Name
Abstract | ||
|---|---|---|
With the increasing difficulty of solo mining in blockchain mining, pool mining has become more and more popular, and most of the miners would like to join a mining pool and contribute their computational power to the pool. When the pool finds a valid block and get the reward from the blockchain network, it will distribute the reward to its miners according to its reward mechanism. In practice, the Pay-Per-Last-N-Shares (PPLNS) mechanism is one of the most commonly used mechanisms by pools, and the pool adopting PPLNS mechanism will distribute the reward to the miners whose reported shares are in the last N shares, according to their proportion of the number of shares in the last N shares. In the PPLNS mechanism, different reporting strategies may bring different rewards for miners. Thus, how to report their found shares to the pool has become an important issue faced by the miners. In this paper, we study the share reporting problem faced by the miners in PPLNS pools, and establish a share reporting optimization model for the miners. We also study the effect of the parameter N in the PPLNS mechanism on the optimal reporting strategies of the miners. With the computational experiments approach, we design experiments to evaluate our proposed share reporting strategies. This work is the first attempt to study the share reporting issue faced by miners in PPLNS pools, and it can provide useful managerial insights for miners when making their share reporting decisions in such pools. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00570 | 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) |
Keywords | Field | DocType |
share reporting strategy, blockchain mining, PPLNS reward, pool mining, computational experiments approach | A share,Computer science,Artificial intelligence,Blockchain,Industrial organization,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Qin | 1 | 65 | 10.85 |
| Yong Yuan | 2 | 239 | 31.09 |
| Fei-Yue Wang | 3 | 5273 | 480.21 |