Making bitcoinethereum hash more efficient

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We have been conducting a longitudinal study of the state of cryptocurrency networks, including Bitcoin and Ethereum. We have just made public our results from making bitcoinethereum hash more efficient study spanning toin a peer-reviewed paper about to be presented at the upcoming Financial Cryptography and Data Security conference in February [1].

Bitcoin nodes generally have higher bandwidth allocated to them than Ethereum. Compared to our previous study inwe see making bitcoinethereum hash more efficient the median bandwidth making bitcoinethereum hash more efficient a Bitcoin node has increased by a factor of 1. The typical Bitcoin node has much more bandwidth available to it than it did before. Higher allocated bandwidth indicates that the maximum blocksize can be increased without impacting orphan rates, which in turn affect decentralization.

If people were happy about the level of decentralization inthey should be able to increase the block size by 1. Some people argue that increasing the maximum block size would also prohibitively increase CPU and disk requirements.

Yet these costs were trivial in the first place, especially compared to today's transaction fees, and have come down drastically. To date, we have seen no sound, quantitative arguments for any specific value of the maximum block size making bitcoinethereum hash more efficient Bitcoin. Arguments on this topic have consisted of vague, technical-sounding-yet-technically-unjustified argumentation, bereft of scientific justification.

The dissonance between the technical-soundiness of the arguments and the actual technical facts on the ground is disconcerting for a technological making bitcoinethereum hash more efficient [3].

Compared to Ethereum, Bitcoin nodes tend to be more clustered together, both in terms of network latency as well as geographically. Put another way, there are more Ethereum nodes, and they are better spread out around the world. That indicates that the full node distribution for Ethereum is much more decentralized.

Part of the reason for this is that a much higher percentage of Bitcoin nodes reside in datacenters. Nodes that reside in datacenters may indicate an increased level of corporatization.

They may also be a symptom of nodes deployed to skew node counts for various different implementations a. The entire blockchain for both systems is determined by fewer than 20 mining entities [4]. While traditional Byzantine quorum systems operate in a different model than Making bitcoinethereum hash more efficient and Ethereum, a Byzantine quorum system with 20 nodes would be more decentralized than Bitcoin or Ethereum with significantly fewer resource costs.

Of course, the design of a quorum protocol that provides open participation, while fairly selecting 20 nodes to sequence transactions, is non-trivial. Thus, we see that more research is needed in this area to develop permissionless consensus protocols that are also energy efficient. Ethereum has a much higher uncle rate than Bitcoin's pruned block rate. This is by design, as Ethereum operates its network closer to its physical limits and achieves higher throughput.

As making bitcoinethereum hash more efficient result, however, less of Ethereum's hash power goes towards sequencing transactions than Bitcoin's. 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 making bitcoinethereum hash more efficient reducing uncle and orphan rates.

Fairness is an important metric: 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 making bitcoinethereum hash more efficient of fairnesswith Bitcoin exhibiting high variance.

That is to say, mining making bitcoinethereum hash more efficient 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 making bitcoinethereum hash more efficient 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. Decentralization in Bitcoin and Ethereum bitcoin ethereum Monday January 15, at 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. 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 Coefficientthat attempts to capture centralization across different fields.

Our personal experience was more drastic than the industry average, closer to making bitcoinethereum hash more efficient 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. This argument is incorrect for a few reasons: In short, pools providing any level of decentralized decision making is more aspirational talk than a proven reality. Our study examines solely the networks and the blockchain maintained by those networks.

Historical price data is notoriously difficult to find, for some reason. Concomittantly, Bitcoin Core has adopted a narrative that it is a Store of Value, in effect making it explicit making bitcoinethereum hash more efficient the token is not a technological artifact meant to facilitate payments, but an investment vehicle where early adopters are compensated by late comers. Of course, some of these entities are pools.

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Announcing World Trade Francs: The Official Ethereum Stablecoin 01st April, Ethereum scalability research and development subsidy programs 02nd January, Merkle trees are a fundamental part of what makes blockchains tick.

Although it is definitely theoretically possible to make a blockchain without Merkle trees, simply by creating giant block headers that directly contain every transaction, doing so poses large scalability challenges that arguably puts the ability to trustlessly use blockchains out of the reach of all but the most powerful computers in the long term.

