One major issue associated to microservices architectures is aspects related to data consistency and management of data within such a context. To measure the effect on system integrity, this research paper compares different data consistency models which are, eventual consistency, distributed transactions through Two-Phase Commit (2PC), the Saga pattern. Employing historical data analysis, case studies, and examining the recent technological advancements, the study gives a comparative assessment of various approaches in terms of their capabilities to achieve transaction latencies and throughput and possibility to guarantee consistent results. Based on the results, it is evident that although eventual consistency models provide high scale-out and availability characteristics, the models experience transitory data inconsistency. Consistency is high since data is accessed from, and committed to, multiple databases in a distributed transaction but they have performance penalties. The Saga patterning gives a balanced view but at the same time brings together additional design difficulty. New trends like Distributed Ledger Technology (DLT) as well as new consensus algorithms that can be used to solve problematic approaches like the Raft algorithm give new possibilities for improving consistency of the data but bring their own concerns. The study implies that one must choose the right consistency model depending on the need and performance Degree and the future research can consider the use of the combination of several models along with the improvement of technological solutions to have a better deal with data consistency in microservices.
Abstract In the context of the IoT platform, the smart home represents a quintessential application scenario. Here, device-to-device (D2D) collaboration serves as the core element of its ecosystem, playing a crucial role in implementing diversified automated execution scenarios that are customized to fulfill user requirements. The progressive integration of edge computing and AI technologies has enhanced the collaboration among heterogeneous devices. Nevertheless, the conventional centralized D2D collaboration authorization decision-making supported by a single IoT Hub violates the Principle of Least Privilege (PoLP), which is a foundational design tenet that has been empirically validated as an optimal engineering practice for enhancing system security and reliability in IoT ecosystems. If there is a trade-off of PoLP violations, it fails to meet the users’ Quality of Experience (QoE). To address this issue, we propose D2DA, a distributed authorization decision-making model for smart home D2D collaboration, which constructs a distributed decision-making consensus network suitable for the edge side of smart homes by leveraging distributed ledger technology. D2DA presents a machine learning algorithm with a time complexity of O ( n ). Through this algorithm, consensus nodes can be efficiently and dynamically selected. Furthermore, D2DA ensures the security of the D2D collaboration process via wallets and hash verification. Extensive experiments conducted on a real-world smart home scenario validate that the decision-making latency of D2DA is on par with that of a single IoT Hub mode. The average latency for verifying the correctness of the newly added execution results is only 0.08% of the system time of D2DA, which is negligible.
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
Blockchain technologies had a significant impact on many sectors of contemporary society, with virtual currencies being the most prominent example. The introduction of the Ethereum blockchain and its native support for smart contracts, self-enforcing programs that enable trustworthy digital interactions, has broadened the possible adoption contexts. These programs possess unique characteristics, such as code immutability and autonomous execution, which necessitate innovative testing methodologies. Despite their growing adoption, current testing practices and tools for smart contracts lag behind those available for traditional software systems, raising concerns about the reliability of decentralized applications. This dissertation addresses these challenges through three core research objectives. First, it advances mutation testing for Ethereum smart contracts by introducing a practical framework and tool that support Solidity-specific test adequacy assessment and incremental mutation analysis during development. This enables developers to systematically evaluate and improve the fault-detection capabilities of their test suites based on metrics that go beyond simple code coverage. Second, it explores how smart contract auditing practices can benefit from mutation testing by integrating live mutant inspection into code reviews and automating the generation of missing test cases. This contribution enhances the auditors’ ability to identify weaknesses in the test suite and provide actionable feedback to clients. Third, it supports smart contract maintenance activities by proposing a novel capture-replay testing framework and tool for upgradeable contracts. The approach harnesses historical blockchain transactions as tests, allowing developers to detect behavioral inconsistencies introduced by upgrades without the need to manually reconstruct testing scenarios. Through these contributions, the thesis aims to advance the state of smart contract quality assurance, offering both practical frameworks and theoretical insights that enhance the reliability of blockchain-based systems.
