
The authors develop and analyze adaptive load balancing policies that can maintain a balanced distribution of tasks across server pools, even when the arrival rate of tasks is time-varying or the service time distribution is non-exponential. The policies integrate a threshold-based dispatching rule with online learning schemes to dynamically adjust the threshold in response to changes in the unknown offered load.


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learning-and-balancing-unknown-loads-in-large-scale-systems-with-time-varying-arrival-rates-and-non-exponential-service-times


Learning and Balancing Unknown Loads in Large-Scale Systems with Time-Varying Arrival Rates and Non-Exponential Service Times
