Centralisation in ethereum and bitcoin networks

Извиняюсь, биткоин сатоши то

This is by design, as Ethereum operates its network closer to its physical limits and achieves higher throughput. Put another way, some hash power is wasted on uncles, which do not help carry out directly useful sequencing work on the chain. Relay networks ferry blocks quickly among miners and full nodes, and help reduce wasted effort by reducing uncle and orphan rates. Fairness is an important metric: it determines whether a small miner is at a greater disadvantage compared to a larger miner.

If a system is perfectly fair, there would be fewer reasons for miners to pool their resources into larger, cooperating pools that operate in unison. To measure fairness, we looked at the proportion of blocks that miners have on the main chain divided by the proportion of their blocks that did not help advance the blockchain, namely, pruned blocks and uncles. In an ideal system, this metric would be equal to 1.

The level of fairness in both systems is, roughly speaking, comparable. But there is a big difference in variance of fairness , with Bitcoin exhibiting high variance. That is to say, mining rewards are more unpredictable for smaller miners in Bitcoin. This is partly because the high block rate in Ethereum helps provide many more opportunities for the laws of large numbers to apply in Ethereum, while Bitcoin, with its infrequent blocks, can exhibit much more uncertainty from month to month.

The full details, of how we measured the data and what we found in more precise terms, are in our paper. Gencer is a researcher at LinkedIn. His thesis research focused on improving the scalability of blockchain technologies. Soumya Basu is a graduate student at Cornell University.

His research interests span the systems aspects of blockchains and cryptocurrencies. My Research Interests are distributed systems and algorithms, specifically distributed storage algorithms, the distributed aspects of Bitcoin, and reliable aggregation in distributed sensor networks. Hacker and professor at Cornell, with interests that span distributed systems, OSes and networking. Hacking, Distributed. Bitcoin Underutilizes Its Network Bitcoin nodes generally have higher bandwidth allocated to them than Ethereum.

Ethereum is Better Distributed Than Bitcoin Compared to Ethereum, Bitcoin nodes tend to be more clustered together, both in terms of network latency as well as geographically. In contrast, Ethereum nodes tend to be located on a wider variety of autonomous systems. Ethereum Exhibits Better Variance in Fairness, Favoring Small Miners Fairness is an important metric: it determines whether a small miner is at a greater disadvantage compared to a larger miner.

More The full details, of how we measured the data and what we found in more precise terms, are in our paper. Footnotes [1] Our study examines solely the networks and the blockchain maintained by those networks. It does not examine development centralization.

Balaji Srinivasan and Leland Lee have developed a metric, called the Nakamoto Coefficient , that attempts to capture centralization across different fields. Our personal experience was more drastic than the industry average, closer to a 2X drop in price over the same time frame. And some people will claim that pools provide decentralization, because they are composed of multiple independent actors.

In short, pools providing any level of decentralized decision making is more aspirational talk than a proven reality. Ongoing research explores ways to make the Bitcoin and Ethereum networks more decentralized without measurements on the underlying network. Hence, debates and decisions about the underlying networks are often based on assumptions rather than measurement.

In this paper, we present a comprehensive measurement study on decentralization metrics in these operational systems and shed light on whether or not existing assumptions are satisfied in practice. The section I personally found most interesting was the one on the distribution of mining power in practice. In both Bitcoin and Ethereum, miners voluntarily disclose their identity as part of each block they mine. Collected identities were manually processed to detect and merge duplicates.

Data was gathered over a period of 10 months starting from July 15, Figure 4 shows the top 20 weekly mining power distribution in the Ethereum and Bitcoin networks. Each group of bars represents a chronologically ordered collection of weekly mining power rations, defined as the fraction of blocks contributed by a miner.

Miners do change spots in the rankings over the observation period, but place is contested by only a few miners. Only two Bitcoin and three Ethereum miners ever held the top rank. These results show that a Byzantine quorum system of size 20 could achieve better decentralization than proof-of-work mining at a much lower resource cost. Moreover, miners are incentivised to obfuscate their true power so as not to raise alarms among the community about centralisation.

Mining power utisilation is a measure of the fraction of mined blocks that remain in the main chain. It tells us how efficiently the network converts energy spent into useful work and therefore has a bearing on how much it costs to launch an attack. Next we might ask whether all miners see pruned blocks are on the losing sides of forks with equal probability.

Data on this is super interesting because it gives an indication as to whether more powerful miners are able to take advantage of their position in practice. In a fair protocol, miners generate pruned blocks proportional to their mining power; hence, the fairness is close to 1. A fairness greater than 1 implies that the miner is at a disadvantage, while a fairness less than 1 implies that the miner has an advantage.