The classical CAP theorem reveals fundamental limitations in distributed system design, namely the impossibility of simultaneously achieving strong Consistency, high Availability, and Partition tolerance. As an important distributed ledger technology, blockchain systems also face these constraints. Recent research has attempted to alleviate this problem through consensus layer or physical layer optimization techniques. However, these methods fail to achieve optimal availability due to the mismatch problem between network layer and physical layer topologies. Therefore, this paper designs a P2P broadcasting protocol that matches physical topology structures at the network layer, namely Matching-Gossip, serving as an intelligent adapter between physical topology and consensus protocols to collectively address the CAP trilemma. Experimental results demonstrate that blockchain systems based on Matching-Gossip achieve trilemma efficiency coefficients exceeding 95%, simultaneously meeting engineering requirements for strong consistency, high availability, and partition tolerance, thereby breaking through traditional CAP limitations on blockchain system design.
Artificial Intelligence Generated Content (AIGC) services have shown tremendous potential in digital content creation. However, AIGC service, trading, and product ownership have not been adequately protected due to the lack of a reliable third-party verification platform. Although blockchain-based AIGC management solutions have been proposed, they still suffer from several overlooked issues, such as performance bottlenecks in blockchain systems and repeated sales of AIGC products. Motivated by these limitations, we propose a trusted, efficient, and reasonable AIGC service and trading framework called BC-AIGC by combining identifiers, state channels, and game theory with Web3. Specifically, we first propose an AIGC management scheme based on identifiers and blockchain that enables AIGC ownership to be traced. Then, an AIGC service and trading method based on state channels is proposed, allowing most processes to be completed off-chain and resolving non-cooperation through dispute handling mechanisms, thereby improving efficiency while ensuring security. Furthermore, to find the optimal strategy, we model the interactions among AIGC service participants as a Stackelberg game and prove the existence of a unique Stackelberg equilibrium. Finally, the system prototype and numerical simulations demonstrate the superiority of the proposed BC-AIGC.
V. Hemamalini, M. Jeyaselvi, Amit Kumar Tyagi, Shabnam Kumari
Decentralized Applications (dApps) and Distributed Ledger Frameworks (DLFs) represent critical advancements in the realm of blockchain technology, promising enhanced security, transparency, and efficiency. Decentralized Applications, leveraging the inherent properties of blockchain, operate without central control, offering users greater autonomy and reducing reliance on intermediaries. These applications span various domains, from finance and supply chain management to gaming and social networks, demonstrating the versatility and transformative potential of dApps. Distributed Ledger Frameworks, encompassing both blockchain and non-blockchain-based systems, provide the underlying architecture for these applications. DLFs ensure data integrity, immutability, and consensus across distributed nodes, fostering a trustless environment where participants can transact securely. Key frameworks like Ethereum, Hyperledger, and Corda offer diverse features tailored to different use cases, ranging from public, permissionless networks to private, permissioned systems.
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a “space-for-time” technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based largenumber modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $\lt sup\gt1\lt/sup\gt|.65 \times$ and $3 \times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of 128 and 8,192, respectively.
Smartphones are widely used Internet of Things (IoT) devices in higher education, but relying on them alone does not sufficiently aid teachers in managing courses or improving student learning. For teachers, managing course progress through predefined rules and safeguarding student privacy at key stages is challenging. Likewise, students lack targeted learning assistance to enhance their capabilities. Therefore, a comprehensive scheme is essential to address these challenges effectively. In this paper, we propose a novel framework based on smart contracts and integrated with generative artificial intelligence (GAI) assistance. This framework manages programming courses and enhances student learning, with smartphones serving as access points. It leverages smart contracts to dynamically manage the entire course lifecycle, with contracts built upon access control policies to ensure that only authorized roles can access course resources via smartphone. The proposed framework is based on a consortium network. GAI model-ChatGPT-4o provides code generation and code explanation assistance in programming courses. We select the optimal prompt templates and store them on the blockchain, allowing GAI to provide more precise services using them. We implement and evaluate the proposed framework using the Hyperledger Fabric blockchain, demonstrating its effectiveness and scalability in real-world scenarios. Additionally, we evaluate the code-related content generated by the prompt templates using three criteria: pass rate, time spent, and number of votes. This evaluation confirms that the templates selected and recorded on the blockchain are optimal. The framework can be easily adapted to other scenarios such as course management, record management, and research collaboration.
