Table of Links Abstract and 1 Introduction 2 Background and 2.1 Blockchain 2.2 Transactions 3 Motivating Example 4 Computing Transaction 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 a transaction in Ethereum? 6.2 RQ2: How accurate are the estimates for transaction processing time provided by Etherscan and EthGasStation? 7 Can a simpler model be derived? A post-hoc study 8 Implications 8.1 How about end-users? 9 Related Work 10 Threats to Validity 11 Conclusion, Disclaimer, and References A. COMPUTING TRANSACTION PROCESSING TIMES A.1 Pending timestamp A.2 Processed timestamp B. RQ1: GAS PRICE DISTRIBUTION FOR EACH GAS PRICE CATEGORY B.1 Sensitivity Analysis on Block Lookback C. RQ2: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS D. POST-HOC STUDY: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS B RQ1: GAS PRICE DISTRIBUTION FOR EACH GAS PRICE CATEGORY The gas price distribution for each gas price category is shown in Figure 21. B.1 Sensitivity Analysis on Block Lookback In Ethereum, a new block is appended every 15 seconds on average and each block can contain a limited number of transactions. Therefore, the gas price of transactions is strongly influenced by a demand-supply relationship. In other words, to know whether a given price x is high or low at a particular moment in time, we need to look back in time and observe how much transaction issuers are paying for their transactions. There is no consensus on how further back one should go to determine what the current payment norm is. EthGasStation, the most popular gas tracker, provides gas price statistics based on the past 200 blocks[29]. Looking back 200 blocks is equivalent to looking back 200 * 15 seconds = 3,000 seconds = 50 minutes. In this study, we look back 120 blocks. Our rationale was to cover 30 minutes (half an hour). We believe that, in practice, looking back half an hour is more intuitive and straightforward than looking back 50 minutes. We performed a sensitivity analysis in order to understand how the block lookback choice influences the definition (distribution) of our five gas price categories. We evaluate the following block lookback choices: 60, 120, 180, 200 (EthGasStation) and 240. The results are shown in Figure 22. As depicted in Figure 22, the definition of the gas price categories is only subtly affected by the block lookback choice. Therefore, we believe that our conclusions are likely to hold to all the five lookback choices investigated in this sensitivity analysis. Further studies in the topic should more extensively evaluate the impact of the choice of the block lookback on the definition of gas prices and on the prediction of transaction processing times. C RQ2: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS D POST-HOC STUDY: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS Authors:
(1) MICHAEL PACHECO, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada;
(2) GUSTAVO A. OLIVA, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada;
(3) GOPI KRISHNAN RAJBAHADUR, Centre for Software Excellence at Huawei, Canada;
(4) AHMED E. HASSAN, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. [29] https://ethgasstation.info/txPoolReport.php Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Blockchain 2 Background and 2.1 Blockchain 2.2 Transactions 2.2 Transactions 3 Motivating Example 3 Motivating Example 4 Computing Transaction Processing Times 4 Computing Transaction Processing Times 5 Data Collection and 5.1 Data Sources 5 Data Collection and 5.1 Data Sources 5.2 Approach 5.2 Approach 6 Results 6.1 RQ1: How long does it take to process a transaction in Ethereum? 6.1 RQ1: How long does it take to process a transaction in Ethereum? 6.2 RQ2: How accurate are the estimates for transaction processing time provided by Etherscan and EthGasStation? 6.2 RQ2: How accurate are the estimates for transaction processing time provided by Etherscan and EthGasStation? 7 Can a simpler model be derived? A post-hoc study 7 Can a simpler model be derived? A post-hoc study 8 Implications 8 Implications 8.1 How about end-users? 8.1 How about end-users? 9 Related Work 9 Related Work 10 Threats to Validity 10 Threats to Validity 11 Conclusion, Disclaimer, and References 11 Conclusion, Disclaimer, and References A. COMPUTING TRANSACTION PROCESSING TIMES A. COMPUTING TRANSACTION PROCESSING TIMES A. COMPUTING TRANSACTION PROCESSING TIMES A.1 Pending timestamp A.1 Pending timestamp A.2 Processed timestamp A.2 Processed timestamp B. RQ1: GAS PRICE DISTRIBUTION FOR EACH GAS PRICE CATEGORY B. RQ1: GAS PRICE DISTRIBUTION FOR EACH GAS PRICE CATEGORY B. RQ1: GAS PRICE DISTRIBUTION FOR EACH GAS PRICE CATEGORY B.1 Sensitivity Analysis on Block Lookback B.1 Sensitivity Analysis on Block Lookback C. RQ2: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS C. RQ2: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS C. RQ2: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS D. POST-HOC STUDY: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS D. POST-HOC STUDY: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS D. POST-HOC STUDY: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS B RQ1: GAS PRICE DISTRIBUTION FOR EACH GAS PRICE CATEGORY The gas price distribution for each gas price category is shown in Figure 21. B.1 Sensitivity Analysis on Block Lookback In Ethereum, a new block is appended every 15 seconds on average and each block can contain a limited number of transactions. Therefore, the gas price of transactions is strongly influenced by a demand-supply relationship. In other words, to know whether a given price x is high or low at a particular moment in time, we need to look back in time and observe how much transaction issuers are paying for their transactions. There is no consensus on how further back one should go to determine what the current payment norm is. EthGasStation, the most popular gas tracker, provides gas price statistics based on the past 200 blocks[29]. Looking back 200 blocks is equivalent to looking back 200 * 15 seconds = 3,000 seconds = 50 minutes. In this study, we look back 120 blocks. Our rationale was to cover 30 minutes (half an hour). We believe that, in practice, looking back half an hour is more intuitive and straightforward than looking back 50 minutes. We performed a sensitivity analysis in order to understand how the block lookback choice influences the definition (distribution) of our five gas price categories. We evaluate the following block lookback choices: 60, 120, 180, 200 (EthGasStation) and 240. The results are shown in Figure 22. As depicted in Figure 22, the definition of the gas price categories is only subtly affected by the block lookback choice. Therefore, we believe that our conclusions are likely to hold to all the five lookback choices investigated in this sensitivity analysis. Further studies in the topic should more extensively evaluate the impact of the choice of the block lookback on the definition of gas prices and on the prediction of transaction processing times. C RQ2: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS D POST-HOC STUDY: SUMMARY OF ACCURACY STATISTICS FOR THE PREDICTION MODELS Authors: (1) MICHAEL PACHECO, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada; (2) GUSTAVO A. OLIVA, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada; (3) GOPI KRISHNAN RAJBAHADUR, Centre for Software Excellence at Huawei, Canada; (4) AHMED E. HASSAN, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada. Authors: Authors: (1) MICHAEL PACHECO, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada; (2) GUSTAVO A. OLIVA, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada; (3) GOPI KRISHNAN RAJBAHADUR, Centre for Software Excellence at Huawei, Canada; (4) AHMED E. HASSAN, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv available on arxiv [29] https://ethgasstation.info/txPoolReport.php

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Sensitivity Analysis of Block Lookback Periods on Ethereum Gas Prices

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