This paper explores the emerging paradigm of Blockchain-as-a-Service (BaaS) and its implementation of distributed ledger technology in cloud environments. We examine the key characteristics, benefits, and challenges of BaaS platforms, analyze different architectural approaches and deployment models, and evaluate performance considerations for blockchain networks in the cloud. Through a comprehensive literature review and analysis of existing BaaS offerings, we provide insights into the current state of the technology and identify promising research directions. Our findings indicate that BaaS has significant potential to accelerate enterprise blockchain adoption by reducing complexity and costs, but also faces hurdles related to security, scalability, and standardization that need to be addressed as the field matures.
This article presents a comprehensive framework for applying blockchain technology to secure data integration challenges in multi-cloud and hybrid-cloud environments. This article examines how distributed ledger technology creates a trust layer that addresses key vulnerabilities in traditional integration approaches while maintaining performance characteristics suitable for enterprise deployments. This article's architecture leverages permissioned blockchain networks, smart contracts, and cryptographic verification mechanisms to ensure data integrity, enforce governance policies, and provide immutable audit trails across heterogeneous cloud platforms. Our performance evaluation demonstrates viable throughput and latency characteristics compared to traditional integration methods, while offering enhanced security properties. Through case studies in financial services, healthcare, supply chain, and critical infrastructure protection, we illustrate practical implementations and quantifiable benefits. Despite challenges in scalability, energy consumption, legacy system integration, regulatory compliance, and organizational adoption, the architecture shows promising results for high-value data workflows. The research contributes to the emerging intersection of blockchain and multi-cloud computing by providing both theoretical foundations and practical implementation guidance for organizations seeking to enhance security posture across distributed cloud environments.
Parwat Singh Anjana, Srivatsan Ravi, Herlihy, Maurice
This paper presents a comprehensive analysis of historical data across two popular blockchain networks: Ethereum and Solana. Our study focuses on two key aspects: transaction conflicts and the maximum theoretical parallelism within historical blocks. We aim to quantify the degree of transaction parallelism and assess how effectively it can be exploited by systematically examining block-level characteristics, both within individual blocks and across different historical periods. In particular, this study is the first of its kind to leverage historical transactional workloads to evaluate conflict patterns. By offering a structured approach to analyzing these conflicts, our research provides valuable insights and an empirical basis for developing more efficient parallel execution techniques for smart contracts in the Ethereum and Solana. Our empirical analysis reveals that historical Ethereum blocks frequently achieve high independence, with over 50\% independent transactions in more than 50\% of blocks, while, on average, Solana blocks contain longer conflict chains $\sim$58\%, compared to $\sim$18\% in Ethereum, reflecting fundamentally different parallel execution dynamics.
G. Sharmila, K. Neha, M. Kaviya, M. Juhe Sherin · 5 authors
Blockchain technology is a cutting-edge advancement in information technology. Bitcoin, as one of its initial uses, has attracted considerable attention as a cryptocurrency. Alongside Ethereum, which emphasizes blockchain-driven smart contracts, these technologies lie at the heart of modern cryptocurrency innovation. Off-chain transactions offer a scalable solution for blockchain networks, reducing congestion, lowering transaction fees, and improving processing efficiency without compromising decentralization. However, existing off-chain solutions often face security and flexibility challenges, particularly in environments with high latency and unstable connectivity. The proposed system leverages the Hardhat blockchain framework with Ethereum to enable secure peer-to-peer transactions from user wallets, ensuring seamless fund transfers even in offline conditions. Additionally, it integrates blockchain-based email functionality, allowing encrypted messages to be sent securely over a decentralized network, thereby enhancing data privacy and security. To further strengthen data integrity, the system incorporates the Inter Planetary File System (IPFS) for decentralized file storage, reducing reliance on centralized servers and minimizing data loss risks. By combining off-chain transactions, blockchain-based email, and IPFS storage, the system enhances efficiency, security, and reliability, offering a robust decentralized solution for financial transactions and secure communication. The data is distributed across all cryptocurrency users within the network. This ensures that when a user initiates a transaction, data mining processes are conducted.
