An Empirical Study of Speculative Concurrency in Ethereum Smart Contracts

We use historical data to estimate the potential benefit of speculative techniques for executing Ethereum smart contracts in parallel. We replay transaction traces of sampled blocks from the Ethereum blockchain over time, using a simple speculative execution engine. In this engine, miners attempt to execute all transactions in a block in parallel, rolling back those that cause data conflicts. Aborted transactions are then executed sequentially. Validators execute the same schedule as miners. We find that our speculative technique yields estimated speed-ups starting at about 8-fold in 2016, declining to about 2-fold at the end of 2017, where speed-up is measured using either gas costs or instruction counts. We also observe that a small set of contracts are responsible for many data conflicts resulting from speculative concurrent execution

DeFi and NFTs Hinder Blockchain Scalability

Many classical blockchains are known to have an embarrassingly low transaction throughput, down to Bitcoin's notorious seven transactions per second limit.Various proposals and implementations for increasing throughput emerged in the first decade of blockchain research. But how much concurrency is possible? In their early days, blockchains were mostly used for simple transfers from user to user. More recently, however, decentralized finance (DeFi) and NFT marketplaces have completely changed what is happening on blockchains. Both are built using smart contracts and have gained significant popularity. Transactions on DeFi and NFT marketplaces often interact with the same smart contracts. We believe this development has transformed blockchain usage. In our work, we perform a historical analysis of Ethereum's transaction graph. We study how much interaction between transactions there was historically and how much there is now. We find that the rise of DeFi and NFT marketplaces has led to an increase in "centralization" in the transaction graph. More transactions are now interconnected: currently there are around 200 transactions per block with 4000 interdependencies between them. We further find that the parallelizability of Ethereum's current interconnected transaction workload is limited. A speedup exceeding a factor of five is currently unrealistic.Comment: 22 pages, 12 figures, to be published in Financial Cryptography and Data Security (FC), May 202

A true concurrent model of smart contracts executions

The development of blockchain technologies has enabled the trustless execution of so-called smart contracts, i.e. programs that regulate the exchange of assets (e.g., cryptocurrency) between users. In a decentralized blockchain, the state of smart contracts is collaboratively maintained by a peer-to-peer network of mutually untrusted nodes, which collect from users a set of transactions (representing the required actions on contracts), and execute them in some order. Once this sequence of transactions is appended to the blockchain, the other nodes validate it, re-executing the transactions in the same order. The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput. In this paper we propose a true concurrent model of smart contract execution. Based on this, we show how static analysis of smart contracts can be exploited to parallelize the execution of transactions.Comment: Full version of the paper presented at COORDINATION 202

Efficient Concurrent Execution of Smart Contracts in Blockchains using Object-based Transactional Memory

This paper proposes an efficient framework to execute Smart Contract Transactions (SCTs) concurrently based on object semantics, using optimistic Single-Version Object-based Software Transactional Memory Systems (SVOSTMs) and Multi-Version OSTMs (MVOSTMs). In our framework, a multi-threaded miner constructs a Block Graph (BG), capturing the object-conflicts relations between SCTs, and stores it in the block. Later, validators re-execute the same SCTs concurrently and deterministically relying on this BG. A malicious miner can modify the BG to harm the blockchain, e.g., to cause double-spending. To identify malicious miners, we propose Smart Multi-threaded Validator (SMV). Experimental analysis shows that the proposed multi-threaded miner and validator achieve significant performance gains over state-of-the-art SCT execution framework.Comment: 49 pages, 26 figures, 11 table

A theory of transaction parallelism in blockchains

Decentralized blockchain platforms have enabled the secure exchange of crypto-assets without the intermediation of trusted authorities. To this purpose, these platforms rely on a peer-to-peer network of byzantine nodes, which collaboratively maintain an append-only ledger of transactions, called blockchain. Transactions represent the actions required by users, e.g. the transfer of some units of crypto-currency to another user, or the execution of a smart contract which distributes crypto-assets according to its internal logic. Part of the nodes of the peer-to-peer network compete to append transactions to the blockchain. To do so, they group the transactions sent by users into blocks, and update their view of the blockchain state by executing these transactions in the chosen order. Once a block of transactions is appended to the blockchain, the other nodes validate it, re-executing the transactions in the same order. The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput. In this paper we develop a theory of transaction parallelism for blockchains, which is based on static analysis of transactions and smart contracts. We illustrate how blockchain nodes can use our theory to parallelize the execution of transactions. Initial experiments on Ethereum show that our technique can improve the performance of nodes.Comment: arXiv admin note: text overlap with arXiv:1905.0436

