Time-optimal network queue control: the case of a single congested node

Iyer, Mahadevan; Tsai, W.K.

doi:10.1109/infcom.2003.1208712

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…In this paper, we investigate the performance trade-off between different AQM disciplines and derive a novel AQM that has a good performance balance when compared to others. Optimal control has been exploited to investigate queue performance in computer networks in [11,12]. We analyze the FBSM-AQM algorithm with and without the use of the adoption of checkpointing.…”

Section: Introductionmentioning

confidence: 99%

Balancing Tradeoffs in Network Queue Management Problem via Forward–Backward Sweeping with Finite Checkpoints

Radwan,

Alenezi,

Alrashdan

et al. 2023

Symmetry

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Network queue management can be modelled as an optimal control problem and is aimed at controlling the dropping rate, in which the state and control variables are the instantaneous queue length and the dropping rate, respectively. One way to solve it is by using an indirect method, namely forward–backward sweeping based on the Pontryagin minimum principle to derive control the trajectory of the dropping rate. However, there exists some performance balance issues in the network queue, such as memory usage versus runtime of the algorithm, or dropping rate versus network queue length. Many researchers have exploited symmetry for constrained systems, controllers, and model predictive control problems to achieve an exponential memory reduction and simple, intuitive optimal controllers. In this article, we introduce the integration of the checkpointing method into forward–backward sweeping to address such balancing issues. Specifically, we exploit the revolve algorithm in checkpointing and choose a finite number of checkpoints to reduce the complexity. Both numerical and simulation results in a popular network simulator (ns-2) are provided through two experiments: varying bandwidth and offered load, which solidify our proposal in comparison to other deployed queue management algorithms.

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Section: Introductionmentioning

confidence: 99%