Links table
Abstract and 1 introduction
2 wallpaper and 2.1 Blockchain
2.2 Transactions
3 an example is a catalyst
4 Computing transactions processing times
5 data collection and 5.1 data sources
5.2 Approach
6 results
6.1 RQ1: How long does it take to process treatment in Ethereum?
6.2 RQ2: How accurate is the time processing time processing time provided by ETHERSCAN and EthgassTation?
7 Is it possible to derive a simpler model? Post -custom study
8 effects
8.1 What about the final users?
9 related work
10 valid threats
11 conclusion and evacuation of responsibility and references
A. Transactions processing times
A.1 is waiting for the time character
A.2 Trying treatment
B. RQ1: Gas distribution per category gas prices
B.1 Allergy Analysis on Lock Lookback
C. RQ2: Summary of Resolution Statistics for prediction models
Post -designated study: Summary of accuracy statistics for prediction models
B RQ1: Gas distribution per category gas prices
The distribution of gas prices for each category of gas price in Figure 21.
B.1 Allergy Analysis on Lock Lookback
In Ethereum, a new mass is attached every 15 seconds on average and each block can contain a limited number of transactions. Therefore, the price of gas for transactions is strongly affected by the relationship provider’s relationship. In other words, to find out if a certain price X is high or low at a certain moment of time, we need to look back in time and note how much exporters pay against their transactions.
There is no consensus on how to increase one to determine the current payment base. EthgassTation, the most popular gas tracking, provides gas prices statistics based on the past 200 pieces[29]. Looking back at 200 blocks is equivalent to backly 200 * 15 seconds = 3000 seconds = 50 minutes. In this study, we look back 120 blocks. Our logical basis was to cover 30 minutes (half an hour). We believe that in practice, looking back half an hour is easier and direct than looking back 50 minutes. We conducted an allergy analysis in order to understand how the choice of mass participation affects the definition of (distribution) for the five gas price categories. We evaluate the following match choices: 60, 120, 180, 200 (Ethgasstation) and 240. The results appear in Figure 22.
As shown in Figure 22, the definition of gas prices categories is skillfully affected only by choosing the appearance of buildings. Therefore, we believe that our conclusions are likely to stick to all the five -appearance options that have been investigated in this allergy analysis. Additional studies on the subject must assess the effect of the mass choice on the definition of gas prices and predicting transactions.
C RQ2: Summary of Resolution Statistics for prediction models
A post -custom less
Authors:
(1) Michael Pacheko, Soft and Intelligence Analysis (SAIL) at Queens University, Canada;
(2) Gustavo A. Oliva, software analysis and intelligence laboratory (SAIL) at Queen University, Canada;
(3) Gopi Krishnan Rajbahadur, Huawei Software Excellence Center, Canada;
(4) Ahmed E. Hassan, Soft and Intelligence Analysis (SAIL) at Queens University, Canada.
[29] https://ethgasstation.info/txpoolreport.php
