EIP-4844 对 L2 用户意味着什么？

As of the writing of this article and the rest of the upgrade, the author co-founder source translation Shanouba Bitcoin Trading Network is currently planned to go online on the main network of Ethereum on January. The next Ethereum hard fork has been running on the test network and will soon enter the main network, which is a big news for users, because it brings a new data availability system, which is expected to greatly reduce the cost. If you use something like this, how will it affect you? Let's investigate the data availability and spend a lot of money. To ensure that its transaction data can be used by all nodes and verifiers, most of the summarization is realized by writing data into Ethereum as call data, and the cost per megabyte is about $ at the current price. Good summarization reduces the cost to $ per megabyte by using advanced data compression. Nevertheless, the data publishing cost still accounts for the largest part of the transaction cost. Why is Ethereum so expensive? Because the demand of Ethereum is great, it must be higher than other parties who want to use Ethereum resources. Higher bids add a new type of data availability to Ethereum, which is called data lack. They are not applicable to Ethereum contracts and can only store days, but they meet the data availability requirements. Therefore, they provide a new hope and a cheaper way to record how much their data is cheaper. This is the key issue, the basic knowledge. Each Ethereum block can contain up to 10 targets. The price of each block containing 10 will fluctuate with time. If there are more than one in the Ethereum block, the price will go up; if there are less than one, the price will go down. A price regulator like this works like a thermostat. If your home is too cold, the thermostat will turn up the heater to make the room warm. If your home is too hot, the thermostat will turn off the heater, and the result is that the temperature will remain at the required level. The pricing is similar, but it does not seek a constant temperature, but seeks a constant utilization rate. If too much is used in summary, the price will go up, so the summary will be reduced. If the sum used is less than the target quantity, the price will drop to encourage more use. The result should be a balance, in which each Ethereum block uses about 100 and the price is set properly to maintain this. Of course, the conditions will change with time, so the equilibrium price may change with the change of conditions, but the average use of 100 is valid, just like how a thermostat keeps your home at a suitable temperature when the outdoor weather changes. The heater is more powerful, which puts forward the method of finding the equilibrium price. We can ask ourselves how much we will use if the price is cents, what if the price is dollars, what if dollars, etc. The equilibrium price will be the price of just one for each Ethereum block, so let's think about how to use a good summary, which will be used in the most suitable way for the summary users. The users hope to get two main things from the summarized data poster. The two goals of low cost and rapid release are tense to use. The lowest cost method is to wait until the summary has enough data to fill before purchasing and publishing the data. However, if the summary does not wait that long, but publishes some parts more frequently, the publishing speed will be faster, which will be more expensive, because even if you don't use them all, you have to pay for the whole, so the frequent publishing strategy will buy more. How to balance these factors depends on the price. If it is very expensive, you want to use as little as possible, so it is worth waiting for a longer time and publishing. On the other hand, if it is very cheap, you can publish it very quickly, and most of it is empty without any cost. The lower the price, the more frequently you publish it, and the more empty it will be. This research paper makes this trade-off more accurate by allocating the nominal dollar cost to the time spent waiting for publication and then finding the usage that minimizes the sum of the cost plus the waiting cost. As suggested above, the result is that the usage is inversely proportional to the price, and they are used when the price is higher. Less, when the price is lower, they use more, and these are not full. The surprising conclusion of this study is that the summary with more transactions is better than that with less busy transactions, and the trade-off between the cost and the final time of the busy summary is less harsh, because the busy summary will accumulate complete data blocks faster. If the busy summary makes wise decisions, it can have better cost and faster than the less busy competitors. For example, if the traffic is twice as high, then if both of them make the best decision in the cost delay trade-off, the data cost of each transaction will be lower than about, and the transaction speed will be increased. In some cases, small aggregation will prefer to use Ethereum to call data instead of this, because when you use it, whether you use it or not, you have to pay for the whole number of bytes used, so low traffic aggregation may find that waiting for the establishment of a large batch will give. Users bring too much delay and the cheapest way to publish a small batch after deciding to use it is to buy a small amount of Ethereum call data instead of a whole block. In short, a smaller summary faces a worse trade-off than a large summary. What can a smaller summary do? The best strategy for a smaller summary is to team up and share it. For example, suppose that they want to publish a full one now and want to publish a full one. If they cooperate, they can publish a single one containing two data. It is a win-win situation to share a single cost instead of buying one for each person, but why stop there? If two summaries form an alliance and always publish together, they can get lower cost and better delay than those obtained separately, which is a long-term win-win situation. Even the busiest summary can sometimes benefit from teamwork. The busy summary may accumulate enough things of its own to fill, and then sell the remaining space to the less busy summary instead of waiting for enough. Enough transactions are completely filled, which may or may not be a win-win situation for both. When sharing one, they can save money. How should they allocate their savings or equally, how should they share the cost of publishing and sharing? An obvious method is to pay in proportion to the amount of data they use, because the summary of the data it provides will pay for the data it provides. This is one way. Another way of saying this is that if the cost is reduced due to cooperation, it should also be reduced, which shows that each of them should share. 比特币今日价格行情网_okx交易所app_永续合约_比特币怎么买卖交易_虚拟币交易所平台

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