公开(公告)号：US11644882B2

公开(公告)日：2023-05-09

申请号：US17337107

申请日：2021-06-02

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Harumi Kuno ,  Alan Davis ,  Torsten Wilde ,  Daniel William Dauwe ,  Duncan Roweth ,  Ryan Dean Menhusen ,  Sergey Serebryakov ,  John L. Byrne ,  Vipin Kumar Kukkala ,  Sai Rahul Chalamalasetti

IPC: G06F1/3206 ,  G06F1/30 ,  H02J3/00 ,  G06F1/18

CPC classification number: G06F1/305 ,  G06F1/188 ,  G06F1/3206 ,  H02J3/003

Abstract: One embodiment provides a system and method for predicting network power usage associated with workloads. During operation, the system configures a simulator to simulate operations of a plurality of network components, which comprises embedding one or more event counters in each simulated network component. A respective event counter is configured to count a number of network-power-related events. The system collects, based on values of the event counters, network-power-related performance data associated with one or more sample workloads applied to the simulator; and trains a machine-learning model with the collected network-power-related performance data and characteristics of the sample workloads as training data 1, thereby facilitating prediction of network-power-related performance associated with a to-be-evaluated workload.

公开(公告)号：US20220390999A1

公开(公告)日：2022-12-08

申请号：US17337107

申请日：2021-06-02

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Harumi Kuno ,  Alan Davis ,  Torsten Wilde ,  Daniel William Dauwe ,  Duncan Roweth ,  Ryan Dean Menhusen ,  Sergey Serebryakov ,  John L. Byrne ,  Vipin Kumar Kukkala ,  Sai Rahul Chalamalasetti

IPC: G06F1/30 ,  G06F1/3206 ,  G06F1/18 ,  H02J3/00

Abstract: One embodiment provides a system and method for predicting network power usage associated with workloads. During operation, the system configures a simulator to simulate operations of a plurality of network components, which comprises embedding one or more event counters in each simulated network component. A respective event counter is configured to count a number of network-power-related events. The system collects, based on values of the event counters, network-power-related performance data associated with one or more sample workloads applied to the simulator; and trains a machine-learning model with the collected network-power-related performance data and characteristics of the sample workloads as training data 1, thereby facilitating prediction of network-power-related performance associated with a to-be-evaluated workload.