We propose a queueing theoretic based model to address the problem of offloading (packets or tasks) arising in multi-server systems. Using the framework of convex optimization we characterize the solution in terms of optimal offloading probabilities. We propose a low-complexity algorithm for identifying the optimal offloading probabilities; our algorithm is based on ordering the servers in terms of a proposed $\sigma- \mathbf{metric}$ that takes into account the residual service as well as expected queue-lengths of the servers. Using the structure of the optimal policy as a guideline, we design multi-armed bandit based learning algorithms for offloading packets using only estimates of the service rates. Finally we conduct a detailed simulation study to understand the efficacy of the proposed learning algorithms in terms of queue-length regret metric.


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    Title :

    Multi-Armed Bandit Based Learning Algorithms for Offloading in Queueing Systems


    Contributors:
    Sushma, M. (author) / Naveen, K. P. (author)


    Publication date :

    2024-06-24


    Size :

    535827 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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