Xin Chen, Manoj Prabhakar Paidiparthy, Dilma Da Silva, Liting Hu
While originally designed for the cloud, the benefits of the serverless paradigm are vital in Edge/Fog computing environments. In this paper, we propose Ekko, a novel decentralized edge serverless scheduling system, which enables a large number of serverless applications to run simultaneously at the edge through the Functionas-a-Service (FaaS) model. The key insight is to re-architect the common centralized or hierarchical scheduling systems into a fully decentralized one by using the distributed hash table (DHT) based peer-to-peer (P2P) model, in which many distributed schedulers operate autonomously without any centralized state. In sharp contrast to existing studies, any edge node in our system can act as a scheduler, a function worker, a query forwarder, or a storage node, and flexibly switch between these roles, thereby significantly improving scalability and adaptivity. Ekko introduces three design innovations: a boundary-aware P2P organization, distributed shadow schedulers with a keychain scheduling algorithm, and a distributed locality-aware bucket image store. Our evaluation on 500 Amazon EC2 nodes shows that, compared to the state-of-the-art, Ekko reduces the 90-th percentile tail queue wait time by up to 96.6 %, the scheduling time by up to 38.5 %, and the total deployment time by up to 89.5 %, while efficiently scaling to millions of function invocation requests on thousands of edge nodes.
Soosan Naderi Mighan, Jelena Mišić, Vojislav B. Mišić
Ethereum 2.0 uses a Proof-of-Stake-based consensus which aims to minimize the impact of malicious validators by decentralizing the voting protocol. In this paper we investigate the lifecycle of a validator in a consensus protocol similar to Ethereum 2.0 but with simplifications introduced for tractability. In particular, the protocol operates with near-single slot finality and includes the impact of behaviors such as truthful and false voting, abstention from voting, voluntary exit from the validator committee, and return to the committee upon depositing the required stake. Using probabilistic techniques and a Markov chain model, we examine the impact of all those factors on consensus probability. Our results indicate that the probability of truthful voting has a predominant effect on consensus, although the interplay between probabilities of voluntary exit and waiting before returning to the committee also plays an important role. We also investigate the process of fork resolution and model the behavior of the blockchain in the presence of multiple tips, and we show that probability of truthful voting is equally important in this case as higher values accelerate fork resolution.
Abstract Pixel-Web3 Wallet is a hierarchical deterministic (HD) wallet designed for secure and decentralized asset management across multiple blockchain networks, including Ethereum and Solana. Unlike traditional wallets that depend on browser extensions or centralized servers, Pixel offers a web-based solution with user-controlled security through locally stored seed phrases. This paper explores the wallet’s architecture, security framework, and innovative features, such as real-time balance updates and flexible recovery options. Additionally, the research evaluates the scalability of Pixel and its potential expansion to support more blockchain networks. By eliminating reliance on third-party services, Pixel enhances accessibility while maintaining strong security, making it a promising solution for blockchain enthusiasts, traders, and developers. Keywords: Blockchain, HD Wallet, Cryptocurrency,Web3,Ethereum,Solana, Security
The Ethereum blockchain, plagued by network congestion and exorbitant transaction fees, faces significant scalability challenges. While Layer 2 solutions offer a promising avenue to address these concerns, their potential remains largely unexplored on blockchain applications. The research proposes a novel Layer 2 architecture specifically designed for the academic certificate system on the Ethereum network. The method commences with a comprehensive survey of existing literature, followed by an analysis of solutions within the business domain. Subsequently, the most suitable and comprehensive solutions are identified for integration into the proposed academic certificate system architecture. In the selection process, the research analyzes 20 studies to determine the frequency of solutions employed in each investigation. The results indicate the InterPlanetary File System (IPFS) exhibiting the highest frequency, while Oracle, Decentralized Identifiers (DIDs), and Application Programming Interfaces (APIs) have comparable frequencies. Furthermore, an analysis of rankings from 10 websites evaluating Layer 2 Ethereum solutions and their performance across various aspects reveals Arbitrum as the top-ranked solution, followed by Polygon and Optimism, respectively. The research demonstrates the implementation of this system architecture within the proposed system's process. The culmination of this effort is a valuable blueprint for developers seeking to build and deploy similar systems efficiently. Notably, the inherent adaptability of the architecture extends beyond the educational domain, paving the way for its application across diverse contexts. The system architecture presented constitutes an initial exploration into developing Decentralized Applications (DApps) on the Ethereum Layer 2 network because prior research has not specifically focused on its application.
