Efficient Network Sharing with Asymmetric Constraint Information
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ID: 283127
2019
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Abstract
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 (DyDeNUM) 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 network-utility maximizing solution at the cost of the balanced
budget.
| Reference Key |
huang2019efficient
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|---|---|
| Authors | Meng Zhang; Jianwei Huang |
| Journal | arXiv |
| Year | 2019 |
| DOI |
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