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Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing

delete2022-12-01
delete32
PRE
AI
S
Su Yao
M
Mu Wang *
曲强 封面图
曲强 (Qiang Qu)
Z
Ziyi Zhang
Y
Yifeng Zhang
K
Ke Xu
徐
徐明伟 (Mingwei Xu)
DOI:10.1109/JSAC.2022.3213358delete
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摘要

摘要

En 中文
How to enable high-performance task offloading and preserve the trust between participants is imperative yet nontrivial to the Cloud-Edge-Device (CED) computing, mainly because the resources are geo-distributed and operated by different parties. Also, the CED participants are highly dynamic and heterogeneous in resource provision and may conflict in interest. This paper proposes BlockChain-empowered CED (BCCED), a blockchain-empowered collaborative task offloading for CED computing. In BC-CED, blockchain plays a central role in the main functionality of CED, including task offloading, brokerage of resource usage, and incentives. We distinguish the BC-CED from the existing solutions by modifying the blockchain consensus process, enabling the participants to reach an agreement via solving the task offloading problem. For this purpose, we formulate the offloading problem by considering the computation capabilities of candidate nodes and the network performance. BC-CED allows each participant to apply reinforcement learning-based methods to solve this problem and compete for the right of block output by comparing the offloading policy performance and accepting the best policy as the offloading scheme within the next period. We also propose a truthful incentive mechanism to encourage resource contributions in BC-CED and force them to he honest. Extensive tests by implementing our solutions in a commercialized blockchain platform have shown how BC-CED achieves a superior performance in task offloading and blockchain maintenance.
Keyword:
Cloud-edge-device computing
task offloading
blockchain
reinforcement learning

期刊

IEEE Journal on Selected Areas in Communications 封面图
IEEE Journal on Selected Areas in Communications
IF:
17.2
论文数:
6.4K
被引数:
3.1W

机构

Z
Zhongguancun Laboratory
学者数:
274
论文数: 200
被引数: 0
T
tsinghua university
学者数:
11.9W
论文数: 10.0W
被引数: 137
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