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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...
Bioreactor Design and Operational System01:29

Bioreactor Design and Operational System

Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

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Related Experiment Videos

Hybrid Bacterial Colony Optimization and Particle Swarm Optimization for load balancing in fog computing.

Aryan Kumar1, Punit Gupta1,2, Rohit Verma3

  • 1School of Computing, National College of Ireland, Dublin, Ireland.

Plos One
|May 8, 2026
PubMed
Summary

A new hybrid metaheuristic algorithm combines Bacterial Colony Optimization (BCO) and Particle Swarm Optimization (PSO) for improved fog computing load balancing. This approach significantly reduces task completion time and enhances virtual machine usage in dynamic environments.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Fog computing processes data near the source to reduce latency and bandwidth usage.
  • Effective workload balancing is crucial for fog computing due to dynamic, resource-limited nodes.
  • Uneven task distribution leads to bottlenecks, underutilized resources, and decreased Quality of Service (QoS).

Purpose of the Study:

  • To introduce a hybrid metaheuristic load balancing algorithm for fog computing.
  • To enhance load balancing by combining Bacterial Colony Optimization (BCO) and Particle Swarm Optimization (PSO).
  • To improve makespan reduction and virtual machine (VM) utilization.

Main Methods:

  • Developed a hybrid BCO-PSO algorithm in Python.
  • Utilized original BCO and hybrid modules alongside a standard PSO executable.
  • Tested the algorithm on a synthetic task-VM dataset (100-10,000 tasks) generated by CloudSim 6.0.

Main Results:

  • The hybrid BCO-PSO algorithm demonstrated significant makespan reduction and increased VM utilization.
  • Outperformed individual BCO, PSO, and Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) algorithms.
  • Achieved faster convergence, especially in high workload scenarios, reducing makespan by up to 35.79%.

Conclusions:

  • The proposed hybrid BCO-PSO algorithm is an effective and adaptive solution for task allocation in fog computing.
  • It addresses the challenges of workload balancing in dynamic fog environments.
  • The algorithm shows promise for fog-inspired computational scheduling scenarios.