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    Practical and Dynamic Buffer Sizing using LearnQueue

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    08454283.pdf
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    Type
    Article
    Authors
    Bouacida, Nader cc
    Shihada, Basem cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-09-03
    Online Publication Date
    2018-09-03
    Print Publication Date
    2019-08-01
    Permanent link to this record
    http://hdl.handle.net/10754/628847
    
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    Abstract
    Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications usage on the Internet (i.e., online gaming, VoIP and safety-critical applications), latency becomes a major issue for the development of wireless technology since it has an enormous impact on user experience. In fact, in a phenomenon known as bufferbloat, large static buffers inside the network devices results in increasing the time that packets spend in the queues and, thus, causing larger delays. Concerns have arisen about designing efficient queue management schemes to mitigate the effects of over-buffering in wireless devices. In this paper, we propose LearnQueue, a novel reinforcement learning design that can effectively control the latency in wireless networks. LearnQueue adapts quickly and intelligently to changes in the wireless environment using a sophisticated reward structure. The latency control is performed dynamically by tuning the buffer size. Adopting a trial-and-error approach, the proposed scheme penalizes the actions resulting in longer delays or hurting the throughput. Using the latest generation of WARP hardware, we investigated LearnQueue performance in various network scenarios. The testbed results prove that LearnQueue can grantee low latency while preserving throughput under various congestion situations. We also discuss the feasibility and possible limitations of large-scale deployment of the proposed scheme in wireless devices
    Citation
    Bouacida N, Shihada B (2018) Practical and Dynamic Buffer Sizing using LearnQueue. IEEE Transactions on Mobile Computing: 1–1. Available: http://dx.doi.org/10.1109/TMC.2018.2868670.
    Sponsors
    This work was funded under grant #AT-35-59 from King Abdulaziz City of Science and Technology.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Mobile Computing
    DOI
    10.1109/TMC.2018.2868670
    Additional Links
    https://ieeexplore.ieee.org/document/8454283/
    ae974a485f413a2113503eed53cd6c53
    10.1109/TMC.2018.2868670
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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