Arun Doshi ,  Francesc Guim Bernat ,  Yipeng Wang ,  Ravishankar Iyer ,  Andrew Herdrich ,  Tsung-Yuan Tai ,  Zhu Zhou ,  Rasika Subramanian

IPC: G06F11/34 ,  G06F9/50 ,  G06F18/21 ,  G06N20/00

Abstract: An apparatus and method for closed loop dynamic resource allocation. For example, one embodiment of a method comprises: collecting data related to usage of a plurality of resources by a plurality of workloads over one or more time periods, the workloads including priority workloads associated with one or more guaranteed performance levels and best effort workloads not associated with guaranteed performance levels; analyzing the data to identify resource reallocations from one or more of the priority workloads to one or more of the best effort workloads in one or more subsequent time periods while still maintaining the guaranteed performance levels; reallocating the resources from the priority workloads to the best effort workloads for the subsequent time periods; monitoring execution of the priority workloads with respect to the guaranteed performance level during the subsequent time periods; and preemptively reallocating resources from the best effort workloads to the priority workloads during the subsequent time periods to ensure compliance with the guaranteed performance level and responsive to detecting that the guaranteed performance level is in danger of being breached.