A Truthful FPTAS Auction for the Edge-Cloud Pricing Problem

Shen, Ziyu; Zhang, Jinshui; Tan, Haisheng

doi:10.1109/bigcom51056.2020.00027

Cited by 4 publications

(1 citation statement)

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“…Existing auction-based works consider several approaches to address the heterogeneity in MEC resources, such as: jointly allocating computing and wireless resources [21], [26], defining multi-dimensional computing resources [17], [18], [24], and partitioning computing resources into virtualized instances [19]. However, it is still unclear how the MEC system faces the service-specific computational capacity requirements in modeling the MEC resources.…”

Section: Gsp Auctionmentioning

confidence: 99%

A Reverse Auction Model for Efficient Resource Allocation in Mobile Edge Computation Offloading

Habiba

Hossain

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-ofthe-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results 1 , we show the effectiveness of our proposed resource allocation mechanism.

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