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Cloud Computing Energy Saving Scheduling Implementation Method Based on QoS Feature Discovery

An implementation method, cloud computing technology, applied in computing, energy-saving computing, program control design, etc.

Active Publication Date: 2022-03-29
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, there are two challenges between providing high-quality and low-cost cloud services in the data center

Method used

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  • Cloud Computing Energy Saving Scheduling Implementation Method Based on QoS Feature Discovery
  • Cloud Computing Energy Saving Scheduling Implementation Method Based on QoS Feature Discovery
  • Cloud Computing Energy Saving Scheduling Implementation Method Based on QoS Feature Discovery

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0068] This example uses the data set of Google trace to simulate the workload of energy efficiency management system training. The data, published by Google, provides cluster-level monitoring data from more than 12,500 machines over a 29-day period. Since the entire cluster is heterogeneous in terms of system type and capacity, this instance decided to choose the largest uniform set consisting of 6732 machines for the experiment. It's worth noting that the capacity of the tracking machines is normalized by Google. Due to the very large size of Google traces (more than 39GB of compressed data), we select some random parts of the above uniform subset for experiments. Each section consists of 24 hours of recording. The total size of the section is over 2.26GB. For presentation purposes, the maximum length of the measurements is scaled to 1 hour. This example also uses this duration as the length of the watch window.

[0069] Figure 4is the result of reconstructing the ori...

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Abstract

The invention discloses a cloud computing energy-saving scheduling realization method based on QoS feature discovery. The present invention reconstructs the QoS features of the data center load log information through the stacked noise-reduction autoencoder to obtain feature vectors strongly correlated with the QoS features; Yes, screen out the host queues that are positively correlated with QoS optimization in the scheduling evaluation machine, and perform operations such as resource migration and integration in the virtual machine scheduling module, and finally connect supervised learning and other classifiers to calculate the loss function. The present invention focuses on analyzing the historical data of network traffic in the data center and the load information of each physical machine, and digs out features with a high degree of correlation with QoS indicators to perform resource migration and scheduling work, and finally guarantees the service quality of the data center and reduces its energy consumption goals.

Description

technical field [0001] The invention belongs to the field of cloud computing energy-saving scheduling, and in particular relates to a method for implementing cloud computing energy-saving scheduling based on QoS feature discovery. Background technique [0002] With the increasing popularity of cloud computing technology and the surge of data-intensive services, organizations in various countries and many IT companies are promoting the construction of data centers. The establishment of data centers can better provide enterprises and the public with access to shared configurable system resource pools or even higher-level services at any time, and quickly supply them to users with minimal management costs through the Internet. Due to the surge in the number and scale of data centers, a large amount of power consumption and energy consumption have become a serious challenge for all countries. According to statistics, the current electricity consumption of data centers accounts ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/455G06F9/50
CPCG06F9/45558G06F9/5038G06F9/5088G06F2009/4557Y02D10/00
Inventor 刘发贵王彬
Owner SOUTH CHINA UNIV OF TECH
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