Multi-Block Ascending Auctions for Effective 5G Licensed Shared Access

Chouayakh, Ayman; Bechler, Aurélien; Amigo, Isabel; Nuaymi, Loutfi; Maillé, Patrick

doi:10.1109/tmc.2021.3063990

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The resource capacity of each edge server is 5. bw 2 = (0, 2, 1), which means that the transmission rate between user 2 and edge server 2 is 2 M/s (bw 22 = 2), the transmission rate between user 2 and edge server 3 is 1 M/s (bw 23 = 1), and the transmission rate of 0 indicates that the distance between user 2 and edge server 1 is too far to transmit (bw 21 = 0). [5,5,5].…”

Section: Parametersmentioning

confidence: 99%

“…Previously, auction mechanisms have been widely applied in spectrum auctions [4,5], offline and online cloud computing virtual resource auctions [6][7][8], network flow models [9], edge computing [10] and mobile blockchains [11,12], and their application in FL is feasible [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…the winning users start participating in FL training. 5. the FL platform pays the corresponding rewards to the winning users.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

Liu,

Zhang,

Wang

et al. 2023

Mathematics

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Federated learning is a promising technique in cloud computing and edge computing environments, and designing a reasonable resource allocation scheme for federated learning is particularly important. In this paper, we propose an auction mechanism for federated learning resource allocation in the edge–cloud collaborative environment, which can motivate data owners to participate in federated learning and effectively utilize the resources and computing power of edge servers, thereby reducing the pressure on cloud services. Specifically, we formulate the federated learning platform data value maximization problem as an integer programming model with multiple constraints, develop a resource allocation algorithm based on the monotone submodular value function, devise a payment algorithm based on critical price theory and demonstrate that the mechanism satisfies truthfulness and individual rationality.

show abstract

Section: Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

Liu,

Zhang,

Wang

et al. 2023

Mathematics

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A Novel Clinching Auction Mechanism for Edge Computing Resource Allocation With Budget Limits

Zhang,

Sun,

Wang

et al. 2024

Trans Emerging Tel Tech

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An auction mechanism is an effective resource allocation method that can increase the revenue of resource providers in the field of edge computing. Existing auction mechanism designs mostly aim to maximize social welfare when allocating resources, but these schemes lead to low revenue. In contrast, clinching auctions have achieved good results in spectrum allocation and advertising due to their high revenue. Therefore, a clinching auction mechanism is a promising tool for allocating edge computing resources. However, clinching auctions have the drawback that they can only allocate homogeneous finitely divisible goods, meaning that they cannot be directly applied for resource allocation in edge computing. This article presents two new auction mechanisms that improve on the clinching auction. Specifically, based on the principle of increasing global prices and local competition, two mechanisms are designed, one from the perspective of resource providers (MDCAM‐ECS) and the other from the perspective of users (MDCAM‐User), to solve the problem of edge computing resource allocation and pricing with deployment constraints and user budget constraints. The mechanisms proposed in this article have the properties of individual rationality, truthfulness, and computational efficiency. In the experiments, in terms of social welfare and revenue, our algorithms can achieve a 20% improvement over existing algorithms, such as fixed‐price, Vickery–Clarke–Groves (VCG), and monotonic critical‐price mechanisms. Additionally, in most experiments, our algorithm can ensure resource utilization greater than 80%.

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Lowest revenue limit-based truthful auction mechanism for cloud resource allocation

Zhang,

Sun,

2023

J Supercomput

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Multi-Block Ascending Auctions for Effective 5G Licensed Shared Access

Cited by 6 publications

References 29 publications

A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

A Novel Clinching Auction Mechanism for Edge Computing Resource Allocation With Budget Limits

Lowest revenue limit-based truthful auction mechanism for cloud resource allocation

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