Network Utility Maximization in Adversarial Environments

Liang, Qingkai; Modiano, E.

doi:10.1109/infocom.2018.8485973

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…We investigate network optimization under a generalized partiallycontrollable network model whereas existing works (e.g., [9,20,22]) imposed very stringent assumptions about the behavior of uncontrollable nodes for analytical tractability (e.g., underlay nodes use shortest path routing). In addition, our framework allows for both adversarial environment (e.g., adversarial exogenous packet injections and link states) and adversarial uncontrollable nodes, thus greatly extending previous adversarial network models that do not account for uncontrollable nodes (e.g., [2,10,11,17]). As far as we know, this is the rst work that establishes network optimization results under the generalized adversarial partiallycontrollable network model.…”

Section: Our Contributionsmentioning

confidence: 99%

“…However, these results are limited to very speci c queueing systems and only exogenous tra c injections are adversarial. Recently, Liang and Modiano [10,11] studied various network optimization problems under a generalized adversarial network model that relaxed the window constraints. ey analyzed the worst-case performance of di erent network control algorithms (e.g., MaxWeight [24] and Dri -plus-Penalty [18]) under their proposed adversarial models with respect to the notions of queue length regret and utility regret.…”

Section: Network Optimization In Adversarialmentioning

confidence: 99%

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Optimal Network Control with Adversarial Uncontrollable Nodes

Liang

Modiano

2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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e e ectiveness of many well-known network control algorithms (e.g., MaxWeight) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and may take arbitrary (possibly adversarial) actions. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks and uncooperative wireless networks. In this paper, we study two fundamental network optimization problems in a network with adversarial uncontrollable nodes. First, we investigate the network stability problem where the objective is to stabilize the network whenever possible. We develop a lower bound on the total queue length that can be achieved by any causal policy, and propose a throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the behavior of uncontrollable nodes. Second, we study the network utility maximization problem where the objective is to maximize cumulative utility subject to stability constraints. We provide a lower bound on the utility-delay tradeo , and develop the Tracking Dri-plus-Penalty (TDP) algorithm that achieves tunable utility-delay tradeo s.

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Section: Our Contributionsmentioning

confidence: 99%

Section: Network Optimization In Adversarialmentioning

confidence: 99%

Optimal Network Control with Adversarial Uncontrollable Nodes

Liang

Modiano

2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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“…The adversarial setting is considered for a MaxWeight model by Liang and Modiano (2018a). Further formulations for wireless networks (Stahlbuhk et al 2019) and for network utility maximization are considered (Liang and Modiano 2018b). A strategic adversarial model of queueing is considered by Gaitonde and Tardos (2020a).…”

Section: Literaturementioning

confidence: 99%

Learning and Information in Stochastic Networks and Queues

Walton¹,

Xu²

2021

Preprint

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We review the role of information and learning in the stability and optimization of queueing systems. In recent years, techniques from supervised learning, bandit learning and reinforcement learning have been applied to queueing systems supported by increasing role of information in decision making. We present observations and new results that help rationalize the application of these areas to queueing systems.We prove that the MaxWeight and BackPressure policies are an application of Blackwell's Approachability Theorem. This connects queueing theoretic results with adversarial learning. We then discuss the requirements of statistical learning for service parameter estimation. As an example, we show how queue size regret can be bounded when applying a perceptron algorithm to classify service. Next, we discuss the role of state information in improved decision making. Here we contrast the roles of epistemic information (information on uncertain parameters) and aleatoric information (information on an uncertain state). Finally we review recent advances in the theory of reinforcement learning and queueing, as well as, provide discussion on current research challenges.

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“…This definition naturally enforces network resource constraints as the undelivered jobs in the queues at time 𝑇 are not counted towards the utility. We seek to design a policy with regret sublinear to 𝑇 , where regret [11] is defined as the gap between the expected utility of the policy and that of the optimal policy that has full knowledge of the utility functions in advance. As a first step, we establish that the expected utility achieved by the optimal (dynamic) policy is upper bounded by 𝑇 times the optimal value of a static optimization problem, whose objective is the sum of the (unknown) utility functions and the constraints are implicitly given by the capacity region of the network.…”

Section: Introductionmentioning

confidence: 99%

Learning-NUM: Network Utility Maximization With Unknown Utility Functions and Queueing Delay

Modiano

2022

IEEE/ACM Trans. Networking

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Network Utility Maximization (NUM) studies the problems of allocating traffic rates to network users in order to maximize the users' total utility subject to network resource constraints. In this paper, we propose a new NUM framework, Learning-NUM, where the users' utility functions are unknown apriori and the utility function values of the traffic rates can be observed only after the corresponding traffic is delivered to the destination, which means that the utility feedback experiences queueing delay. The goal is to design a policy that gradually learns the utility functions and makes rate allocation and network scheduling/routing decisions so as to maximize the total utility obtained over a finite time horizon 𝑇 . In addition to unknown utility functions and stochastic constraints, a central challenge of our problem lies in the queueing delay of the observations, which may be unbounded and depends on the decisions of the policy. We first show that the expected total utility obtained by the best dynamic policy is upper bounded by the solution to a static optimization problem. Without the presence of feedback delay, we design an algorithm based on the ideas of gradient estimation and Max-Weight scheduling. To handle the feedback delay, we embed the algorithm in a parallel-instance paradigm to form a policy that achieves Õ (𝑇 3/4 )-regret, i.e., the difference between the expected utility obtained by the best dynamic policy and our policy is in Õ (𝑇 3/4 ). Finally, to demonstrate the practical applicability of the Learning-NUM framework, we apply it to three application scenarios including database query, job scheduling and video streaming. We further conduct simulations on the job scheduling application to evaluate the empirical performance of our policy.

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Network Utility Maximization in Adversarial Environments

Cited by 9 publications

References 17 publications

Optimal Network Control with Adversarial Uncontrollable Nodes

Optimal Network Control with Adversarial Uncontrollable Nodes

Learning and Information in Stochastic Networks and Queues

Learning-NUM: Network Utility Maximization With Unknown Utility Functions and Queueing Delay

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