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Related Concept Videos

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...
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...
Reinforcement Schedules01:24

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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Related Experiment Videos

Federated multi-cloud task scheduling with load balancing using multi-objective NSGA-II and reinforcement learning.

Wad Ghaban1, Hind Salem Alatawi2

  • 1Computer Science Department, Applied College, University of Tabuk, Tabuk, 71491, Saudi Arabia. wghaban@ut.edu.sa.

Scientific Reports
|May 24, 2026
PubMed
Summary

This study introduces a new Multi-Objective Non-Dominated Sorting Genetic Algorithm with Q-Learning (MO-NSGAQ) for federated multi-cloud task scheduling. MO-NSGAQ enhances adaptability and optimizes cost, makespan, and resource utilization in dynamic cloud environments.

Keywords:
Federated Cloud ComputingLoad BalancingNSGA-IIQ-LearningReinforcement LearningTask Scheduling

Related Experiment Videos

Area of Science:

  • Cloud Computing
  • Artificial Intelligence
  • Operations Research

Background:

  • Federated multi-cloud task scheduling faces challenges due to heterogeneous agreements, decentralized control, and dynamic workloads.
  • Existing hybrid optimization methods lack real-time adaptability and rely on centralized coordination.

Purpose of the Study:

  • To propose an adaptive and scalable federated task scheduling framework for multi-cloud environments.
  • To address the limitations of existing approaches in handling inter-cloud heterogeneity and dynamic conditions.

Main Methods:

  • Integration of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Q-learning within a federated broker architecture.
  • Development of a hybrid multi-objective scheduling framework named MO-NSGAQ.
  • Simulations using synthetic workloads including Google Cloud job traces and IoT-based workloads.

Main Results:

  • MO-NSGAQ simultaneously optimizes execution cost, makespan, load imbalance, and resource utilization.
  • Demonstrated reduction in makespan by 18-32% and improvement in resource utilization by 10-22%.
  • Achieved superior load balancing compared to existing baseline methods.

Conclusions:

  • The proposed MO-NSGAQ framework is effective for adaptive and scalable federated cloud scheduling.
  • The hybrid approach successfully addresses inter-cloud heterogeneity and dynamic workload characteristics.