Consensus algorithms are fundamental to the operation of distributed systems, underpinning applications ranging from blockchain technology to distributed databases. However, ensuring the correctness and resilience of these algorithms remains a significant challenge. This paper presents a novel approach to formally verifying consensus algorithms using distributed simulation. We propose a framework that allows for the emulation of a consensus algorithm on a distributed network, facilitating the detection of potential vulnerabilities and failures before deployment. The core of our method involves systematically simulating the algorithm under various failure scenarios, capturing the algorithm's behavior and identifying deviations from expected outcomes. This approach offers a practical and scalable solution for verifying consensus algorithms in real-world distributed systems, contributing to increased system reliability and security. The simulation framework utilizes the following key elements: a model of the consensus algorithm, a distributed network simulator, and a verification engine that analyzes the simulation results. We demonstrate the feasibility and effectiveness of our method through a detailed example, highlighting its potential for broad application in the verification of diverse consensus algorithms.
This paper presents a novel approach to distributed constraint satisfaction (CS) algorithms by leveraging blockchain technology for verification. The core challenge in distributed CS lies in ensuring the correctness and reliability of the algorithms across multiple nodes. Existing solutions often rely on centralized trust models, which are vulnerable to manipulation and single points of failure. Our proposed system addresses this by employing a decentralized architecture where each node executes a CS algorithm, and the resulting solutions are cryptographically verified through a blockchain. This creates an immutable audit trail, guaranteeing the integrity of the process and fostering trust among participants. The system uses a distributed consensus mechanism to maintain the blockchain and ensures that any deviation from the expected solution will be flagged. This approach significantly enhances the security, reliability, and transparency of distributed CS, opening possibilities for applications in areas like smart grids, supply chain management, and decentralized autonomous organizations (DAOs). The key innovation resides in the combination of distributed computation with verifiable blockchain records, offering a robust and trustworthy solution for complex problem-solving scenarios.
Tokenized real-world assets trade continuously on public blockchains, but thevenues that price their underlyings do not. For roughly two-thirds of wall-clocktime, an on-chain protocol must value collateral against a market that is shut. This record accompanies "Coverage Inversion: Calibration-Transparent Fair-ValueOracles for Closed-Market Hours". The paper inverts the conventionalpoint-plus-confidence oracle interface: the target coverage level tau becomes apublished product input, and every served price band carries a calibrationreceipt that a third party can reconstruct from public data. CONTENTS The paper (67 pages) and the LaTeX source arXiv compiles. A reference implementation in three languages — the Python serving path, its Rust port (pinned to the Python by a 329-case parity harness), and the Anchor programs for the on-chain publish path. The calibration artefacts: the 20 deployment scalars that define the served bands, including the SHA-256-stamped frozen artefact used for out-of-sample validation. The public band archive: an append-only record of bands actually served, with Saturday width commitments published before Monday's open, so the claims can be audited after the fact rather than taken on trust. EVIDENCE Two closed-market panels over the same ten US-listed tickers, 2014-2026: 5,996 weekend windows (Friday close to Monday open) and 22,624 overnight windows (close to next open). The headline weekend result is held out by leave-one-symbol-out cross-validation at tau = 0.95: realised coverage 0.9497 +/- 0.0128, every fold passing Kupiec. On a 40-cell symbol-by-tau grid the architecture passes 40 of 40 Kupiec tests, against 31 of 40 for the strongest practitioner baseline (GARCH-t). The same architecture, with only its gap selector changed, carries from weekends to overnight gaps — calibration transparency is a property of closed market hours generally, not of weekends specifically. Because an earnings release has a publicly known date and session, the band widens deterministically ahead of it. No incumbent oracle exposes calendar-conditioned coverage. LICENSING This record is CC BY 4.0. The source code in the reference-implementation archive is Apache-2.0 and ships its own LICENSE file, which governs that archive.
