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Supplementary information for Learning-based mobile edge computing resource management to support public blockchain networks

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posted on 2024-10-11, 10:04 authored by Alia AsheralievaAlia Asheralieva, Dusit Niyato

Article abstract

We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-Term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully-and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.

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Funding

National Natural Science Foundation of China (NSFC): project 61950410603

Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST? SCI2019-0007

A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing RGANS1906

WASP/NTU: grant M4082187 (4080)

Singapore MOE Tier 1: grant 2017-T1-002-007 RG122/17

Singapore MOE Tier 2: grant MOE2014-T2-2-015 ARC4/15

Singapore: grant NRF2015-NRF-ISF001-2277

Singapore EMA Energy Resilience: grant NRF2017EWT-EP003-041

History

School

  • Science

Department

  • Computer Science