ACO Load Balancer Simulator
Ant Colony Optimization algorithm for distributing tasks across servers
ACO Parameters
Factors affecting task distribution

Pheromone

Initial:1.0
Decay:0.8
Min:0.1
Deposit (Q):10

Load Balancing

Alpha (pheromone weight):1.0
Beta (load weight):2.0
Current max load:0

Task Batch

Batch size:0
Min tasks:3
Max tasks:5
Current tasks:None

Current Probabilities

S1:
33.3%
S2:
33.3%
S3:
33.3%
Task Assignment Overview
See which server handled which tasks
ServerTasks Assigned

Probability Formula

P(Si) = (Piα/ Liβ) / Σ(Ptotα/ Ltotβ)

Where:

Pheromone Update Formula

P(Si) = (1 - ρ) * P(Si) + ΔP(Si)

Where ΔP(Si) = Q / (L(Si) + 1)

Where:

How It Works

This simulator demonstrates Ant Colony Optimization (ACO) for load balancing. Tasks are assigned to servers based on pheromone levels and current server loads. Servers with higher pheromone levels and lower loads are more likely to be chosen. After each assignment, pheromone levels are updated to reflect the quality of the solution.