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
With the growing demand for data storage, cloud storage has become prevalent but has introduced significant security challenges, particularly in data deletion. Malicious cloud servers may fail to execute deletions properly, posing risks to data security and software updates. This paper proposes a blockchain-based verifiable data deletion scheme, leveraging blockchain to record deletion evidence and ensure transparency. The scheme enables mutual authentication between data owners and cloud servers through smart contracts. Upon successful authentication, the server deletes the data, generates deletion evidence organized via a Merkle tree, and stores it on the blockchain. The data owner can verify the deletion by comparing the server's evidence with blockchain-stored hashes. Simulations and security analyses on Ethereum demonstrate the scheme's ability to ensure each deletion operation without relying on trusted third parties, offering safety and traceability. Experimental results highlight its efficiency and practicality, making it decentralized. Meanwhile, it is of meaningful significance to improve the royal of the cloud storage services and the reliability of node management of multi-branch software.
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.
W.T. KIM, Hocheol Nam, Muoi N. Tran, Amin Jalilov · 7 authors
Blockchain networks allow multiple client implementations of the same consensus algorithm by different developers to coexist in the same system. Ensuring correct implementations among these heterogeneous clients is crucial, as even slight semantic discrepancies in their implementations can lead to safety failures. While existing fuzzing frameworks have discovered implementation flaws in blockchain, they suffer from several challenges in testing them with sequences of conflicting blocks, called forks. Existing tools fail to adequately assess the forkhandling processes in blockchain implementations when relying on traditional code coverage feedback, which lacks the granularity needed to navigate the diverse and complex fork-handling scenarios. This paper introduces FORKY, a fork state-aware differential fuzzing framework designed to detect implementation discrepancies within the critical fork-handling process with its novel fork-aware mutation and fork-diversifying feedback mechanisms. We test FORKY on the two most influential blockchain projects: Bitcoin and Ethereum, which are the representatives of the two major blockchain consensus algorithm families, Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms.
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.
Interoperability is a fundamental challenge for longenvisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves lowcost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design an incentive scheme based on smart contracts to stimulate the honest behavior of two hosts, bypassing the requirement of the number of TEE and large memory need. We examine the conditions to guarantee the uniqueness of Nash Equilibrium via Game Theory. Furthermore, an extended optimistic concurrency control protocol is designed to ensure the correctness of concurrent contracts execution. We formally prove the security of IvyCross in the Universal Composability (UC) framework and implement a prototype atop Bitcoin, Ethereum, and FISCO BOCS. Extensive experimental results on end-to-end performance and concurrency control demonstrate the efficiency and practicality of IvyCross.
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.
In the philanthropy sector, new technology is required to address issues like lack of transparency, trust, and accountability, as well as extended processing times and expenses. So, the present research examines the application of blockchain (BC) technology to enhance the donation tracking Platform, which delivers data immutability, and transaction transparency, as well as peer-to-peer transactions, as well as it is all the rage in today's technological period. BC enables the tracking of every donation, by permitting the donors to track the usage of their funds. The three primary roles of the present research are nongovernmental organization, Donors, as well as scholar beneficiaries. Corporate social responsibility (CSR) funds of industries are considered as donations, which causes educational scholarship for the initial prototype building. Subsequently, this prototype is scalable to every feasible field of educational scholarship as well as other methods of philanthropy. Thus, a Crypto-Go-Charity is proposed to solve the problems of the supply chain by utilizing the Ethereum BC and delivering a more secure and immutable ecosystem for scholarships. Solidity language is utilized here to write smart contracts and InterPlanetary File System for document storage. The performance is demonstrated by calculating the evaluation metrics as the privacy ratio and computational time for 200 users with the number of verifications at 300, as well as a key size of 50 kb, is 97.52% and 89.28 s, respectively. Likewise, the detection rate and memory usage of the system acquired the value of 98% and 81.46 MB for 200 users with the number of verifications at 300 as well as a key size of 50 kb.
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.
The integration of Internet of Things (IoT), fog computing, and blockchain technologies is revolutionizing data management by addressing critical challenges such as latency, bandwidth, security, and energy efficiency. This chapter explores the synergy between these technologies, emphasizing their potential to optimize data collection, processing, and storage while ensuring sustainability. IoT sensors generate vast volumes of data, which fog computing processes locally to reduce latency and bandwidth usage, enabling real-time decision-making. Blockchain technology enhances data security and transparency, though energy-efficient mechanisms like Proof of Stake and Proof of Authority are essential to mitigate its high energy demands. The chapter examines the strategies for energy-efficient data management, which include smart data collection techniques, selective data handling, dynamic resource allocation, and the use of edge AI for localized processing.
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.
Smart contracts were introduced as autonomous programs running across a blockchain network. To solve the difficulty that smart contracts on the blockchain cannot interact with the real world, some blockchain oracle implementation schemes have been proposed. However, the existing data feed schemes still cannot meet the demand of off-chain intensive data feed. This paper introduces a directed acyclic graph (DAG)-distributed ledger into the blockchain oracle data feed scheme, to propose a DAG-distributed ledger-based decentralized oracle network (DDON) framework. The lightweight DAG consensus mechanism ensures data integrity and significantly reduces the entry of valueless information into the DDON, ultimately generating deterministic data. The proposed DAG structure enables parallel processing of transactions and accelerates the efficiency of data feeds. This also enables the feeding of historical data. In addition, an off-chain data feed mechanism is designed for off-chain intensive streaming of data feeds through the proposed oracle network, to separate data feeds from data fetches and improve the efficiency of feeding multiple requests. The evaluation results and discussions demonstrate that the proposed framework reduces the response time for every smart contract request and is more efficient and flexible than other mainstream oracle data feed services.
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.