Stability of Decentralized Queueing Networks Beyond Complete Bipartite Cases

Fu, Hu; Hu, Qun; Lin, Jia’nan

doi:10.1007/978-3-031-22832-2_6

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Other related works. Beyond multi-player bandits, our work is also related to the literature in learning-aided scheduling (Walton and Xu 2021;Chen, Dong, and Shi 2020;Krishnasamy et al 2016Krishnasamy et al , 2018Krishnasamy et al , 2021Choudhury et al 2021;Stahlbuhk, Shrader, and Modiano 2021;Liu and Fang 2023;Liu and Xu 2024) and learning-aided queuing Tardos 2020, 2021;Sentenac, Boursier, and Perchet 2021;Gaitonde and Tardos 2023;Fu, Hu, and Lin 2022;Bistritz and Bambos 2022;Freund, Lykouris, and Weng 2022;Krishnasamy et al 2016Krishnasamy et al , 2021.…”

Section: Introductionmentioning

confidence: 98%

Decentralized Scheduling with QoS Constraints: Achieving O(1) QoS Regret of Multi-Player Bandits

Liu,

Fang

2024

AAAI

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We consider a decentralized multi-player multi-armed bandit (MP-MAB) problem where players cannot observe the actions and rewards of other players and no explicit communication or coordination between players is possible. Prior studies mostly focus on maximizing the sum of rewards of the players over time. However, the total reward maximization learning may lead to imbalanced reward among players, leading to poor Quality of Service (QoS) for some players. In contrast, our objective is to let each player n achieve a predetermined expected average reward over time, i.e., achieving a predetermined level of QoS. We develop a novel decentralized MP-MAB algorithm to accomplish this objective by leveraging the methodology of randomized matching. We prove that our decentralized algorithm can ensure that all players have an O(1) QoS regret. We also reveal an analog between our MP-MAB model and the online wireless queuing systems, which builds a connection between QoS in MP-MAB learning and stability in queuing theory.

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Section: Introductionmentioning

confidence: 98%

Decentralized Scheduling with QoS Constraints: Achieving O(1) QoS Regret of Multi-Player Bandits

Liu,

Fang

2024

AAAI

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The Price of Anarchy of Strategic Queuing Systems

Gaitonde

Tardos

2023

J. ACM

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Bounding the price of anarchy, which quantifies the damage to social welfare due to selfish behavior of the participants, has been an important area of research in algorithmic game theory. Classical work on such bounds in repeated games makes the strong assumption that the subsequent rounds of the repeated games are independent beyond any influence on play from past history. This work studies such bounds in environments that themselves change due to the actions of the agents. Concretely, we consider this problem in discrete-time queuing systems, where competitive queues try to get their packets served. In this model, a queue gets to send a packet each step to one of the servers, which will attempt to serve the oldest arriving packet, and unprocessed packets are returned to each queue. We model this as a repeated game where queues compete for the capacity of the servers, but where the state of the game evolves as the length of each queue varies. We analyze this queuing system from multiple perspectives: as a baseline measure, we first establish precise conditions on the queuing arrival rates and service capacities that ensure all packets clear efficiently under centralized coordination. We then show that if queues strategically choose servers according to independent and stationary distributions, the system remains stable provided it would be stable under coordination with arrival rates scaled up by a factor of just \(\frac{e}{e-1} \) . Finally, we extend these results to no-regret learning dynamics: if queues use learning algorithms satisfying the no-regret property to choose servers, then the requisite factor increases to two, and both of these bounds are tight. Both of these results require new probabilistic techniques compared to the classical price of anarchy literature, and show that in such settings, no-regret learning can exhibit efficiency loss due to myopia.

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Maximum Revenue of Second Price Auction with Reserve Price for Single Agent Models

Shi,

Pang

2024

Preprint

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Stability of Decentralized Queueing Networks Beyond Complete Bipartite Cases

Cited by 4 publications

References 8 publications

Decentralized Scheduling with QoS Constraints: Achieving O(1) QoS Regret of Multi-Player Bandits

Decentralized Scheduling with QoS Constraints: Achieving O(1) QoS Regret of Multi-Player Bandits

The Price of Anarchy of Strategic Queuing Systems

Maximum Revenue of Second Price Auction with Reserve Price for Single Agent Models

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