How Ethereum and Bitcoin Handle Transaction Delays and Fees
Hu et al. propose a private blockchain to facilitate banking processes and analyze transaction processing times. They conclude that transaction times correlate with block generation times and are unaffected by network congestion due to dynamic block…
Hu et al. propose a private blockchain to facilitate banking processes and analyze transaction processing times. They conclude that transaction times correlate with block generation times and are unaffected by network congestion due to dynamic block difficulty adjustments. However, these findings are not applicable to the main Ethereum blockchain.
Kasahara and Kawahara study the impact of Bitcoin fees on transaction processing times, concluding that smaller fees result in longer processing times. They highlight that increasing the block size does not alleviate congestion issues.
Rouhani and Deters examine transaction processing times in Ethereum clients, finding significant differences between Geth and Parity. The lack of metadata on the client used poses challenges for further analysis.
Pierro and Rocha analyze factors affecting transaction fees in Ethereum, finding that the number of miners and pending transactions significantly impact fees. Chen et al. discuss the security risks associated with low gas prices and propose a dynamic cost adjustment solution to mitigate denial of service attacks.
Transaction fees vary based on gas prices selected by transaction issuers. Tools like Etherscan's Gas Tracker provide recommendations, although the adoption of these suggestions warrants further investigation.
propose a private blockchain to facilitate banking processes and analyze transaction processing times.
Kim et al. develop NodeFinder to explore Ethereum's P2P network, revealing insights about node distribution and client usage. Silva et al. investigate the geo-distribution and mining pool impacts on network efficiency and security, identifying selfish mining behaviors.
Oliva et al. report that a small proportion of popular contracts heavily influence network activity. The congestion caused by high-demand contracts like CryptoKitties highlights the impact on gas prices.
Studies on gas usage estimation include worst-case and exact estimations. Marescotti et al. and Albert et al. focus on upper bounds for gas usage, while Das and Qadeer introduce GasBoX for exact estimations. Zarir et al. propose a historical method for gas usage prediction, suggesting improvements for smart contract documentation and tools like Etherscan.
Zou et al. identify challenges in gas optimization through developer surveys. Chen et al. create GASPER to detect inefficient programming patterns. Brandstätter et al. propose strategies for code optimization, demonstrating potential cost savings in transaction fees.
Michael Pacheco, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada Gustavo A. Oliva, Software Analysis and Intelligence Lab (SAIL) at Queen’s University, Canada Gopi Krishnan Rajbahadur, Centre for Software Excellence at Huawei, Canada 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.
Based on reporting by hackernoon.com.