This research examines the risk profiles of XRPUSD and ADAUSD cryptocurrencies through Value at Risk (VaR) analysis with Monte Carlo simulation, providing quantitative risk assessments for both individual assets and a diversified portfolio. Analyzing historical price data from January 2016 to November 2024, the study identifies distinctive risk characteristics between these cryptocurrencies: ADAUSD exhibited marginally higher historical returns (1.44% monthly) compared to XRPUSD (1.42%), but with notably higher volatility (standard deviation of 5.41% versus 4.65%). The Monte Carlo simulation with 1,000 iterations generated VaR estimates at multiple confidence levels, revealing that XRPUSD consistently demonstrated lower downside risk than ADAUSD across all confidence thresholds. At the 99% confidence level, ADAUSD showed a Mean VaR of -10.97%, indicating potential monthly losses exceeding $10.97 million on a hypothetical $100 million investment, while XRPUSD's lower Mean VaR of -9.52% translated to potential losses of approximately $9.52 million. The most striking finding emerged from the portfolio analysis, which revealed dramatic risk reduction through diversification—the equally-weighted portfolio achieved a Mean VaR of merely -2.22% at the 99% confidence level, representing an approximately 80% reduction in potential losses compared to ADAUSD alone. These results demonstrate that cryptocurrency diversification can substantially mitigate extreme downside risk while maintaining exposure to the digital asset class. The significant risk reduction achieved through a simple two-asset allocation validates the application of modern portfolio theory principles to cryptocurrency investments despite their unique characteristics and underscores the critical importance of diversified approaches rather than concentrated positions for risk-conscious cryptocurrency investors. This research contributes to both theoretical understanding of cryptocurrency risk dynamics and practical portfolio construction approaches, providing quantitative evidence for the value of diversification strategies in navigating the substantial volatility inherent in digital asset markets.
Your public cloud environment can't run at low latency in today's digital-driven landscape, so it has become a strategic necessity. This comprehensive article discusses actionable strategies for latency optimization in public cloud systems traversing across network, compute, and storage layers. Though slower than form 2, form 3 cannot be recommended for imports because it presents challenges like How to easily make duplex payments with very high values. Reading form 4, you will learn how a decentralized finance system comprises different core components. This delves deep into the root causes of latency, like Geographic distance, resource contention, and inefficient configurations, and proffers sufficient guidance on combatting these through architectural best practices, edge computing, private connectivity, and intelligent resource selection. It also explores how real-time monitoring, predictive benchmarking, and automation tools allow organizations to detect and deal with latency problems before those affect the user experience. New technologies like AI/ML and 5G are targeted as these technologies will completely transform cloud performance optimization through the ability to make proactive decisions and super-fast connectivity. Besides, real-world case studies show successful implementations and cautionary failures and give useful lessons for IT leaders and cloud architects. This guide offers readers the tools and knowledge to build fast, scalable, and reliable cloud applications in both a single—or, indeed, a multi—or, not least, hybrid environment. The aim is easy: their clouds should not only work but work in an optimized way for all those milliseconds of performance and response time.
As more organizations move to use the multi-tenant cloud infrastructure, the perimeter-based security model is insufficient for the concept of zero-trust security states. Thatently, curing this complex environment, It has “never trust, always verify”. Completely contradicting the conventional models, Zero Trust continually promotes authentication and validation of every access request (inside or outside the network perimeter). As they try to understand how to protect the isolation of tenants, stop alteration movements, and support identity cross services, the paper investigates the challenges and parts of zero trust taking effect in the multi-tenant cloud. Everything must always be authenticated, no matter the connection status, to ensure the user (only the user) has permission to do all the things they need. Further, it shows that Artificial Intelligence (AI) and Machine Learning (ML) technologies can highly enhance the detection of threats and adaptive access control. It shall see an exhibited case study of a SaaS provider going from providing limited risk mitigation against these risks, such as credential stuffing, API abuse, and insider data leakage, to Zero Trust security. This paper discusses decentralized identity (DID), post-quantum cryptography, blockchain as immutable audit trails, and AI-led autonomous zero trust systems as some of the future emerging trends. As the world reaches the multi-tenant cloud architecture, they are ready to enhance cloud security further.