This article examines the transformative advancements in real-time payment processing optimization and their profound impact on digital commerce. It explores how millisecond-level processing improvements significantly enhance conversion rates in high-volume e-commerce environments. Key innovations discussed include intelligent predictive routing algorithms that leverage historical data to make real-time transaction routing decisions, the implementation of standardized interfaces like Payment Request API and ISO 20022, and privacy-preserving optimization techniques compliant with evolving regulatory frameworks. It further analyzes the substantial impact of these optimizations on e-commerce conversion rates, demonstrating how they reduce cart abandonment and improve customer trust and retention. Looking ahead, the article considers emerging technologies such as Central Bank Digital Currencies and distributed ledger systems that promise to further revolutionize payment processing with faster settlement times, lower costs, and expanded financial inclusion. The article findings suggest that payment processing optimization has evolved beyond technical consideration to become a strategic business imperative with measurable revenue impact and broader economic implications.
Atefeh Zareh Chahoki, Maurice Herlihy, Marco Roveri
Conthereum is a concurrent Ethereum solution for intra-block parallel transaction execution, enabling validators to utilize multi-core infrastructure and transform the sequential execution model of Ethereum into a parallel one. This shift significantly increases throughput and transactions per second (TPS), while ensuring conflict-free execution in both proposer and attestor modes and preserving execution order consistency in the attestor. At the heart of Conthereum is a novel, lightweight, high-performance scheduler inspired by the Flexible Job Shop Scheduling Problem (FJSS). We propose a custom greedy heuristic algorithm, along with its efficient implementation, that solves this formulation effectively and decisively outperforms existing scheduling methods in finding suboptimal solutions that satisfy the constraints, achieve minimal makespan, and maximize speedup in parallel execution. Additionally, Conthereum includes an offline phase that equips its real-time scheduler with a conflict analysis repository obtained through static analysis of smart contracts, identifying potentially conflicting functions using a pessimistic approach. Building on this novel scheduler and extensive conflict data, Conthereum outperforms existing concurrent intra-block solutions. Empirical evaluations show near-linear throughput gains with increasing computational power on standard 8-core machines. Although scalability deviates from linear with higher core counts and increased transaction conflicts, Conthereum still significantly improves upon the current sequential execution model and outperforms existing concurrent solutions under a wide range of conditions.
Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a ``space-for-time'' technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based large-number modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $1.65\times$ and $3\times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of $128$ and $8,192$, respectively.
