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