SoK: Lending Pools in Decentralized Finance

Lending pools are decentralized applications which allow mutually untrusted users to lend and borrow crypto-assets. These applications feature complex, highly parametric incentive mechanisms to equilibrate the loan market. This complexity makes the behaviour of lending pools difficult to understand and to predict: indeed, ineffective incentives and attacks could potentially lead to emergent unwanted behaviours. Reasoning about lending pools is made even harder by the lack of executable models of their behaviour: to precisely understand how users interact with lending pools, eventually one has to inspect their implementations, where the incentive mechanisms are intertwined with low-level implementation details. Further, the variety of existing implementations makes it difficult to distill the common aspects of lending pools. We systematize the existing knowledge about lending pools, leveraging a new formal model of interactions with users, which reflects the archetypal features of mainstream implementations. This enables us to prove some general properties of lending pools, and to precisely describe vulnerabilities and attacks. We also discuss the role of lending pools in the broader context of decentralized finance and identify relevant research challenges

Speculative Transaction Scheduling for Adding Concurrency to Ethereum

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 문수묵.이더리움은 대표적인 블록체인 플랫폼으로, 암호화폐로부터 시작된 블록체인 기술에 대한 관심을 바탕으로 최근까지도 사용량을 꾸준히 유지하고 있다. 특히 유니스왑과 같은 탈중앙화 금융 서비스들이 인기를 끌며 일일 토큰 전송량은 2020년들어 두 배 가까이 증가하기도 했다. 하지만 점점 늘어나는 트랜잭션 수에도 불구하고, 현재 이더리움은 모든 트랜잭션을 순차적으로 수행하고 있기에 트랜잭션의 처리량을 높이는 데에 한계가 있다. 실제로 이로 인해 사용자가 많은 서비스가 등장할 때마다 트랜잭션 처리량은 그대로인데 트랜잭션의 수가 증가하면서 수수료가 매우 높아지는 현상이 발생하고 있다. 이러한 문제점을 해결하기 위한 방법 중 하나로 이더리움 상의 트랜잭션들을 동시 수행하기 위한 연구들이 제안되어왔다. 하지만 이러한 동시 수행은 트랜잭션간의 충돌을 유발할 수 있으며, 충돌이 자주 발생할 경우 오히려 성능이 저하될 수 있다. 따라서 본 논문에서는 충돌이 최대한 적게 발생하도록 스케줄링하는 것에 초점을 맞춘 사전 스케줄링을 통한 동시 수행 기법을 제안한다. 트랜잭션에 명시된 정보로 쉽게 예측 가능한 명시적 충돌(explicit conflict)뿐 아니라 직접 수행해보아야 확인이 가능한 내재적 충돌(implicit conflict)까지도 과거 충돌 정보를 저장하는 프로파일러를 통해 예측함으로써, 4-thread 시뮬레이션에서 순차 수행 대비 전체 트랜잭션 수행 속도를 약 3.1배, 기존의 그리디 스케줄링 방식의 동시 수행 대비 약 28.2% 향상시켰다.Ethereum is one of the most popular blockchain platform, and the amount of usage has been increasing for years based on interest in blockchain-based financial services. Despite the growing number of these complex transactions, Ethereum currently handles all transactions in a sequential execution method using a single thread, which limits throughput. To overcome this limitation, studies have been proposed to execute Ethereum transactions concurrently. However, if the transactions scheduled in a greedy manner, frequent collisions may lead to poor concurrency and high rollback costs. In this paper, we propose a speculative scheduling technique that focuses on minimizing collisions between Ethereum transactions. Not only collisions that can be easily predicted by explicit feature in the transaction, but also collisions that can only detected by implicit feature are considered based on past records through profiling. As a result, the speedup is increased by 28.2% compared to the concurrent execution of the greedy method in 4-thread simulation, and the overall transaction execution speedup was improved by 3.1 times compared to sequential execution.제 1 장 서론 1 제 2 장 배경지식 3 제 1 절 이더리움 기본 개념 3 제 2 절 이더리움 트랜잭션 충돌 5 제 3 장 이더리움 워크로드 분석 7 제 1 절 데이터 수집 7 제 2 절 분석 결과 8 제 4 장 사전 스케줄링을 통한 트랜잭션 동시수행 11 제 1 절 명시적 충돌 예측 11 제 2 절 내재적 충돌 예측 12 제 3 절 프로파일 기반 사전 스케줄링 13 제 5 장 실험 및 결과 15 제 1 절 시뮬레이션 설계 15 제 2 절 결과 15 제 6 장 관련 연구 20 제 7 장 결론 및 향후 연구 21 참고 문헌 22 Abstract 23Maste