Computation offloading and pricing in mobile edge computing based on Stackelberg game

Liu, Zongyun; Fu, Jingqi; Zhang, Yue

doi:10.1007/s11276-021-02767-z

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…JB Xue also discussed dynamic pricing strategies for edge cloud resources based on Stackelberg games, aiming to enhance user service quality while ensuring fairness [33]. Task offloading and resource pricing strategies based on Stackelberg games are simultaneously considered in [34]. In the context of edge-enabled Internet of Things, resource pricing problems are explored using Stackelberg games, demonstrating the advantages of game-based pricing through comparison with other methods [35].…”

Section: Related Workmentioning

confidence: 99%

Tripartite Game Theory-Based Edge Resource Pricing Approach for Edge Federation

Ye,

Feng,

2024

Preprint

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Mobile Edge Computing (MEC) deploys edge servers close to end-users to address their computing requirements, with an emphasis on minimizing latency and enhancing security. However, the significant deployment costs associated with edge servers and the potential for resource wastage due to redundant deployment by various Edge Infrastructure Providers (EIPs) highlight the importance of adopting edge federation architecture. This architectural approach aims to deliver superior computing services tailored for MEC scenarios. Nevertheless, effective resource allocation and pricing strategies are crucial to ensure optimal resource sharing within the edge federation. This paper presents a game-theoretic model involving three primary stakeholders: terminal users, resource demanders within the federation, and resource providers within the federation. The game among these stakeholders is structured into two levels, with negotiation rounds playing a pivotal role. At the first level, the game outlines the initial resource demands of terminal users and the initial pricing by EIPs, while also considering the influence of historical pricing trends on resource demands. The second level focuses on defining the psychological expectations of resource demanders within the federation and the initial pricing by resource providers. To solve this game model, this paper introduces the Tripartite Game Pricing Approach (TGPA). Simulation results corroborate that TGPA outperforms existing pricing approaches, yielding higher total revenue for the edge federation and ensuring a certain utilization of computational resources within acceptable time constraints.

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Section: Related Workmentioning

confidence: 99%

Tripartite Game Theory-Based Edge Resource Pricing Approach for Edge Federation

Ye,

Feng,

2024

Preprint

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“…From the standpoint of the operator, the pricing calculation is about lowering the cost of utilizing resources as well as the service latency to achieve MDs' QoS. The strategies used to tackle interactive problems are typically based on game theory [170] [157] [195], although the techniques for decision-making might range from Lyapunov optimization [190] to dynamic programming [182] [193] [181] [196] to deep learning [192] and reinforcement learning [76] [197].…”

Section: Price Strategy For Multi-tier Networkmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Dynamic Offloading Based on Meta Deep Reinforcement Learning and Load Prediction in Smart Home Edge Computing

Li,

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Computation offloading and pricing in mobile edge computing based on Stackelberg game

Cited by 8 publications

References 28 publications

Tripartite Game Theory-Based Edge Resource Pricing Approach for Edge Federation

Tripartite Game Theory-Based Edge Resource Pricing Approach for Edge Federation

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Dynamic Offloading Based on Meta Deep Reinforcement Learning and Load Prediction in Smart Home Edge Computing

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