Aashish Kumar Jha, Mohammed Nihar N R, J Sankalpa, Chetana Prakash
ABSTRACT: As statistics is the backbone of the digital financial system dependence on centralized cloud storage structures makes users prone to troubles concerning statistics breaches operational price and lack of control this paper examines the deployment of a decentralized cloud storage DCS framework with the use of interplanetary file system IPFS and Ethereum blockchain clever contracts to triumph over those drawbacks the gadget proposed here improves protection and information availability by incorporating aes-256 encryption sharding of records and decentralized metadata control by the introduction of a working prototype based on react.js, Ethereum wallet, ether.js and solidity this mission illustrates the viability of a decentralized statistics garage whilst resolving troubles with latency user adoption and value effectiveness experimental consequences affirm enhancements in safety and availability establishing a strong platform for additional research on decentralized storage architectures
Abstract— The swift uptake of cloud computing services has brought with it new complexities in tracking and billing for resource usage, frequently resulting in disagreements between customers and service providers as a result of unclear pricing models. This study investigates the use of Distributed Ledger Technology (DLT) to improve transparency, trust, and accuracy in cloud resource billing. By taking advantage of the distributed and immutable aspect of distributed ledgers, bill records can be recorded, stored, and audited in real-time by anyone involved in an immutable manner. This removes dependence on centralized bill authorities and reduces tampering and manipulation of the data. Our proposed blockchain framework tracks resource consumption metrics, such as compute time, storage, and bandwidth used, directly on a distributed ledger. Smart contracts eliminate manual billing computations and payments, providing consistency and fairness. With this system, users obtain verifiable information on their billing history, while providers enjoy fewer operational disagreements and higher customer trust. Our paper presents the system architecture, principal technical challenges, possible performance overheads, and feasible solutions for deployment at scale. Finally, this research illustrates how the convergence of distributed ledger systems with cloud billing systems presents a revolutionary entry point to the development of an increasingly open and responsive cloud economy. Keywords— Ledger, Blockchain, Billing , software.
Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting in the prevalence of failed transactions and network congestion. Prior work on Solana has mainly focused on the evaluation of the performance of the Solana blockchain, particularly scalability and transaction throughput, as well as on the improvement of smart contract security, leaving a gap in understanding the characteristics and implications of failed transactions on Solana. To address this gap, we conducted a large-scale empirical study of failed transactions on Solana, using a curated dataset of over 1.5 billion failed transactions across more than 72 million blocks. Specifically, we first characterized the failed transactions in terms of their initiators, failure-triggering programs, and temporal patterns, and compared their block positions and transaction costs with those of successful transactions. We then categorized the failed transactions by the error messages in their error logs, and investigated how specific programs and transaction initiators are associated with these errors. We find that transaction failure rates on Solana exhibit recurring daily patterns, and demonstrate a strong positive correlation with the volume of failed transactions, with bots on Solana experiencing a high transaction failure rate of 58.43%. We identify ten distinct error types in the error logs of failed transactions, with price or profit not met and invalid status errors accounting for 67.18% of all failed transactions. AMMs primarily experience invalid status errors among failed transactions, while DEX aggregators are more commonly affected by price or profit not met errors. Among transaction initiators, bots encounter a broader range of errors due to their high-frequency trading and complex interactions with smart contracts. In contrast, human users experience a more limited range of errors. Based on our findings, we provide recommendations to mitigate transaction failures on Solana and outline future research directions.
Rollup stands out as one of the most effective techniques for blockchain Layer-2 scaling. By processing transactions off-chain, it significantly enhances the throughput. However, the most rollup implementations currently rely on centralized sequencers, exposing the system and users to censorship attacks and risking network paralysis. In contrast, fully decentralized sequencers encounter latency issues and reduced throughput during the consensus phase. We propose a multislot weighted leader election algorithm based on shared sequencers, apply the proposer–builder separation (PBS) model, and use the fuzzy cognitive map (FCM) to analyze and optimize the important influence parameters. With its low trust dependence and high functionality, the probability of selecting malicious nodes is reduced. The sequencing and consensus are separated, so that the transaction can quickly reach soft confirmation. We implement this algorithm in a shared sequencer prototype. The experimental results show that the proposed algorithm parameter settings are in line with the expectations, and the probability of electing malicious nodes is significantly reduced. The transactions per second (TPS) of the network can cope with the throughput requirements of the Layer-2.
