Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing

Wang, Quyuan; Guo, Songtao; Liu, Jiadi; Pan, Chengsheng; Yang, Li

doi:10.1109/tsc.2019.2924002

Cited by 80 publications

(35 citation statements)

References 32 publications

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“…Wang et al [152] propose a profit maximization multiround auction considering a trusted third party as an auctioneer. In their proposed model, first the users submit their resource requirement to the auctioneer and then the providers submit their bids accordingly to it.…”

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Sharghivand¹,

Derakhshan

Siasi

2021

IEEE Access

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Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resources management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids-forward, reverse, and two-sided auctions, (2) the heterogeneity of resources-combinatorial auctions, (3) the number and types of adopted attributes in winner determination-multi-attribute auctions, (4) the setting of auction-offline, online, or sequential auctions, and (5) the interoperability among cloud providers-inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented, followed by existing challenges and research directions.INDEX TERMS Auction mechanism design, cloud computing, edge computing, resource management, resource pricing.

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Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Sharghivand¹,

Derakhshan

Siasi

2021

IEEE Access

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“…We compare the network throughput for the different transmission radii and different strategies as simulation time increases. Furthermore, we compare the network throughput of our algorithm, flow split optimization (FSO) algorithm [4] and profit maximization multi-round auction (PMMRA) algorithm [5]. Fig.…”

Section: Comparison Of Network Throughputmentioning

confidence: 99%

“…The proposed flow split optimization (FSO) algorithm in [4] selects the relay nodes with the smallest redundant data flow as the optimal transmission path, which can minimize the traffic load of software defined wireless sensor networks (SDWSNs). And the profit maximization multi-round auction (PMMRA) algorithm in [5] is…”

Section: Introductionmentioning

confidence: 99%

“…Although these scheduling strategies [4], [5] can improve network performance to a certain degree, but they have certain limitations in network architecture and data processing. Among the above two strategies, novel software definition and edge computing techniques are used separately.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these limitations motivate us to propose an effective strategy for achieving energy consumption averaging and minimization in ESDWSNs. On the other hand, the existing schedule strategies [4], [5] are limited to the counterpoise of energy consumption and data processing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Consumption Averaging and Minimization for the Software Defined Wireless Sensor Networks With Edge Computing

2019

IEEE Access

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The software-defined (SD) and edge computing (EC) are emerging technologies that have been used to improve the network operation efficiency of wireless sensor networks (WSNs). Due to the advantages of the SD and EC technologies, the area of WSNs has achieved a new dimension and breakthrough. However, the limited energy allocation mechanism in edge-SD wireless sensor networks (ESDWSNs) makes the energy consumption of different nodes unbalanced. In this paper, we propose an energy allocation optimization (EAO) algorithm that solves the energy averaging and minimization (ECAM) problem in ESDWSNs by selecting appropriate relay nodes and de-duplicated data flows. Specifically, we first establish a novel three-layer network architecture based on the edge computing and software-defined technologies. Then we proposed the ECAM problem, which minimizes the energy consumption in ESDWSNs. Furthermore, we propose an adaptive Levenberg-Marquardt algorithm and derive the optimization value of energy cost function. The extensive simulation results based on the NS-2 simulator demonstrate the energy balance efficiency of the EAO algorithm in ESDWSNs. INDEX TERMS Edge computing (EC) technology, energy allocation optimization (EAO) algorithm, energy consumption averaging and minimization, software-defined (SD) technology, wireless sensor networks (WSNs).

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An Efficient Mechanism for Resource Allocation in Mobile Edge Computing

Zeng

Zhang

2020

Combinatorial Optimization and Applications

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Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing

Cited by 80 publications

References 32 publications

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Energy Consumption Averaging and Minimization for the Software Defined Wireless Sensor Networks With Edge Computing

An Efficient Mechanism for Resource Allocation in Mobile Edge Computing

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