The primary trend in the development of modern information technologies is the migration of computations to the cloud, making distributed computing the dominant strategy for information processing. In particular, this poses the challenge of reliable distributed data storage. A well-known approach to solving the problem of distributed data storage is blockchain or, more generally, distributed ledger technology. A key challenge of this technology is creating an effective mechanism for the global numbering of registry records. The complexity of solving this problem results from the fundamental limitations of distributed computing — the inability to accurately synchronize distributed computing processes and the limitations resulting from the CAP theorem for distributed data stores. The authors attempt to circumvent the mentioned limitations based on the hypothesis that such limitations can be overcome by considering both the network topology and narrowing the class of distributed systems to distributed registers. The work is based on methods of modeling distributed computing, particularly the model of space-time diagrams proposed by L. Lamport. This model allows us to introduce such a tool as logical clocks, including Lamport's logical clock algorithm. Unfortunately, Lamport's logical clock algorithm allows assigning a common timestamp to different events if they are concurrent. The paper proposes an algorithm that is a composition of Lamport's clock algorithm and the wave algorithm, which is not only a logical clock but also assigns different timestamps to different events. Thus, this algorithm provides a mechanism for the global numbering of entries of distributed ledger replicas. A problematic issue remains gaps in the series of ledger entry numbers. Thus, the paper proposes an effective mechanism for the global numbering of records of a distributed register and identifies a shortcoming of this mechanism. Further study is to identify specific conditions in terms of network topology that would ensure the absence of the mentioned shortcoming.
Smart contracts are programs stored on a blockchain ledger, thus being immutable after deployment, which makes assessment of their correctness before deployment vital. Extended finite state machines (EFSM) offer a structured framework for modeling complex systems, thus providing a systematic approach to scrutinize smart contract functionalities. This paper describes a methodology to automatically convert from the abstract syntax tree of a smart contract to an EFSM model. A smart contract implementing a casino is the specific use case, and verification of the EFSM model reveals it to be blocking. This blocking represents that a malicious player can lock the funds of the casino so that they can never be retrieved.
Abstract This paper provides an overview of the distributed ledger technology (DLT) options available to central banks for issuing central bank digital currency (CBDC). We discuss the main requirements that a DLT solution must fulfill and analyze the various structures for implementation offered by DLT — public, permissioned and private — and the implications that each has for the central bank and the existing financial system. While a CBDC built on an open, permissionless system would provide the full functionality offered by DLT, it is also far more disruptive to the existing financial system and consequently requires more new infrastructure on the part of the central bank.
This bachelor thesis examines blockchain technology and consensus algorithms to provide a comprehensive overview of these key elements of modern decentralized systems. The thesis describes in detail the blockchain architecture, its layers and key features. It also focuses on types of blockchain networks, security risks and consensus mechanisms. The section on consensus algorithms analyses their variants such as Proof of Work, Proof of Stake, Proof of Authority or algorithms based on oriented acyclic graphs. A comparative analysis of the algorithms evaluates their performance based on the chosen criteria. The paper also discusses the use of blockchain in practical applications such as smart contracts, decentralized applications, and technologies for enterprise environments. The conclusions include recommendations for selecting an appropriate consensus algorithm with respect to the specific requirements of different application scenarios.
The rapid rise of blockchains and “Web3” has sparked significant interest in designing and implementing distributed systems for untrusted environments. However, due to their rigid one-size-fits-all design, current systems fail to adapt to dynamic workloads, new hardware setup, and different user intentions. To address this problem, we examine the systems stack in a top-down layered manner, from transaction management, Byzantine fault-tolerant (BFT) consensus to their underlying infrastructure. We propose i) AdaChain, the first learned system that adaptively manages blockchain transactions, ii) BFTBrain, a multi-protocol BFT consensus engine that dynamically adjusts itself using reinforcement learning, and iii) FlexChain, a flexible disaggregated infrastructure tailored for permissioned blockchains. AdaChain addresses the challenge in transaction execution under dynamic workloads. As a system driven by reinforcement learning, AdaChain adaptively selects the optimal transactional architecture for dynamic workloads, maximizing throughput. AdaChain securely switches architectures at runtime when a better option is identified. Experiments show AdaChain quickly converges to optimal setups, significantly outperforming fixed architectures in committed transactions with minimal overhead. BFTBrain is a practical reinforcement learning based BFT consensus that provides significant operational benefits: a plug-and-play system suitable for a broad set of hardware and network configurations, and adjusts effectively in real-time to changing fault scenarios and workloads. BFTBrain’s key innovations include systematic performance modeling to guide protocol selection and decentralized learning resilient to adversarial data pollution. As a result, in addition to providing significant operational benefits, BFTBrain improves throughput over fixed protocols by 18% to 119% under dynamic conditions and outperforms state-of-the-art learning based approaches by 44% to 154%. FlexChain is a novel infrastructure layer for permissioned blockchains that physically disaggregates CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation enables independent scaling of hardware resources, improving utilization and preventing fragmentation. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.