Popular technologies such as blockchain and zero-knowledge proof, which have already entered the enterprise space, heavily use cryptography as the core of their protocol stack. One of the most used systems in this regard is Elliptic Curve Cryptography, precisely the point multiplication operation, which provides the security assumption for all applications that use this system. As this operation is computationally intensive, one solution is to offload it to specialized accelerators to provide better throughput and increased efficiency. In this paper, we explore the use of Field Programmable Gate Arrays (FPGAs) and the High-Level Synthesis framework of AMD Vitis in designing an elliptic curve point arithmetic unit (point adder) for the secp256k1 curve. We show how task-level parallel programming and data streaming are used in designing a RISC processor-like architecture to provide pipeline parallelism and increase the throughput of the point adder unit. We also show how to efficiently use the proposed processor architecture by designing a point multiplication scheduler capable of scheduling multiple batches of elliptic curve points to utilize the point adder unit efficiently. Finally, we evaluate our design on an AMD-Xilinx Alveo-family FPGA and show that our point arithmetic processor has better throughput and frequency than related work.
In its present form, traditional Web2 domain infrastructures have become vulnerable to increasing amounts of censorship, security breaches, and centralized authority exclusively handled by third party Certificate Authorities (CAs). These weaknesses and the single points of failure inherent, under spotlight give out the necessity of migrating to a safer decentralized Web3 model. Mygrate was developed using solidity, Hedera and hardhat. This paper elucidates Mygrate development, features, and applications. It offers a functionality to bridge Web2 domains with Web3 architecture by utilizing SSL certificates in combination with smart contracts to map domains to cryptographically signed InterPlanetary File System (IPFS) addresses, verified over decentralized blockchains such as Hedera. By solving a major part of the oracle problem, Mygrate emerged as an innovative solution, leveraging the power of Hedera Blockchain. This paper delves into the comprehensive development journey of Mygrate. It allows us to bring web2 hosted domain name source of trust and offset authenticity to web3 for easy identifiability. Moreover, there is increased security, decreased oracle fees, and money goes directly to the data providers who provide data as a public good. Through the integration of libraries like Ethereum-Waffle, ethers, chai,node-forge, Mygrate offers Oracle-free SSL verification, EVM assembly optimization, Frontrunning mitigation, gas optimization and security.
Multi-scalar multiplication (MSM) is the primary building block in many pairing-based zero-knowledge proof (ZKP) systems. MSM at large scales has become the main bottleneck in ZKP implementations. Inspired by existing SIMD-accelerated work, we are focused on accelerating MSM computing efficiency using SIMD instructions in a single CPU environment. First, we propose a SIMD-accelerated MSM computing architecture with no write conflicts and constant memory overheads. This architecture utilizes multithreading to achieve task-level and loop-level parallelism and employs a three-tier buffer mechanism to maximize the utilization of the SIMD engine. Instanced with AVX512-IFMA instructions, we implement six SIMD elliptic curve arithmetic engines for different point addition in three coordinate systems and two groups. Moreover, we integrate our AVX-MSM implementation into the libsnark library, naming it AVX-ZK. In more detail, point deduplication and “Three-Stage” memory optimization are proposed to address problems existing in practical applications. Based on the RELIC library, our performance results on the BLS12-381 curve show that our AVX-MSM achieves up to 27.86x speedup over the most popular Pippenger algorithm. Compared with libsnark, our AVX-ZK implementation achieves over 11.53x (up to 20.26x) speedup under standard benchmarks.
With the widespread adoption of Zero-Knowledge Proof systems, particularly ZK-SNARK, the efficiency of proof generation, encompassing both the witness generation and proof computation phases, has become a significant concern. While substantial efforts have successfully accelerated proof computation, progress in optimizing witness generation remains limited, which inevitably hampers overall efficiency. In this paper, we propose Yoimiya, a scalable framework with pipeline, to optimize the efficiency in ZK-SNARK systems. First, Yoimiya introduces an automatic circuit partitioning algorithm that divides large circuits of ZK-SNARK into smaller subcircuits, the minimal computing units with smaller memory requirement, allowing parallel processing on multiple units. Second, Yoimiya decouples witness generation from proof computation, and achieves simultaneous executions over units from multiple circuits. Moreover, Yoimiya enables each phase scalable separately by configuring the resource distribution to make the time costs of the two phases aligned, maximizing the resource utilization. Experimental results confirmed that our framework effectively improves the resource utilization and proof generation speed.
