Machine learning-based server energy consumption prediction method and system

A technology of machine learning and forecasting method, applied in the field of machine learning, can solve the problem that the accuracy is not as good as the internal performance parameters of the server, and achieve the effect of improving practicability and forecasting accuracy

Active Publication Date: 2018-09-28
INST OF COMPUTING TECH CHINESE ACAD OF SCI
View PDF5 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method is to monitor the external environment of the server and make predictions based on this. The external environmental factor of the temperatur

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Machine learning-based server energy consumption prediction method and system
  • Machine learning-based server energy consumption prediction method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] There are some potential factors leading to model errors in the current mainstream energy consumption prediction modeling method, and the present invention makes targeted improvements and optimizations based on these factors, mainly from the following aspects.

[0035] First consider the selected system parameter indicators, whether it is the program counter or the combination of CPU utilization and memory utilization, their changes can lead to changes in server power, but these parameters are not the only factors that can cause changes in server power. The current research results can only show that these parameters are highly correlated with the actual power of the server, but either the internal system resource module or the external physical hardware environment may have an impact on the real-time power. Therefore, the present invention uses system resource utilization as an input parameter, and expands the number of collected system resource utilization parameters, ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a machine learning-based server energy consumption prediction method and system. The method comprises the following steps: separately collecting system resource utilization rate and real-time energy consumption of a server under a zero load state, a low load state and a high load state, as a zero load training set, a low load training set and a high load training set; respectively, inputting the zero load training set, the low load training set and the high load training set into a machine learning model for training, and generating a zero load energy consumption model, a low load energy consumption model, and a high load energy consumption model; according to the to-be-predicted system resource utilization rate of the server, selecting the zero load energy consumption model, or the low load energy consumption model, or the high load energy consumption model to predict the energy consumption value of the server, wherein the system resource utilization rate comprises the memory utilization rate, the network bandwidth utilization rate, the disk utilization rate, and the CPU utilization rate, thereby improving the prediction accuracy and practicability by expanding the collected system resource utilization rate parameters and the segmentation training models.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a method and system for predicting server energy consumption based on machine learning. Background technique [0002] With the popularization of big data and cloud computing technology and the increase in business volume and data volume, the scale of server clusters is increasing day by day, and the energy consumption generated is also increasing, which leads to an increase in energy consumption costs. Therefore, a cloud operating system Or the optimization of server energy consumption in a data center has become a more important issue in the current technical environment. The traditional method of measuring power is to directly measure the electrical parameters of the server through electrical instruments to obtain the actual power of the server, but this physical measurement method can only obtain accurate actual power, and it is impossible to analyze what caused the ser...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F11/30G06F11/34
CPCG06F11/3024G06F11/3447G06F11/3452Y02D10/00
Inventor 牛逸翔孙毓忠
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products