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dc.contributor.authorBouacida, Nader
dc.contributor.authorShihada, Basem
dc.date.accessioned2018-09-30T12:12:43Z
dc.date.available2018-09-30T12:12:43Z
dc.date.issued2018-09-03
dc.identifier.citationBouacida 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.
dc.identifier.issn1536-1233
dc.identifier.issn1558-0660
dc.identifier.issn2161-9875
dc.identifier.doi10.1109/TMC.2018.2868670
dc.identifier.urihttp://hdl.handle.net/10754/628847
dc.description.abstractWireless 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
dc.description.sponsorshipThis work was funded under grant #AT-35-59 from King Abdulaziz City of Science and Technology.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8454283/
dc.rights(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectActive Queue Management
dc.subjectbufferbloat
dc.subjectdynamic buffer sizing
dc.subjectlatency
dc.subjectReinforcement Learning
dc.subjectwireless networks
dc.titlePractical and Dynamic Buffer Sizing using LearnQueue
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Mobile Computing
dc.eprint.versionPost-print
kaust.personBouacida, Nader
kaust.personShihada, Basem
refterms.dateFOA2018-10-01T08:34:42Z
dc.date.published-online2018-09-03
dc.date.published-print2019-08-01


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