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In multi-server queueing systems where there is no central queue holding allincoming jobs, job dispatching policies are used to assign incoming jobs to thequeue at one of the servers. Classic job dispatching policies such asjoin-the-shortest-queue and shortest expected delay assume that the servicerates and queue lengths of the servers are known to the dispatcher. In thiswork, we tackle the problem of job dispatching without the knowledge of servicerates and queue lengths, where the dispatcher can only obtain noisy estimatesof the service rates by observing job departures. This problem presents a novelexploration-exploitation trade-off between sending jobs to all the servers toestimate their service rates, and exploiting the currently known fastestservers to minimize the expected queueing delay. We propose a bandit-basedexploration policy that learns the service rates from observed job departures.Unlike the standard multi-armed bandit problem where only one out of a finiteset of actions is optimal, here the optimal policy requires identifying theoptimal fraction of incoming jobs to be sent to each server. We present aregret analysis and simulations to demonstrate the effectiveness of theproposed bandit-based exploration policy.

proceedings of the twenty-second international symposium on theory, algorithmic foundations, and protocol design for mobile networks and mobile computing

Job Dispatching Policies for Queueing Systems with Unknown Service Rates