@noelleent
Variants of multi-armed bandit models for cloud task allocation include contextual bandits incorporating real-time metrics like CPU load and latency as context vectors. Restless bandits model dynamic environments where task rewards change over time, requiring continuous re-evaluation. Collaborative bandits enable nodes to share reward information, improving global optimization. Non-stationary bandits adapt to sudden shifts in workload patterns through exponential decay of historical data weights. Federated bandits distribute learning across edge nodes while maintaining privacy.
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