Revolutionizing distributed agile software testing, we propose BCTestingPlus, a groundbreaking blockchain-based platform. In the traditional distributed agile software testing lifecycle, software testing has suffered from a lack of trust, traceability, and security in communication and collaboration. Furthermore, developers’ failure to complete unit testing has been a significant bottleneck, causing delays and contributing to project failures. Introducing BCTestingPlus, a transformative blockchain-based architecture engineered to overcome these challenges. This framework integrates blockchain technology to establish an inherently transparent and secure environment for software testing. BCTestingPlus operates on a private Ethereum blockchain network, offering superior control and privacy. By implementing smart contracts on this network, BCTestingPlus ensures secure payment verification and efficient acceptance testing. Crucially, it aligns development and testing teams toward shared objectives and guarantees equitable compensation for their efforts. The experimental results and findings conclusively show that this innovative approach demonstrates that BCTestingPlus significantly enhances transparency, bolsters trust, streamlines coordination, accelerates testing, and secures communication channels for all parties involved in the distributed agile software testing lifecycle. It delivers robust security for both development and testing teams, ultimately transforming the efficiency and reliability of distributed agile software testing.
We present the availability and reproducibility report of the ACM SIGMOD 2025 paper titled ''InTime: Towards Performance Predictability In Byzantine Fault Tolerant Proof-of-Stake Consensus''. Following the instructions provided the authors, we evaluated the artifacts hosted on Github. The reviewers confirmed that the codebase is functional and evaluatable. Reproducibility was straightforward as the authors provided automated scripts that simplified the execution of experiments. Experiments on single machine were reproduced while experiments requiring a large cluster of machines are constrained by limited resource capacity and instructions. Hence, the paper's core claims were partially reproduced.
As financial institutions increasingly migrate their core platforms to microservices-based architectures, the challenge of managing distributed transactions has gained critical importance. Banking APIs typically require atomicity and consistency across multiple services—such as account management, fraud detection, notifications, and audit trails all of which operate independently with isolated data stores. In such an ecosystem, ensuring consistency, performance, and fault tolerance becomes a balancing act that traditional and modern transaction patterns attempt to resolve differently. This paper explores and contrasts two dominant approaches to distributed transaction management: the Two-Phase Commit (2PC) protocol and the Saga Pattern, particularly in the context of mission-critical banking applications. 2PC has long been considered the gold standard for ensuring atomicity and strong consistency in distributed systems. However, its blocking nature, reliance on a centralized coordinator, and vulnerability to network partitions make it less suitable for high-throughput, globally distributed systems common in modern fintech platforms. On the other hand, the Saga Pattern, an eventual consistency model that orchestrates a sequence of local transactions with compensating rollback operations—offers better fault tolerance and non-blocking behavior. Yet, its trade-offs include the complexity of compensating logic, lack of strict ACID guarantees, and potential for data anomalies if not carefully implemented. To ground the discussion in real-world reliability needs, I introduce a chaos engineering-based simulation that demonstrates the behavior of both 2PC and Saga under controlled failure scenarios, such as inter-service latency spikes and partial service outages. We benchmark recovery times, resource locking, system availability, and data reconciliation behavior using a representative banking microservice architecture deployed in a containerized environment. My findings reveal that Saga outperforms 2PC in terms of availability and fault recovery, making it suitable for user-facing, latency-sensitive operations. However, 2PC remains superior for operations demanding immediate consistency and compliance with strict audit requirements, such as core ledger updates. Based on this analysis, we propose a hybrid transaction strategy that applies 2PC to core financial operations and Saga to surrounding auxiliary services, striking a balance between performance and correctness. This study offers practical design insights for architects building resilient, scalable, and regulation-compliant financial systems. It also highlights the need for adaptive orchestration platforms capable of dynamically selecting transaction models based on context and SLA requirements.
Md. Nahidul Islam Opu, Md Shahidul Islam, Sara Rouhani, Shaiful Chowdhury
Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both general software development issues and blockchain-specific concerns are nearly equally represented, with Wallet Management and UI Enhancement emerging as the most prominent topics. We further examine the temporal evolution of issue categories and resolution times, finding that Wallet issues not only dominate in frequency but also exhibit the longest resolution time. Conversely, Mechanisms issues are resolved significantly faster. Issue frequency surged after 2016 with the rise of Ethereum and decentralized applications, but started declining after 2022. These findings enhance our understanding of blockchain software maintenance, informing the development of specialized tools and practices to improve robustness and maintainability.
Md. Shariful Islam, Mohammad Saifur Rahman, M. Sohel Rahman
Log management is crucial for ensuring the security, integrity, and compliance of modern information systems. Traditional log management solutions face challenges in achieving tamper-proofing, scalability, and real-time processing in distributed environments. This paper presents a blockchain-based log management framework that addresses these limitations by leveraging blockchain's decentralized, immutable, and transparent features. The framework integrates a hybrid on-chain and off-chain storage model, combining blockchain's integrity guarantees with the scalability of distributed storage solutions like IPFS. Smart contracts automate log validation and access control, while cryptographic techniques ensure privacy and confidentiality. With a focus on real-time log processing, the framework is designed to handle the high-volume log generation typical in large-scale systems, such as data centers and network infrastructure. Performance evaluations demonstrate the framework's scalability, low latency, and ability to manage millions of log entries while maintaining strong security guarantees. Additionally, the paper discusses challenges like blockchain storage overhead and energy consumption, offering insights for enhancing future systems.