Damodar Bihani, Bright Chibunna Ubamadu, Andrew Ifesinachi Daraojimba
The evolution of Web3 has ushered in a paradigm shift from centralized control to decentralized, user-centric ecosystems powered by blockchain technology. At the core of these ecosystems lies tokenomics—the strategic design and management of token economies—which plays a crucial role in ensuring long-term sustainability, scalability, and user engagement. This paper presents a strategic framework for understanding and optimizing tokenomics within Web3 ecosystems, integrating insights from game theory, behavioral economics, and blockchain governance. It identifies key components of effective tokenomic models, including token utility, supply mechanisms, distribution strategies, and incentive alignment. The proposed framework emphasizes the importance of balancing inflationary and deflationary forces, designing value accrual mechanisms that benefit both users and network developers, and embedding governance protocols that enhance transparency and resilience. Additionally, this study explores the interplay between token utility and network effects, underscoring how strategic token design can accelerate ecosystem growth while maintaining decentralization. By analyzing successful Web3 projects such as Ethereum, Polkadot, and Cosmos, the paper extracts best practices and highlights potential pitfalls that hinder ecosystem scalability and trust. Furthermore, it evaluates regulatory implications, sustainability challenges, and market volatility, proposing adaptive policy mechanisms to future-proof token economies. The framework provides a roadmap for developers, investors, and policymakers aiming to build or assess blockchain ecosystems that are not only technologically sound but also economically viable. In doing so, this research bridges the gap between technical blockchain design and economic sustainability, offering actionable insights for fostering inclusive, community-driven, and robust Web3 infrastructures. As blockchain adoption accelerates globally, strategic tokenomics will be pivotal in shaping the next generation of digital economies, ensuring equitable value creation and distribution in decentralized environments.
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.
Decentralization is a foundational principle of permissionless blockchains, with consensus mechanisms serving a critical role in its realization. This study quantifies the decentralization of consensus mechanisms in proof-of-stake (PoS) blockchains using a comprehensive set of metrics, including Nakamoto coefficients, Gini, Herfindahl-Hirschman Index (HHI), Shapley values, and Zipf’s coefficient. Our empirical analysis across ten prominent blockchains reveals significant concentration of stake among a few validators, posing challenges to fair consensus. To address this, we introduce two alternative weighting models for PoS consensus: Square Root Stake Weight (SRSW) and Logarithmic Stake Weight (LSW), which adjust validator influence through non-linear transformations. Results demonstrate that SRSW and LSW models improve decentralization metrics by an average of 51% and 132%, respectively, supporting more equitable and resilient blockchain systems.
B. Vaidianathan, G. Regina Manicka Rajam, R.L. Shyja, M. Anitha
Know your customer (KYC) is the process of confirming user identities and assessing business risks from illicit activity. The manual KYC procedure is insecure, time-consuming, and expensive. With Blockchain technology's immutability, security, and decentralisation, such difficulties can be solved. KYC legal provide blockchain-based KYC verification by validating papers by a trustworthy network participant. This paper proposes an Ethereum-based Optimised KYC Blockchain system with symmetric AES encryption and LZ compression. The distributed ledger, cryptography, compression algorithm, and blockchain technologies make this system transparent, secure, efficient, and optimised. The suggested method uses Distributed Ledger Technology (Blockchain technology) to reduce KYC verification costs for institutions and speed up the process for clients. Our system is superior to conventional techniques since each customer only needs to be verified once, regardless of the number of institutions they want to link to. Since we use the DLT, we can securely communicate verification results with customers, boosting transparency. We created a Proof of Concept (POC) using the Ethereum API, websites as endpoints, and an android app as front office to prove its viability and efficacy. Overall, this strategy enhances customer experience, decreases costs, and boosts customer on boarding transparency.
