Bernhard Scholz

Luigi Crisci, Lorenz Schuler, Herbert Jordan et al.

The Ethereum state database uses Merkle Patricia Trie (MPT), which suffers from large witness proof sizes and high storage overhead. Verkle Tries have been proposed as a replacement, offering witness proofs below 150 bytes through vector commitments and Inner Product Argument aggregation. However, deploying a Verkle Trie in a high-throughput, short block-time blockchain such as Sonic, which produces a block every 300 milliseconds, introduces substantial engineering challenges related to storage efficiency, commitment computation costs, and the need to serve both live and historical state queries in real time. We present SonicDB S6, a production Rust Verkle Trie database for the Sonic blockchain, which leverages its non-forking property to enable aggressive storage optimizations. Occupancy-aware node specializations, selected via an O(kn2) dynamic program, reduce live storage by 98%. Delta nodes that record only changed slots reduce archive storage by 95%. Batched updates, multi-threaded commitment computation, and homomorphic Pedersen caching yield 2.85x higher throughput than a persistent Geth Verkle baseline while sustaining production block-rate performance.

Kirk Baird, Seongho Jeong, Yeonsoo Kim et al.

Ethereum is a distributed blockchain that can execute smart contracts, which inter-communicate and perform transactions automatically. The execution of smart contracts is paid in the form of gas, which is a monetary unit used in the Ethereum blockchain. The Ethereum Virtual Machine (EVM) provides the metering capability for smart contract execution. Instruction costs vary depending on the instruction type and the approximate computational resources required to execute the instruction on the network. The cost of gas is adjusted using transaction fees to ensure adequate payment of the network. In this work, we highlight the "real" economics of smart contracts. We show that the actual costs of executing smart contracts are disproportionate to the computational costs and that this gap is continuously widening. We show that the gas cost-model of the underlying EVM instruction-set is wrongly modeled. Specifically, the computational cost for the SLOAD instruction increases with the length of the blockchain. Our proposed performance model estimates gas usage and execution time of a smart contract at a given block-height. The new gas-cost model incorporates the block-height to eliminate irregularities in the Ethereum gas calculations. Our findings are based on extensive experiments over the entire history of the EVM blockchain.

Wasuwee Sodsong, Bernhard Scholz, Sanjay Chawla

Program analysis is a technique to reason about programs without executing them, and it has various applications in compilers, integrated development environments, and security. In this work, we present a machine learning pipeline that induces a security analyzer for programs by example. The security analyzer determines whether a program is either secure or insecure based on symbolic rules that were deduced by our machine learning pipeline. The machine pipeline is two-staged consisting of a Recurrent Neural Networks (RNN) and an Extractor that converts an RNN to symbolic rules. To evaluate the quality of the learned symbolic rules, we propose a sampling-based similarity measurement between two infinite regular languages. We conduct a case study using real-world data. In this work, we discuss the limitations of existing techniques and possible improvements in the future. The results show that with sufficient training data and a fair distribution of program paths it is feasible to deducing symbolic security rules for the OpenJDK library with millions lines of code.