Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Authors

Yuan Wu, Zhejiang University of TechnologyFollow
Xiangxu Chen, Zhejiang University of TechnologyFollow
Jiajun Shi, Zhejiang University of TechnologyFollow
Kejie Ni, Zhejiang University of TechnologyFollow
Liping Qian, Zhejiang University of TechnologyFollow
Liang Huang, Zhejiang University of TechnologyFollow
Kuan Zhang, University of Nebraska - LincolnFollow

ORCID IDs

0000-0002-2777-7915

0000-0001-6210-2617

Document Type

Article

Date of this Version

2018

Citation

Sensors 2018, 18, 3472

Comments

Copyright 2018 by the authors.

Open access

doi:10.3390/s18103472

Abstract

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.