Thanks to Merkle trees, it is possible to build Ethereum nodes that run on all computers and laptops large and small, smart phones, and even internet of things devices such as those that will be produced by Slock. So how exactly do these Merkle trees work, and what value do they provide, both now and in the future?

The most common and simple form of Merkle tree is the binary Mekle tree, where a bucket always consists of two adjacent chunks or hashes; it can be depicted as follows:.

So what is the benefit of this strange kind of hashing algorithm? Why not just concatenate all the chunks together into a single big chunk and use a regular hashing algorithm on that? The answer is that it allows for a neat mechanism known as Merkle proofs:. Someone reading the proof can verify that the hashing, at least for that branch, is consistent going all the way up the tree, and therefore that the given chunk actually is at that position in the tree.

The application is simple: Then, a user who wants to do a key-value lookup on the database eg. It allows a mechanism for authenticating a small amount of data, like a hash, to be extended to also authenticate large databases of potentially unbounded size. The original application of Merkle proofs was in Bitcoin, as described and created by Satoshi Nakamoto in The Bitcoin blockchain uses Merkle proofs in order to store the transactions in every block:.

If the light client wants to determine the status of a transaction, it can simply ask for a Merkle proof showing that a particular transaction is in one of the Merkle trees whose root is in a block header for the main chain.

This gets us pretty far, but Bitcoin-style light clients do have their limitations. One particular limitation is that, while they can prove the inclusion of transactions, they cannot prove anything about the current state eg.

How many bitcoins do you have right now? To get around this, Ethereum takes the Merkle tree concept one step further. Every block header in Ethereum contains not just one Merkle tree, but three trees for three kinds of objects:.

This allows for a highly advanced light client protocol that allows light clients to easily make and get verifiable answers to many kinds of queries:. The first is handled by the transaction tree; the third and fourth are handled by the state tree, and the second by the receipt tree. The first four are fairly straightforward to compute; the server simply finds the object, fetches the Merkle branch the list of hashes going up from the object to the tree root and replies back to the light client with the branch.

The fifth is also handled by the state tree, but the way that it is computed is more complex. Here, we need to construct what can be called a Merkle state transition proof. To compute the proof, the server locally creates a fake block, sets the state to S, and pretends to be a light client while applying the transaction.

That is, if the process of applying the transaction requires the client to determine the balance of an account, the light client makes a balance query. If the light client needs to check a particular item in the storage of a particular contract, the light client makes a query for that, and so on. The server then sends the client the combined data from all of these requests as a proof.

The client then undertakes the exact same procedure, but using the provided proof as its database ; if its result is the same as what the server claims, then the client accepts the proof.

For the state tree, however, the situation is more complex. The state in Ethereum essentially consists of a key-value map, where the keys are addresses and the values are account declarations, listing the balance, nonce, code and storage for each account where the storage is itself a tree. For example, the Morden testnet genesis state looks as follows:.

Unlike transaction history, however, the state needs to be frequently updated: What is thus desired is a data structure where we can quickly calculate the new tree root after an insert, update edit or delete operation, without recomputing the entire tree.

There are also two highly desirable secondary properties:. The Patricia tree , in simple terms, is perhaps the closest that we can come to achieving all of these properties simultaneously. Each node has 16 children, so the path is determined by hex encoding: In practice, there are a few extra optimizations that we can make to make the process much more efficient when the tree is sparse, but that is the basic principle.

The two articles mentioned above describe all of the features in much more detail. A timely article showing clearly that Merkle trees and their variants are the foundation and future of blockchain technology! One question is how to leverage their power to create Ethereum 2.

I wrote this article exactly so that I can have something to point to and otherwise just assume background knowledge of Merkle trees for my upcoming article on Ethereum 2. You may use these HTML tags and attributes: Merkling in Ethereum Introduction. The Official Ethereum Stablecoin 01st April, Ethereum scalability research and development subsidy programs 02nd January, Author Simon Janin Posted at 4: Author Vitalik Buterin Posted at Author bruno cecchini Posted at Author Uri Yurman Posted at Just a small typo in the last paragraph: Author Michael Kilday Posted at Cool explanation of the basics of blockchaining, I never knew this.

Author pardha saradhi kuchipudi Posted at 2:

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