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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Index Terms
Minimizing Queue Length Regret Under Adversarial Network Models
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Minimizing Queue Length Regret Under Adversarial Network Models
SIGMETRICS '18Stochastic 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 SystemsStochastic 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
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