000263271 001__ 263271
000263271 005__ 20190131232935.0
000263271 020__ $$a978-1-4503-5773-9
000263271 02470 $$2isi$$a000455379800003
000263271 0247_ $$a10.1145/3210424.3210430$$2doi
000263271 037__ $$aCONF
000263271 245__ $$aA Bayesian Bandit Approach to Adaptive Delay-based Congestion Control
000263271 260__ $$c2018$$aNew York$$bASSOC COMPUTING MACHINERY
000263271 269__ $$a2018-01-01
000263271 336__ $$aConference Papers
000263271 520__ $$aAdapting the transmission rate of video telephony Internet applications in order to guarantee the maximal communication quality is still an open and extremely challenging problem. The congestion control algorithm, which is the algorithm responsible for adjusting the transmission rate according to the network conditions, should typically be able to reach the largest possible rate, in order to achieve a high video quality, at the minimum possible delay, in order to guarantee a good interactivity. At the same time, it should also guarantee a fair share of the network resources when competing with other communication protocols, in particular loss-based congestion protocols. These two objectives actually conflict with each other: whereas, in order to achieve the largest rate with the minimum delay, the delay-based congestion control should be extremely sensitive to delay variations, it should also be ideally immune to delay variations to have perfect coexistence with loss-based protocols. In order to achieve this double objective we propose a learning-based adaptive controller that tunes the delay sensitivity of an underlying delay-based congestion control according to the estimated network conditions. We first define a simple low-dimensional model for the network response. We then formulate a bayesian bandit problem for the selection of the delay sensitivity of the congestion control algorithm. By solving the bandit problem using an optimal learning method we are able to maximize effectively the long term utility provided to the user. Finally, we provide simulation results to demonstrate the operation of the proposed method and its effective ability to adapt to different network scenarios in order to maximize the communication quality.
000263271 6531_ $$acongestion control
000263271 6531_ $$abandit problems
000263271 6531_ $$aadaptive control
000263271 700__ $$aD'Aronco, Stefano$$0248090
000263271 700__ $$aFrossard, Pascal$$0241061
000263271 7112_ $$a23th ACM Workshop on Packet Video (ACM PV)$$dJun 12, 2018$$cAmsterdam, NETHERLANDS
000263271 773__ $$q13-18$$tProceedings Of The 23Th Acm Workshop On Packet Video (Pv'18)
000263271 8560_ $$fpascal.frossard@epfl.ch
000263271 909C0 $$zMarselli, Béatrice$$0252393$$yApproved$$pLTS4$$xU10851$$mpascal.frossard@epfl.ch
000263271 909CO $$pSTI$$ooai:infoscience.epfl.ch:263271$$pconf
000263271 961__ $$afantin.reichler@epfl.ch
000263271 973__ $$aEPFL$$sPUBLISHED$$rREVIEWED
000263271 980__ $$aCONF
000263271 980__ $$aWoS
000263271 981__ $$aoverwrite