Flaviene Scheidt de Cristo, Jorge Augusto Meira, Jean-Philippe Eisenbarth, Radu State
Several distributed systems based on unstructured p2p networks, such as blockchains, rely on underlying protocols to disseminate messages in a fast and reliable way. As the state-of-the-art for message dissemination in blockchains, GossipSub guarantees delivery and resilience against attacks and byzantine faults by scaling pubsub dissemination without exceeding bandwidth or overloading peers. Although GossipSub relies heavily on the way its mesh is constructed, there is little insight into how different configuration parameters impact the overall performance of the system. This study analyzes the relationships between the configuration and the performance of GossipSub from a causal point of view using the concrete case of the XRPL. By employing graphical causal methods to investigate the strength of those connections, this study goes towards the direction of finding the best configuration for GossipSub for different domains, without the need for excessive empirical tests.
Dan Lin, Ziye Zheng, Jiajing Wu, Jingjing Yang · 8 authors
Cross-chain technology enables seamless asset transfer and message-passing within decentralized finance (DeFi) ecosystems, facilitating multi-chain coexistence in the current blockchain environment. However, this development also raises security concerns, as malicious actors exploit cross-chain asset flows to conceal the provenance and destination of assets, thereby facilitating illegal activities such as money laundering. Consequently, the need for cross-chain transaction traceability has become increasingly urgent. Prior research on transaction traceability has predominantly focused on single-chain and centralized finance (CeFi) cross-chain scenarios, overlooking DeFispecific considerations. This paper proposes ABCTRACER, an automated, bi-directional cross-chain transaction tracing tool, specifically designed for DeFi ecosystems. By harnessing transaction event log mining and named entity recognition techniques, ABCTRACER automatically extracts explicit cross-chain cues. These cues are then combined with information retrieval techniques to encode implicit cues. ABCTRACER facilitates the autonomous learning of latent associated information and achieves bidirectional, generalized cross-chain transaction tracing. Our experiments on 12 mainstream cross-chain bridges demonstrate that ABCTRACER attains 91.75% bi-directional traceability (F1 metrics) with self-adaptive capability. Furthermore, we apply ABCTRACER to real-world cross-chain attack transactions and money laundering traceability, thereby bolstering the traceability and blockchain ecological security of DeFi bridging applications.
Smart contracts are fundamental pillars of the blockchain, playing a crucial role in facilitating various business transactions. However, these smart contracts are vulnerable to exploitable bugs that can lead to substantial monetary losses. A recent study reveals that over 80% of these exploitable bugs, which are primarily functional bugs, can evade the detection of current tools. Automatically identifying functional bugs in smart contracts presents challenges from multiple perspectives. The primary issue is the significant gap between understanding the high-level logic of the business model and checking the low-level implementations in smart contracts. Furthermore, identifying deeply rooted functional bugs in smart contracts requires the automated generation of effective detection oracles based on various bug features.To address these challenges, we design and implement PromFuzz, an automated and scalable system to detect functional bugs in smart contracts. In PromFuzz, we first propose a novel Large Language Model (LLM)-driven analysis framework, which leverages a dual-agent prompt engineering strategy to pinpoint potentially vulnerable functions for further scrutiny. We then implement a dual-stage coupling approach, which focuses on generating invariant checkers that leverage logic information extracted from potentially vulnerable functions. Finally, we design a bug-oriented fuzzing engine, which maps the logical information from the high-level business model to the low-level smart contract implementations, and performs the bug-oriented fuzzing on targeted functions. We evaluate PromFuzz from 4 perspectives on 5 ground-truth datasets and compare it with multiple state-of-the-art methods. The results show that PromFuzz achieves 86.96% recall and 93.02% F1-score in detecting functional bugs, marking at least a 50% improvement in both metrics over state-of-the-art methods. Moreover, we perform an in-depth analysis on 10 real-world DeFi projects and detect 30 zero-day bugs. Our further case studies, the risky first deposit bug and the AMM price oracle manipulation bug on real-world DeFi projects, demonstrate the serious risks of the exploitable functional bugs in smart contracts. Up to now, 24 zero-day bugs have been assigned CVE IDs. Our discoveries have safeguarded assets totaling $18.2 billion from potential monetary losses.
