@hollyent

The convergence of multi-armed bandit (MAB) models in cloud task allocation depends on exploration-exploitation trade-offs and algorithm choice. Upper Confidence Bound (UCB) algorithms converge faster than ε-greedy or Softmax by prioritizing arms with higher uncertainty-adjusted rewards, reducing redundant exploration. However, UCB’s computational overhead increases with the number of arms. Bayesian methods (e.g., Thompson Sampling) converge efficiently by sampling from posterior distributions but require prior knowledge. In dynamic cloud environments, non-stationary MAB variants (e.g., discounted or sliding-window UCB) adapt to changing task priorities, ensuring convergence despite shifting reward distributions. Properly tuned parameters (e.g., exploration rates) and parallel arm evaluation further accelerate stability.

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