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Related Concept Videos

Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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...
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:
Scale-Up Processes01:14

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...

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Related Experiment Videos

A Two-Stage VM Migration Framework for Power-Constrained Data Center Load Scheduling.

Xiande Bu1,2, Haixin Sun1,3, Feng Tian1

  • 1School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces a novel framework for optimizing virtual machine (VM) migration in data centers facing dynamic power constraints. The MFB-MFEACO framework effectively balances energy consumption, service levels, and migration efficiency.

Keywords:
data centersdynamic power constraintsinformation load schedulingmulti-factor equilibrium optimizationvirtual machine migration

Related Experiment Videos

Area of Science:

  • Computer Science
  • Energy Systems
  • Cloud Computing

Background:

  • Data centers face increasing energy demands and fluctuating renewable energy integration, leading to dynamic power constraints.
  • Virtual machine workloads directly impact IT and auxiliary power consumption, creating complex cyber-physical systems.
  • Existing methods struggle to reconcile dynamic power limits with variable information loads.

Purpose of the Study:

  • To address the mismatch between dynamic power upper bounds and time-varying information loads in data centers.
  • To propose a two-stage virtual machine migration optimization framework for power-constrained environments.
  • To improve energy efficiency, reduce service level agreement violations, and minimize migrations.

Main Methods:

  • Developed a two-stage virtual machine (VM) migration optimization framework: Multi-Factor Balanced (MFB) for VM selection and Multi-Factor Equilibrium Ant Colony Optimization (MFEACO) with Random Roulette Wheel (RRW) for VM placement.
  • MFB algorithm utilizes an arctangent-based warning-line trend model considering resource utilization, power load trends, and SLA violations.
  • MFEACO algorithm employs normalized multi-dimensional equilibrium factors to balance energy consumption, load balancing, and SLA violations.

Main Results:

  • The proposed MFB-MFEACO framework successfully satisfies dynamic power constraints while achieving a favorable trade-off between energy consumption, SLA violations, and migration reduction.
  • Simulation experiments on an enhanced CloudSim platform with real-world data demonstrated superior performance compared to traditional methods and a power-constrained genetic algorithm.
  • The framework exhibits enhanced dynamic adaptability and scheduling stability in complex data center environments.

Conclusions:

  • The MFB-MFEACO framework provides an effective solution for information load scheduling under dynamic power constraints in data centers.
  • The study highlights the importance of considering multiple factors for optimizing VM migration in energy-aware cloud environments.
  • This research contributes to more sustainable and reliable data center operations amidst renewable energy integration.