Hamra Afzaal, Nazir Ahmad Zafar, Aqsa Tehseen, Shaheen Kousar · 5 authors
In recent years, Beacon Chain known as the core of Ethereum 2.0, has gained considerable attention since its launch. Many validators have staked billions of Ether in the Proof of Stake (PoS) network. It is a mission critical system and its security and stability rely on the justification and finalization of checkpoints. These are essential elements of the Casper FFG consensus algorithm utilized by the Beacon Chain. This process is critical for establishing a trustworthy foundation and finalizing proposed blocks by confirming agreed upon checkpoints. Hence, ensuring the correctness of checkpoints in the Beacon Chain has significant importance because any bug in it can cause serious implications. To address this challenge, we employ formal methods, a popular mathematical approach used for verifying the correctness of such critical systems. In this work, we have done formal verification of the processes of Beacon Chain state initialization, justification and finalization of checkpoints using the Process Analysis Toolkit (PAT) model checker. The adoption of model checking through the PAT model checker presents a novel contribution of our work, as this approach is not previously utilized in the formal verification of Beacon Chain. The presented work is specified through the Communicating Sequential Programs, formal specification language, and the properties are described through Linear Temporal Logic. The PAT model checker takes the specified formal model and properties as input to assess whether the properties are satisfied. The properties are analyzed with respect to the verification time, visited states, total transitions, and memory used. Through this research, we aim to increase confidence in the correctness and reliability of the Beacon Chain.
As blockchains shift from energy-hungry Proof-of-Work to capital-intensive Proof-of-Stake, they trade electricity costs for a new vulnerability: the risk of a capital run that can destabilize consensus and security. We model investors who choose between staking their coin to earn rewards or exiting to cash out, potentially triggering mass withdrawals. These "staking runs" are more likely when protocols are weak, when failure would hit coin prices hard, or when staking rewards are low. Leverage worsens things: margin calls accelerate exits and amplify run dynamics. Longer lock-up periods slow the run but may not prevent it. Previous research shows that low rewards are good for protocol security. We show they also raise the risk of a run. A run on a major Proof-of-Stake chain---like Ethereum---could destabilize the entire crypto ecosystem, threatening DeFi platforms that depend on it.
We present the Layered Merkle Patricia Trie (LMPT), a performant storage data structure for processing transactions in high-throughput systems when compared to traditional Merkle Patricia Tries used in Ethereum clients. LMPTs keep smaller intermediary tries in memory to alleviate read and write amplification from high-latency disk storage. As an additional feat, they also allow for the I/O and transaction verifier threads to be scheduled in parallel and independently. LMPTs can ultimately reduce significant I/O traffic that happens on the critical path of transaction processing. Empirical results show that LMPTs can process up to$\times6$more transactions per second on real-life ERC20 smart contract workloads when compared to existing Ethereum clients.
Ethereum is undergoing significant changes to its architecture as it evolves. These changes include its switch to PoS consensus and the introduction of significant infrastructural changes that do not require a change to the core protocol, but that fundamentally affect the way users interact with the network. These changes represent an evolution toward a more modular architecture, in which there exists new exogenous vectors for centralization. This paper builds on previous studies of decentralization of Ethereum to reflect these recent significant changes, and Ethereum's new modular paradigm.
Decentralized finance revolutionizes traditional financial systems by leveraging blockchain technology to reduce trust. However, some vulnerabilities persist, notably front-running by malicious actors who exploit transaction information to gain financial advantage. Consensus with a fair order aims at preventing such attacks, and in particular, the differential order fairness property addresses this problem and connects fair ordering to the validity of consensus. The notion is implemented by the Quick Order-Fair Atomic Broadcast (QOF) protocol (Cachin et al., FC ‘22). This paper revisits the QOF protocol and describes a modular implementation that uses a generic consensus component. Moreover, an empirical evaluation is performed to compare the performance of QOF to a consensus protocol without fairness. Measurements show that the increased complexity comes at a cost, throughput decreases by at most 5%, and latency increases by roughly 50 ms, using an emulated ideal network. This paper contributes to a comprehensive understanding of practical aspects regarding differential order fairness with the QOF protocol and also connects this with similar fairness-imposing protocols like Themis and Pompē.
Ethereum has emerged as a leading platform for decentralized applications (dApps) due to its robust smart contract capabilities. One of the critical issues in the Ethereum ecosystem is Maximal Extractable Value (MEV), a concept that has gained significant attention in the blockchain community. However, MEV has remained a major challenge with significant implications for the platform's operation and integrity. This paper introduces the FairFlow protocol, a novel framework designed to mitigate the effects of MEV within Ethereum's existing infrastructure. The protocol aims to provide a more equitable environment, preventing exploitation by miners or validators, and protecting user data. The combined approach of auction-based block space allocation and randomized transaction ordering significantly reduces the potential for MEV exploitation.
In this paper, we propose MACS - a new coin selection algorithm for multi-asset UTXO-based blockchains. MACS addresses the primary coin selection requirements regarding transaction fee, transaction size, UTXO pool size, transaction age, and privacy. We compare the performance of MACS with other coin selection algorithms. The performance evaluation shows that MACS achieves the mentioned requirements while outperforming other coin selection algorithms in terms of having a wider distribution of their number of UTXO inputs. Moreover, UTXOs will not remain in the UTXO pool forever when the MACS algorithm is used since MACS keeps the fraction of old UTXOs oscillating between $0 \%$ and $20 \%$.
The core of a blockchain network is its consensus algorithm. Starting with the Proof-of-Work, there have been various versions of consensus algorithms, such as Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Practical Byzantine Fault Tolerance (PBFT). Each of these algorithms focuses on different aspects to ensure efficient and reliable processing of transactions. Blockchain operates in a decentralized manner where there is no central authority and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes. However, there is no one-size-fits-all mechanism to identify all malicious nodes. Hence, the dynamic adaptability of the blockchain network is important to maintain security and reliability at all times. This paper introduces MRL-PoS, a Proof-of-Stake consensus algorithm based on multi-agent reinforcement learning. MRL-PoS employs reinforcement learning for dynamically adjusting to the behavior of all users. It incorporates a system of rewards and penalties to eliminate malicious nodes and incentivize honest ones. Additionally, MRL-PoS has the capability to learn and respond to new malicious tactics by continually training its agents.