Cloud/fog computing resource management and pricing for blockchain networks

Zehui Xiong, Shaohan Feng, Wenbo Wang, Dusit Niyato, Ping Wang, Zhu Han

Research output: Contribution to journalArticlepeer-review

249 Scopus citations

Abstract

Public blockchain networks using proof of work (PoW)-based consensus protocols are considered as a promising platform for decentralized resource management with financial incentive mechanisms. In order to maintain a secured, universal state of the blockchain, PoW-based consensus protocols financially incentivize the nodes in the network to compete for the privilege of block generation through cryptographic puzzle solving. For rational consensus nodes, i.e., miners with limited local computational resources, offloading the computation load for PoW to the cloud/fog providers (CFPs) becomes a viable option. In this paper, we study the interaction between the CFPs and the miners in a PoW-based blockchain network using a game theoretic approach. In particular, we propose a lightweight infrastructure of the PoW-based blockchains, where the computation-intensive part of the consensus process is offloaded to the cloud/fog. We formulate the computation resource management in the blockchain consensus process as a two-stage Stackelberg game, where the profit of the CFP and the utilities of the individual miners are jointly optimized. In the first stage of the game, the CFP sets the price of offered computing resource. In the second stage, the miners decide on the amount of service to purchase accordingly. We apply backward induction to analyze the subgame perfect equilibria in each stage for both uniform and discriminatory pricing schemes. For uniform pricing where the same price applies to all miners, the uniqueness of the Stackelberg equilibrium is validated by identifying the best response strategies of the miners. For discriminatory pricing where the different prices are applied, the uniqueness of the Stackelberg equilibrium is proved by capitalizing on the variational inequality theory. Further, the real experimental results are employed to justify our proposed model.

Original languageEnglish
Article number8470083
Pages (from-to)4585-4600
Number of pages16
JournalIEEE Internet of Things Journal
Volume6
Issue number3
DOIs
StatePublished - Jun 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Funding

Manuscript received May 28, 2018; revised August 29, 2018; accepted September 17, 2018. Date of publication September 24, 2018; date of current version June 19, 2019. This work was supported in part by WASP/NTU under Grant M4082187 (4080), in part by the Singapore MOE Tier 1 under Grant 2017-T1-002-007 RG122/17 and MOE Tier 2 under Grant MOE2014-T2-2-015 ARC4/15 and Grant NRF2015-NRF-ISF001-2277, in part by EMA Energy Resilience under Grant NRF2017EWT-EP003-041, in part by the U.S. MURI, and in part by the NSF under Grant CNS-1717454, Grant CNS-1731424, Grant CNS-1702850, Grant CNS-1646607, and Grant ECCS-1547201. An earlier version of this paper was accepted by IEEE ICC in [1]. (Corresponding author: Ping Wang.) Z. Xiong, S. Feng, W. Wang, and D. Niyato are with the School of Computer Science and Engineering, Nanyang Technological University, Singapore.

FundersFunder number
EMA Energy ResilienceNRF2017EWT-EP003-041
WASP
National Science FoundationCNS-1702850, CNS-1717454, CNS-1731424, CNS-1646607, ECCS-1547201
Nanyang Technological UniversityMOE2014-T2-2-015 ARC4/15, M4082187 (4080, 2017-T1-002-007 RG122/17, NRF2015-NRF-ISF001-2277

    Keywords

    • Blockchain
    • Computation offloading
    • Game theory
    • Pricing
    • Proof-of-work
    • Variational inequalities (VIs)

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