In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageable groups of nodes that collaborate to achieve consensus without the overhead associated with traditional large-scale consensus protocols. CohortSync operates by first classifying nodes based on their network latency, data relevance, and operational load. This classification enables the protocol to intelligently form micro-cohorts that are geographically and contextually optimized, reducing the latency typically experienced in global consensus operations. Each micro-cohort is responsible for a subset of the reconciliation tasks, allowing for parallel processing and significantly reducing the time to reach consensus. The protocol incorporates a hybrid approach to consensus that combines elements of both deterministic and probabilistic consensus mechanisms. This hybrid model allows CohortSync to maintain high availability and consistency, even in the face of node failures or network partitions. By adapting the consensus mechanism based on real-time network performance and node responsiveness, CohortSync can dynamically adjust its operations to maintain system performance and data accuracy. Another key feature of CohortSync is its reconciliation process, which uses a version-controlled state reconciliation algorithm. This algorithm ensures that all nodes within a micro-cohort maintain a synchronized state, with conflicts resolved through a majority rule among the cohort members. This approach not only minimizes the risk of data divergence but also optimizes the reconciliation process to be both time-efficient and resource-conservative. CohortSync also integrates a continuous learning component that analyzes past consensus rounds to optimize future cohort formation and consensus strategies. This machine learning-driven adaptability makes the protocol robust against evolving network conditions and varying operational loads across nodes. The protocol has been tested in various simulated environments that mimic real-world distributed systems across different industries, including finance, healthcare, and e-commerce. The results demonstrate that CohortSync significantly outperforms existing consensus protocols in terms of scalability, fault tolerance, and operational efficiency. In conclusion, CohortSync presents a transformative approach to consensus and reconciliation in distributed systems. By decentralizing the consensus process into manageable micro-cohorts and integrating adaptive learning mechanisms, CohortSync offers a scalable, efficient, and robust solution that can meet the demands of contemporary distributed computing environments.
The reproducibility of scientific simulations is one of the key challenges of scientific research. Current best practices involve version-controlled code, tracking dependencies, specifying hardware configurations, and sometimes using Docker containers to enable one-click simulation setups. However, these approaches still fall short of achieving true reproducibility. For example, Docker depends on the underlying host kernel, and high-performance computing (HPC) codes often link with specific kernel modules and headers. Over time, changes in host kernel versions can render Dockerized simulations unusable. Furthermore, non-deterministic simulations, such as Monte Carlo methods, may not yield identical results even when rerun on the same hardware with the same code.This talk explores the potential of blockchain technology to address these challenges. By running simulations natively on-chain (via smart contracts) and emitting logs of each state transition, we can achieve reproducibility while also verifying the simulation's authenticity (associating the original author of the simulation and the reporting author).Other potential ideas include using zero-knowledge proofs to hash the call stack and the stack memory into a Merkle tree or also to think about the tokenisation of compute.We will delve into the technical feasibility and potential benefits of this approach, including its implications for trust, transparency, and the future of scientific research.
In today's increasingly interconnected world, the demand for efficient, resilient and fault tolerance distributed data communication systems is paramount. This research explores a novel alternative approach to address the challenges of traditional distributed systems. The study investigates the integration of cutting-edge technologies, such as decentralized networks, blockchain, and Software-Defined Wide Area Network (SD-WAN), to revolutionize data communication. This alternative approach aims to enhance system efficiency, scalability and reliability while reducing vulnerabilities associated with centralized systems. By leveraging decentralization principles, networking automation approaches and distributed ledger technology, it prioritizes data efficiency, integrity, security, presenting a transformative vision for network infrastructure. This research contributes to the ongoing discussions about distributed data transmission systems. It opens up a new perspective and paves the way for future achievements in this field.