A Surrogate Optimization-Based Mechanism for Resource Allocation and Routing in Networks With Strategic Agents

Farhadi, Farzaneh; Golestani, S. Jamaloddin; Teneketzis, Demosthenis

doi:10.1109/tac.2018.2823270

Cited by 33 publications

(33 citation statements)

References 33 publications

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“…This forces that the solution (x(y * ), y * ) of OCP-S must solve each agent's subproblem at equilibrium. A similar argument with a numerical example is given in [25] in the context of routing. Also, each agent reports weights such that the derivative of the surrogate function f matches exactly their marginal cost with respect to states and inputs.…”

Section: Equilibrium Characterizationsupporting

confidence: 55%

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Surrogate Optimal Control for Strategic Multi-Agent Systems

Hespanhol

Aswani

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper studies how to design a platform to optimally control constrained multi-agent systems with a single coordinator and multiple strategic agents. In our setting, the agents cannot apply control inputs and only the coordinator applies control inputs; however, the coordinator does not know the objective functions of the agents, and so must choose control actions based on information provided by the agents. One major challenge is that if the platform is not correctly designed then the agents may provide false information to the coordinator in order to achieve improved outcomes for themselves at the expense of the overall system efficiency. Here, we design an interaction mechanism between the agents and the coordinator such that the mechanism: ensures agents truthfully report their information, has low communication requirements, and leads to a control action that achieves efficiency by achieving a Nash equilibrium. In particular, we design a mechanism in which each agent does not need to posses full knowledge of the system dynamics nor the objective functions of other agents. We illustrate our proposed mechanism in a model predictive control (MPC) application involving heating, ventilation, airconditioning (HVAC) control by a building manager of an apartment building. Our results showcase how such a mechanism can be potentially used in the context of distributed MPC.

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Section: Equilibrium Characterizationsupporting

confidence: 55%

“…A major hurdle in implementing such mechanisms is their steep communication needs [11], [24], [25]. But, minimal strategy spaces that elicit efficient Nash equilibrium in convex environments have been developed [26].…”

Section: A Contributionsmentioning

confidence: 99%

Surrogate Optimal Control for Strategic Multi-Agent Systems

Hespanhol

Aswani

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…There are many excellent works that proposed Nash mechanisms for specific allocations, such as the general flow control problems (e.g. [13]- [17]), plug-in electric vehicles system (e.g. [19]), power allocation and spectrum sharing problem (e.g.…”

Section: B Mechanism Design For the Num Problemsmentioning

confidence: 99%

“…Only a few studies focused on the distributed algorithms (dynamics) for the Nash mechanisms for network applications [17], [21]. We cannot directly apply these algorithms in [17], [21] in our context.…”

Section: Distributed Algorithms For Nash Mechanismsmentioning

confidence: 99%

“…Second, existing mechanisms proposed for network resource allocation are often applicable to only specific networking scenarios (e.g. flow control problems [13]- [17], power and spectrum allocation [18], and electric vehicles systems [19]). These mechanisms often do not work for more general and sophisticated NUM Problems or the general network sharing framework.…”

mentioning

confidence: 99%

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Efficient Network Sharing With Asymmetric Constraint Information

Zhang

Huang

2019

IEEE J. Select. Areas Commun.

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Network sharing has become a key feature of various enablers of the next generation network, such as network function virtualization and fog computing architectures. Network utility maximization (NUM) is a general framework for achieving fair, efficient, and cost-effective sharing of constrained network resources. When agents have asymmetric and private information, however, a fundamental economic challenge is how to solve the NUM Problem considering the self-interests of strategic agents. Many previous related works have proposed economic mechanisms that can cope with agents' private utilities. However, the network sharing paradigm introduces the issue of information asymmetries regarding constraints. The related literature largely neglected such an issue; limited closely related studies provided solutions only applicable to specific application scenarios. To tackle these issues, we propose the Decomposable NUM (DeNUM) Mechanism and the Dynamic DeNUM (DyDe-NUM) Mechanism, the first mechanisms in the literature for solving NUM Problems considering private utility and constraint information. The key idea of both mechanisms is to decentralize the decision process to agents, who will make resource allocation decisions without the need of revealing private information to others. Under a monitorable influence assumption, the DeNUM Mechanism yields the network-utility maximizing solution at an equilibrium, and achieves other desirable economic properties (such as individual rationality and budget balance). We further establish the connection between the equilibrium structure and the primal-dual solution to a related optimization problem, based on which we prove the convergence of the DeNUM Algorithm to an equilibrium. When the agents' influences are not monitorable, we propose the DyDeNUM Mechanism that yields the networkutility maximizing solution at the cost of the balanced budget. Finally, as a case study, we apply the proposed mechanisms to solving the NUM problem for a fog-based user-provided network, and show that both mechanisms improve the network utility by 34% compared to a non-cooperation benchmark.Index Terms-Mechanism design, network sharing, network utility maximization, asymmetric constraint information.

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Resilient algorithm for distributed resource allocation under false data injection attacks

Chen,

Cai,

Gao

et al. 2024

Asian Journal of Control

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For a first‐order nonlinear multi‐agent system who is subject to false data injection (FDI) attacks on agents' actuators and sensors, agents execute a distributed resource allocation algorithm according to the compromised control inputs and interactive information such that the multi‐agent system is unstable and agents' decisions deviate from the optimal resource allocation. At first, we analyze the robustness of the distributed resource allocation algorithm under the FDI attacks. Then, a resilient distributed algorithm is proposed to solve the distributed resource allocation problem by resisting the adverse effect of the attacks. In detail, the unknown nonlinear term and the false data injected in agents are considered as extended states that can be estimated by extended state observers. The estimation is used in the feedback control to suppress the effect of the FDI attacks. As a result, the designed resilient algorithm ensures that agents' decisions converge to the optimal allocation without requiring any information about the nature of the attacks. An example is given to illustrate the results.

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A Surrogate Optimization-Based Mechanism for Resource Allocation and Routing in Networks With Strategic Agents

Cited by 33 publications

References 33 publications

Surrogate Optimal Control for Strategic Multi-Agent Systems

Surrogate Optimal Control for Strategic Multi-Agent Systems

Efficient Network Sharing With Asymmetric Constraint Information

Resilient algorithm for distributed resource allocation under false data injection attacks

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