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Related Experiment Video

Updated: May 13, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Adaptive controller for dynamic power and performance management in the virtualized computing systems.

Chengjian Wen1, Xiang Long, Yifen Mu

  • 1Department of Computer Science and Engineering, Beihang University, Beijing, People's Republic of China. wenchengjian@gmail.com

Plos One
|March 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive controller for power and performance management in virtualized data centers. The method effectively saves energy while meeting performance demands across various workloads.

Related Experiment Videos

Last Updated: May 13, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Systems Engineering

Background:

  • Power and performance management in large-scale computing systems, particularly data centers, is a critical challenge.
  • The complexity arises from multiple objectives, factors, and hierarchical structures, demanding efficient solutions for power saving.

Purpose of the Study:

  • To investigate power and performance management in virtualized computing systems.
  • To formulate the problem as a constrained power optimization task and develop an adaptive control strategy.

Main Methods:

  • Designed an adaptive controller using a least-square self-tuning regulator (LS-STR) for performance tracking.
  • Integrated resource allocation with the controller to minimize power consumption under performance constraints.

Main Results:

  • The adaptive controller demonstrated effectiveness across diverse performance metrics and workloads (single and multiple).
  • Simulations confirmed the controller's ability to track performance requirements accurately while significantly reducing power consumption.

Conclusions:

  • The proposed adaptive controller is a viable and effective solution for power optimization in virtualised computing environments.
  • This approach offers significant power savings without compromising system performance, applicable to various operational scenarios.