Making Ethereum Transactions Faster and Cheaper

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

11 CONCLUSION

Transactions are at the forefront on how information is exchanged on the blockchain. Yet, it is generally unclear how long transactions commonly take to be processed in Ethereum. Predicting the processing time of transactions is key to development of cost-effective ÐApps, since developers need to optimize the balance between cost (transaction fees) and user-experience (transaction processing speed). Few online services exist to help smart contract developers estimate how long transactions will take to be processed. Most importantly, the accuracy of these estimation services remain unclear.

In this paper, we collected data from Etherscan, EthGasStation, and Google BigQuery in order to empirically determine how long transactions tend to take in Ethereum. We also evaluate the estimation accuracy of the models employed by the state-of-the-practice estimation services (Etherscan and EthGasStation). Our results led us to conclude that (i) ÐApp developers should typically avoid very expensive transactions, as there is no practical difference in their processing time compared to expensive transactions, (ii) the state-of-the-practice services are far from perfect, leaving considerable room for improvement (especially for cheaper transactions), and (iii) our simple linear regression model outperforms those services for very cheap and cheap transactions. processing times under different settings (e.g., other time frames) and using different techniques (e.g., alternative model ranking approaches) is also a fruitful follow up to our study.

DISCLAIMER

Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not reflect the views of Huawei.

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