This article compares event-driven architectures for real-time financial transactions, examining leading streaming technologies through the lens of financial industry requirements. The transition from batch processing to real-time event processing has been driven by customer expectations, regulatory mandates, and competitive pressures in modern financial services. Through evaluation of architectural patterns including event sourcing, CQRS, and saga patterns, the article demonstrates how different streaming technologies address immutability, consistency, and performance challenges unique to financial contexts. Performance characteristics and security considerations are assessed across platforms, providing decision frameworks for financial institutions balancing throughput, latency, and compliance requirements. Additionally, the article explores emerging technological trends that promise to further transform financial processing capabilities, including serverless computing, multi-cloud strategies, artificial intelligence integration, distributed ledger technologies, and edge computing solutions.
The rapid expansion of decentralized finance (DeFi) applications has catalyzed the emergence of new blockchain systems at an unprecedented pace. However, these systems are largely evolving in isolation, hindering the development of a cohesive ecosystem where value and data can flow seamlessly across networks. Blockchain interoperability technologies are introduced to break down these communication barriers and facilitate effective interactions between different blockchain systems. In recent years, numerous approaches and solutions to blockchain interoperability have been proposed. While some reviews have attempted to categorize cross-chain solutions based on blockchain standards and architectures, a more in-depth analysis is warranted. In this work, we investigate mainstream cross-chain solutions from the perspective of their principles, applications, protocols, and performance. To clarify the concept of blockchain interoperability, we propose a conceptual model that characterizes both asset interoperability and data interoperability. Furthermore, we introduce a hierarchical architecture to categorize and analyze representative cross-chain solutions, covering both academic research and industrial implementations. To maximize the utility of this review for a wide audience, we also highlight open challenges and identify future directions in the field of blockchain interoperability, expecting to provide a comprehensive overview of cross-chain solutions.
Cloud computing has become a critical component of modern IT infrastructure, offering businesses scalability, flexibility, and cost efficiency. Unoptimized cloud migration strategies can lead to significant financial waste due to inefficient resource allocation, redundant workloads, and unpredictable cloud expenses. Traditional methods often rely on static provisioning and manual decision-making, leading to suboptimal cloud resource utilization. This research introduces an AI-driven framework for intelligent cloud planning and migration aimed at reducing cloud costs while maintaining high performance and compliance standards. The proposed framework leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques to automate workload distribution, real-time scaling, and dynamic cost optimization. It integrates Predictive Analytics Engine: Uses AI models (Long Short-Term Memory LSTMs, CNNs, and Transformers) to analyze historical workload data and forecast future resource demands. Optimization Algorithm: Implements AI-driven cost minimization functions, optimizing resource allocation while maintaining Quality of Service (QoS). Automated Migration Engine: Reduces manual intervention by executing AI-based cloud workload transfers efficiently. Security and Compliance Module: Uses explainable AI (XAI) and federated learning to maintain cloud security, privacy, and regulatory compliance. A proof of concept (PoC) is developed and evaluated across multiple cloud platforms (AWS, Azure, Google Cloud) with real-world datasets. Experimental results indicate that the AI-driven framework achieves: Cost savings of up to 42% compared to traditional cloud migration strategies. Resource utilization improvement by 53%, ensuring minimal wastage. Reduction in system downtime by 75%, leading to higher reliability. Reduction in manual intervention by 85%, automating resource scaling and load balancing. The research paper also presents real-world case studies across finance, healthcare, e-commerce, and manufacturing sectors, demonstrating the tangible impact of AI-based cloud optimization. This research explores future advancements in cloud computing, including Quantum AI for cloud workload acceleration, Blockchain for transparent cloud cost auditing, and Decentralized AI governance for multi-cloud management. This study contributes to the growing field of AI-driven cloud cost optimization, providing a roadmap for enterprises, cloud architects, and AI researchers to achieve cost-efficient, high-performance, and automated cloud management.
