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Minimizing Queue Length Regret Under Adversarial Network Models

Authors:

Eytan ModianoAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 2, Issue 1

Article No.: 11, Pages 1 - 32

https://doi.org/10.1145/3179414

Published: 03 April 2018 Publication History

Abstract

Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, these models fail to capture non-stationary or even adversarial network dynamics which are of increasing importance for modeling the behavior of networks under malicious attacks or characterizing short-term transient behavior. In this paper, we focus on minimizing queue length regret under adversarial network models, which measures the finite-time queue length difference between a causal policy and an "oracle" that knows the future. Two adversarial network models are developed to characterize the adversary's behavior. We provide lower bounds on queue length regret under these adversary models and analyze the performance of two control policies (i.e., the MaxWeight policy and the Tracking Algorithm). We further characterize the stability region under adversarial network models, and show that both the MaxWeight policy and the Tracking Algorithm are throughput-optimal even in adversarial settings.

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Cited By

Dai YHuang L(2024)Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop NetworksProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004138:3(1-48)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3700413
Freund DLykouris TWeng WOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Quantifying the cost of learning in queueing systemsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666407(6532-6544)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666407
Nguyen QModiano EJi BChiasserini CWu JSubramaniam S(2023)Learning to Schedule in Non-Stationary Wireless Networks With Unknown StatisticsProceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3565287.3610258(181-190)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3565287.3610258
Show More Cited By

Index Terms

Minimizing Queue Length Regret Under Adversarial Network Models
1. Networks
  1. Network performance evaluation
    1. Network performance analysis

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Minimizing Queue Length Regret Under Adversarial Network Models
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Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, these models fail to capture non-stationary or even adversarial network dynamics which are of increasing importance ...
Minimizing Queue Length Regret Under Adversarial Network Models
SIGMETRICS '18: Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems

Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, these models fail to capture non-stationary or even adversarial network dynamics which are of increasing importance ...
Large number of queues in tandem: Scaling properties under back-pressure algorithm

We consider a system with N unit-service-rate queues in tandem, with exogenous arrivals of rate at queue 1, under a back-pressure (MaxWeight) algorithm: service at queue n is blocked unless its queue length is greater than that of the next queue ...

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 2, Issue 1

March 2018

603 pages

EISSN:2476-1249

DOI:10.1145/3203302

Editors:
Augustin Chaintreau
Columbia University
,
Aditya Akella
University of Wisconsin-Madison
,
Adam Wierman
California Institute of Technology

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Copyright © 2018 ACM.

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Publication History

Published: 03 April 2018

Published in POMACS Volume 2, Issue 1

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Cited By

Dai YHuang L(2024)Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop NetworksProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004138:3(1-48)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3700413
Freund DLykouris TWeng WOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Quantifying the cost of learning in queueing systemsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666407(6532-6544)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666407
Nguyen QModiano EJi BChiasserini CWu JSubramaniam S(2023)Learning to Schedule in Non-Stationary Wireless Networks With Unknown StatisticsProceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3565287.3610258(181-190)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3565287.3610258
Liu BModiano E(2022)Optimal Control for Networks with Unobservable MaliciousNodesACM SIGMETRICS Performance Evaluation Review10.1145/3529113.352911949:3(18-19)Online publication date: 25-Mar-2022
https://dl.acm.org/doi/10.1145/3529113.3529119
Liu BModiano E(2022)Universal Policy Tracking: Scheduling for Wireless Networks with Delayed State Observation2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)10.1109/Allerton49937.2022.9929348(1-8)Online publication date: 27-Sep-2022
https://doi.org/10.1109/Allerton49937.2022.9929348
Stahlbuhk TShrader BModiano E(2021)Learning Algorithms for Minimizing Queue Length RegretIEEE Transactions on Information Theory10.1109/TIT.2021.305485467:3(1759-1781)Online publication date: Mar-2021
https://doi.org/10.1109/TIT.2021.3054854
Liu BModiano E(2021)Optimal control for networks with unobservable malicious nodesPerformance Evaluation10.1016/j.peva.2021.102230(102230)Online publication date: Sep-2021
https://doi.org/10.1016/j.peva.2021.102230
Fu XModiano E(2019)Fundamental Limits of Volume-based Network DoS AttacksProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/33666983:3(1-36)Online publication date: 17-Dec-2019
https://dl.acm.org/doi/10.1145/3366698
Liang QModiano E(2018)Minimizing Queue Length Regret Under Adversarial Network ModelsACM SIGMETRICS Performance Evaluation Review10.1145/3292040.321963046:1(31-32)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3292040.3219630
Liang QModiano EPsounis KAkella AWierman A(2018)Minimizing Queue Length Regret Under Adversarial Network ModelsAbstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems10.1145/3219617.3219630(31-32)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3219617.3219630

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