Geopositioning of fog nodes based on user device location and framework for game theoretic applications in an fog to cloud network

Bandyopadhyay, Anjan; Datta, Utsav; Banik, Antara; Biswas, Pratyay; Sarkar, Vaskar

doi:10.1016/b978-0-12-822844-9.00030-x

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main goal of our work is to provide an effective dynamic pricing scheme that, when combined with resource allocation, overcomes the constraints of the existing pricing schemes used in the current fog computing deployment. We build upon the framework proposed in reference [44], which aims to maximize social welfare through a value-based economic mechanism. Specifically, we focus on designing an efficient dynamic pricing scheme for users facing time constraints in completing their desired tasks within the specified completion time.…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing

Bandopadhyay,

Swain,

Singh

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.

show abstract

Section: Introductionmentioning

confidence: 99%