One of the most emblematic theorems in the theory of distributed databases is Eric Brewer’s CAP theorem. It stresses the tradeoffs between Consistency, Availability, and Partition and states that it is impossible to guarantee all three of them simultaneously. Inspired by this, we introduce the new CAP theorem for autonomous consensus systems, and we demonstrate that, at most, two of the three elementary properties, Consensus achievement (C), Autonomy (A), and entropic Performance (P) can be optimized simultaneously in the generic case. This provides a theoretical limit to Blockchain systems’ decentralization, impacting their scalability, security, and real-world adoption. To formalize and analyze this tradeoff, we utilize the IoT micro-Blockchain as a universal, minimal, consensus-enabling framework. We define a set of quantitative functions relating each of the properties to the number of event witnesses in the system. We identify the existing mutual exclusions, and formally prove for one homogenous system consideration, that (A), (C), and (P) cannot be optimized simultaneously. This suggests that a requirement for concurrent optimization of the three properties cannot be satisfied in the generic case and reveals an intrinsic limitation on the design and the optimization of distributed Blockchain consensus mechanisms. Our findings are formally proved utilizing the IoT micro-Blockchain framework and validated through the empirical data benchmarking of large-scale Blockchain systems, i.e., Bitcoin, Ethereum, and Hyperledger Fabric.
Blockchain technology is gaining traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in health data management. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain's strong immutability by storing data directly on-chain. In this work, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, highlighting the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, by designing and implementing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves 2x faster data insertion, 500x faster retrieval, 60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data.
Fujiang Yuan, Xia Huang, Long Tai Zheng, Lusheng Wang · 8 authors
With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design.
Payroll and compensation backends represent some of the most legally sensitive and financially consequential components of enterprise software systems. Traditional implementations often rely on mutable database records that overwrite prior state, complicating auditability, replay safety, and regulatory compliance. In cloud-native, distributed environments, mutable state models further amplify risks related to concurrency, partial failures, and inconsistent recovery. This paper proposes an immutable ledger-based modeling approach for payroll and compensation backends deployed in cloud-native architectures. By treating every compensation-relevant change as an append-only, versioned ledger entry, the system achieves deterministic state reconstruction, strong audit traceability, and resilience under distributed execution. The study examines canonical ledger design, event-sourced architectures, retroactive correction handling, concurrency isolation, and cross-entity coordination within compensation workflows. It also analyzes partitioning strategies, operational resilience, and anti-patterns associated with mutable payroll systems. The resulting framework demonstrates how immutable modeling principles—when combined with identity-scoped partitioning and cloud-native scalability patterns—enable high-integrity financial backend systems that remain deterministic, replay-safe, and regulatorily compliant under high concurrency and infrastructure variability.
Smart contracts are small programs that run autonomously on the blockchain, using it as their persistent memory. The predominant platform for smart contracts is the Ethereum VM (EVM). In EVM smart contracts, a problem with significant applications is to identify data structures (in blockchain state, a.k.a. "storage"), given only the deployed smart contract code. The problem has been highly challenging and has often been considered nearly impossible to address satisfactorily. (For reference, the latest state-of-the-art research tool fails to recover nearly all complex data structures and scales to under 50% of contracts.) Much of the complication is that the main on-chain data structures (mappings and arrays) have their locations derived dynamically through code execution. We propose sophisticated static analysis techniques to solve the identification of on-chain data structures with extremely high fidelity and completeness. Our analysis scales nearly universally and recovers deep data structures. Our techniques are able to identify the exact types of data structures with 98.6% precision and at least 92.6% recall, compared to a state-of-the-art tool managing 80.8% and 68.2% respectively. Strikingly, the analysis is often more complete than the storage description that the compiler itself produces, with full access to the source code.
In recent years, blockchains have been attracting attention because they are decentralized networks with transparency and trustworthiness. Generally, transactions on blockchain networks with higher transaction fees are processed preferentially compared to others. The processing fee varies significantly depending on other transactions; it is difficult to predict the fee, and it may be significantly high. These are major barriers to blockchain utilization. Although several consensus algorithms have been proposed to solve these problems, their performance has not been fully evaluated. In this study, we model a blockchain system with a base fee, such as in Ethereum, via a priority queueing model. To assess the model’s performance, we derive the stability condition, stationary probability, average number of customers, and average waiting time for each type of customer. In deriving the stability conditions, we propose a method that uses the theoretical values of the partial models. These theoretical values match well with those obtained from Monte Carlo simulations, confirming the validity of the analysis.