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.
Cloud computing is popular in modern businesses because it is scalable, versatile, and cost-effective. Due to the spread and complexity of cloud infrastructures, fault tolerance is crucial. This research focuses on improved cloud failure tolerance using Distributed Ledger Technology (DLT). Decentralised and immutable DLT offers verifiable and transparent transaction history, data consistency, and decentralised recovery, promising to improve cloud infrastructure stability and robustness. This study examines how DLT improves cloud-based detection, recovery, and fault tolerance. Using blockchain and cloud services to create a fault-tolerant system is novel. Comparison of fault tolerance solutions in existing cloud settings with DLT integration is the plan. Data was obtained during failure testing simulations using genuine cloud infrastructure platforms and blockchain networks. Recovery time, defect detection, energy utilisation, and uptime were measured. Graphviz was used to create flowcharts, and Matplotlib was utilised for graphs and plots. Test results show improved fault tolerance due to lower fault impact and faster recovery from DLT. DLT can make cloud systems more fault-tolerant and resilient, setting the pace for distributed computing innovation, the research concludes.
We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.
Testing in the software development process is one of the crucial disciplines. The immutability of deployed smart contracts makes tests particularly important in their design and implementation. The objective is to minimize the costs of smart contract testing by reducing the number of test cases. The article presents the test suite reduction method for smart contracts, which exploits the knowledge of their design manner. In particular, the design of a mechanism for evaluating the list of verification rules controlling the execution of transactions was used. The proposed method defines a minimum set of test cases for smart contracts designed according to a specific design pattern. The test case reduction level was shown using analytical formulas enclosed in the paper. Compared to full coverage, the method reduces over 96% of test cases for a smart contract with at least eight verification rules. The performance tests were conducted and the analysis of the results was enclosed as well. Both the smart contract evaluation time and the test suite execution time are less than 0.01 of a millisecond. Besides, the method reduces the number of test data for smart contracts by over 79%. Therefore, the method can be especially useful in continuous software integration and delivery.
Advanced cloud automation architectures establish the foundation for transformative humanitarian aid operations through resilient, scalable infrastructure deployments. This technical transformation utilizes containerized microservices, distributed edge computing nodes, and event-driven patterns that maintain operational integrity despite connectivity challenges characteristic of disaster environments. Distributed ledger integration with standardized API frameworks facilitates secure tracking across heterogeneous systems, while multi-cloud analytics platforms with machine learning capabilities enable proactive disaster management. These systems demonstrate exceptional performance metrics in real-world implementations, including significant reductions in processing latency, dramatic improvements in forecast accuracy, and unprecedented resource tracking transparency. The widespread adoption of these technologies represents a fundamental shift from traditional emergency response toward sophisticated, automated solutions that dynamically adapt to changing conditions. As edge computing, quantum optimization, and adaptive machine learning frameworks continue to mature, the humanitarian sector finds itself at the leading edge of distributed systems innovation, deploying complex cloud architectures that establish new benchmarks for performance and reliability under extreme conditions.
Microservices commonly rely on bearer tokens such as JSON Web Tokens (JWTs). Although fast, these tokens reveal user claims to every service and can be reused if stolen. This thesis designs and evaluates a privacy-preserving alternative based on zero-knowledge proofs (ZKPs) that verifies each request under a zero-trust model without disclosing identity attributes. The authentication prototype service is built with two implementations (TypeScript/NestJS and Rust) and compared with a JWT baseline in privacy, access control correctness, and performance. Across six concurrency levels (1, 10, 15, 20, 25, 30) over 15-second runs, CPU and memory usage were sampled every 250 ms, and means were reported across the concurrency points. The workflow achieves zero attribute disclosure while keeping verification practical: verification adds 1.8 ms per microservice hop with a native Rust verifier and ∼130 ms with a NestJS verifier. Proof generation is the main computational cost (∼0.7s in NestJS; ∼1.4s in Rust), with NestJS demonstrating superior performance due to efficient WASM witness generation and reuse of artefacts. These measurements were taken on the same host with an otherwise idle machine to control variance. Formal statistical significance would require multiple independent repetitions; the present analysis reports central tendencies and observed stability. Contributions are: (i) a working ZKP-based authentication workflow suitable for microservices, (ii) a dual-stack prototype and comparative evaluation against JWT, and (iii) evidence that privacy can be improved without prohibitive verification overhead.
