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• W2963686989 abstract "The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this paper, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms, which efficiently converge to an ME, are introduced. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques." @default.
• W2963686989 created "2019-07-30" @default.
• W2963686989 creator A5037078336 @default.
• W2963686989 creator A5081948892 @default.
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• W2963686989 date "2021-01-01" @default.
• W2963686989 modified "2023-10-16" @default.
• W2963686989 title "Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach" @default.
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• W2963686989 doi "https://doi.org/10.1109/tcc.2018.2844379" @default.
• W2963686989 hasPublicationYear "2021" @default.
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