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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Related Experiment Videos

Approximation modeling for the online performance management of distributed computing systems.

Dara Kusic1, Nagarajan Kandasamy, Guofei Jiang

  • 1Electrical and Computer Engineering Department, DrexelUniversity, Philadelphia, PA 19104, USA. dmk25@drexel.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 12, 2008
PubMed
Summary

This study introduces a hierarchical control framework for automating computing system management. The proposed method effectively reduces computational complexity, achieving near-optimal performance in dynamic resource provisioning.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Control Theory
  • Operations Research

Background:

  • Automating management tasks in computing systems is crucial for efficiency.
  • Online optimization in distributed systems faces challenges with dimensionality and modeling.
  • Performance metrics are key to formulating management tasks as control problems.

Purpose of the Study:

  • To develop a hierarchical control framework for performance management in data center computing systems.
  • To address the curses of dimensionality and modeling in distributed control.
  • To reduce the computational burden of large-scale system control.

Main Methods:

  • Formulating management tasks as control or optimization problems.
  • Employing approximation theory to simplify dynamical models and control equations.
  • Developing a hierarchical control framework for distributed computing systems.
  • Case study on a dynamic resource-provisioning problem.

Main Results:

  • The proposed framework effectively manages distributed computing systems.
  • Approximation models significantly reduce computational complexity.
  • Profit gains were within 1% of a controller using an explicit model in a dynamic resource-provisioning case study.

Conclusions:

  • Hierarchical control with approximation theory offers a practical solution for automated management of large-scale computing systems.
  • The framework successfully tackles dimensionality and modeling challenges.
  • Near-optimal performance can be achieved with reduced computational cost.