Illicit transaction detection on blockchain networks presents a critical challenge due to the pseudonymous, decentralized, and high-volume nature of decentralized finance (DeFi) ecosystems. Traditional machine learning models struggle to effectively capture the temporal dynamics and irregular patterns of illicit behavior, while graph-based methods often incur high computational costs and rely on static relational structures. In this paper, we propose a novel dual-attention framework—GAM-CoT Transformer—for robust transaction-level anomaly detection. The proposed model integrates two key components: a Global Attention Module (GAM) that adaptively reweights feature channels and temporal steps to emphasize salient patterns, and a Contextual Transformer (CoT) block that efficiently models short-range dependencies using grouped convolutions instead of full self-attention. This design enables the model to simultaneously achieve computational efficiency, temporal expressiveness, and improved detection sensitivity. We evaluate our approach on a real-world blockchain transaction dataset and demonstrate its superiority over conventional classifiers including Random Forest, XGBoost, and LSTM-based models. The GAM-CoT Transformer achieves higher recall and F1 scores, particularly for the minority illicit class, while maintaining fast convergence and deployment scalability. Our method offers a practical and effective solution for enhancing the security of blockchain systems through intelligent transaction behavior modeling.
Zubaida Rehman, Iqbal Gondal, Hai Dong, Mengmeng Ge · 6 authors
Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.
Runtime Verification (RV) of smart contracts ensures the absence of exploitations within a transaction during execution. It is a crucial security aspect that is often omitted due to its high onchain overhead. The lack of RV in public blockchains allowed attackers to compromise vulnerable contracts and cause significant monetary losses. Existing runtime protection solutions fail to address the onchain overhead limitation, which may hinder their deployment and undermine their effectiveness. To address this problem, we propose SRP (Smart contract Runtime Protection), an efficient framework that integrates an off-chain mechanism with on-chain contract execution to minimize the burden of RV. Our hybrid architecture aims to protect deployed smart contracts from real-time attacks while maintaining blockchain scalability. We first present SRP from a design perspective proposing a protocol customized for off-chain RV interoperability. Then, we evaluate our approach empirically and demonstrate the applicability of SRP using a proof-of-concept implementation on a local Ethereum network. We further generalize our evaluation results using a queue-theoretic approach. We present a discrete-time queuing network model of SRP and the block creation process. The model is then used to conduct an extensive data-driven parametric evaluation of SRP compared to onchain-only RV. Our empirical, experimental, and quantitative results indicate the feasibility and efficiency of our approach, where SRP outperforms the onchain-only mechanism in terms of service time and throughput, for increasing workloads. Lastly, we introduce a game-theoretic incentive mechanism enhancing the robustness and security of SRP. We analyze the security of SRP and demonstrate that the presence of at least one honest validator serves as a deterrent to RV forging attacks.
Abstract: In the ever-evolving landscape of distributed systems, ensuring safety, efficiency, and scalability remains a paramount challenge. BlockEdge emerges as a pioneering framework designed to address these critical issues by leveraging the principles of blockchain technology and advanced consensus mechanisms. This abstract outlines the key features, innovations, and potential impacts of BlockEdge on the realm of distributed computing. BlockEdge integrates blockchain's immutable ledger properties with a novel consensus algorithm tailored for distributed systems. Unlike traditional blockchain applications that prioritize decentralization for financial transactions, BlockEdge focuses on enhancing the performance and reliability of distributed applications. The framework employs a hybrid consensus model that combines Byzantine Fault Tolerance (BFT) with Proof-of-Stake (PoS), optimizing both security and energy efficiency. A standout feature of BlockEdge is its modular architecture, which allows seamless interoperability between different types of distributed networks. This modularity facilitates the integration of various consensus protocols, catering to the specific needs of diverse applications, from IoT networks to large-scale data processing systems. By enabling secure and efficient cross-chain communication, BlockEdge effectively mitigates the silo effect prevalent in current distributed system designs.
U ovom radu su istraženi mehanizmi koji osiguravaju i decentraliziraju blok-lanac mreže čiji se konsenzus postiže mehanizmom dokaza o zalogu (engl. Proof of Stake). Dan je pregled različitih načina na koji korisnici mogu sudjelovati validaciji mreže. Predstav- ljene su prednosti za korisnike koji sudjeluju, no i mane koje se pojavljuju prisutnošću većeg broja transakcija na mreži u jedinici vremena. Objašnjen je predložen pristup koji povezuje tehnologije skaliranja blok-lanac mreža i validacije uz analizu rezultata i pri- jedloge poboljšanja te decentralizirane aplikacije.