ComputeGrid is a concept paper and feasibility framework for a utility-integrated network of provider-owned compute nodes hosted at commercial buildings, public facilities, and — through a heat-recovery variant — homes. Hosts receive full metered reimbursement of node electricity plus a separate credit; nodes are aggregated by an orchestration and settlement platform (ComputeGrid OS) into one secure, grid-aware, dispatchable compute layer beneath hyperscale data centers. Version 3.0 evaluates the concept against two structural facts: residential retail electricity is the most expensive power a compute operator can buy, and a decade of distributed-compute ventures (spot marketplaces, deploy-first token networks, and heat-recovery operators) shows which configurations survive. The paper therefore leads with commercial and public-building deployment, admits residential nodes only where waste heat displaces heating the host would otherwise purchase, and requires every site to pass at least one of four economic qualifiers — near-commercial power price, monetizable heat, a priced locality premium, or measurable grid-flexibility value — before hardware is committed. The paper includes a two-sided illustrative unit ledger, host consumer-protection and property-rights baselines, rules preventing host-purchased “income” hardware, a staged offtake-first pilot plan with published kill criteria, and a claim framework that treats all economics as illustrative pending Stage 0 diligence. It is an open concept paper, not peer reviewed; no field deployment or empirical dataset is reported. Version 1.0 drafting was assisted by OpenAI ChatGPT; Versions 2.0 and 3.0 critique and revision were assisted by Anthropic Claude under the author’s direction. The author is responsible for the final claims and release.
This survey reviews adaptive communication over QUIC for distributed and agent-based systems, focusing on security-first transport design, preservation of session context, and orchestration across request-response, streaming, and publish-subscribe under a single connection.The survey organizes existing approaches along four axes: transport-layer security and auditability, telemetry-driven mode selection, congestion and priority control for multi-stream workloads, and resilience through fallbacks and graceful degradation.Techniques synthesized include authentication and rate/IP hygiene, rules and contextual bandits for mode scoring, decision arbitration, latency/throughput decomposition for online tuning, and priority scheduling on QUIC streams.To ground the taxonomy, the paper also presents a consolidated reference model-QUICPowered Adaptive Communication (QPAC)-comprising an Edge Optimizer, Security Manager, hybrid Rule+ML engine with Decision Arbiter, a Performance Monitor, Intelligent Routing with priority-aware scheduling, and a Security Audit Ledger using Merkle Tree batching for immutable, transparent logging.The survey identifies open challenges in verifiable transport-layer trust, cross-mode QoS guarantees, and audit-cost amortization, and proposes evaluation guidelines for mode-selection accuracy, latency variance under dynamic workloads, and ledger overheads in real deployments.
Trans-border data circulation across multi-jurisdictional boundaries faces an operational conflict between ownership provenance prerequisites and data minimisation mandates, compounded by the tight coupling of large data payloads with synchronous state consensus ledgers, which forces replication of feature matrices across all consensus nodes and leads to network saturation. Existing frameworks remain unequipped to resolve this, as coupling in-band payload routing with synchronous state ledgers generates communication overheads scaling with data volume. The proposed Trusted Data Space with Registration (TDSR) implements a four-layer protocol stack. A dual-plane topology establishes a decoupled storage–ledger mechanism, partitioning asynchronous payload datastores and synchronous consensus ledgers to sustain throughput independent of data dimensionality. Navigating this infrastructure, the Unified Data Resource Identifier (UDRI) executes out-of-band cross-domain routing without exposing verifier intents. Driven by the Oblivious Data Asset Registration (ODAR) mechanism, a two-phase, four-algorithm lifecycle dictates end-to-end ownership provenance. This execution shifts hypothesis testing to isolated sandboxes via an algorithm-agnostic mathematical contract, capping external data transit at a constant leakage bound. A deployed testbed across the Guangdong-Hong Kong-Macao Greater Bay Area validates the proposed architecture, supporting data circulation across divergent legal jurisdictions.
The rapid advancement of Large Language Models (LLMs) has established autonomous agents as the core vehicles for artificial intelligence applications. However, existing Internet infrastructures, primarily relying on TCP/IP and DNS, are designed for human-centric, host-to-host data transmission, inherently lacking the semantic awareness, dynamic capability discovery, and decentralized trust mechanisms required for autonomous agent interactions. To address these limitations and break the closed ecosystems of single vendors, this paper proposes AONA (Agentic Overlay Network Architecture), a novel overlay network architecture for the Internet of Agents (IoA). We first provide a multi-disciplinary scientific defense for multi-agent collaboration, demonstrating its theoretical necessity over single super-intelligence through the lenses of organizational economics, scaling principles, and the Price of Anarchy. AONA is then structured as a four-layer logical blueprint comprising the Base, Interconnection, Collaboration, and Application layers, which facilitates cross-protocol and cross-platform interoperability without disrupting the underlying physical network. To physically instantiate this blueprint, we design a distributed node infrastructure anchored by Management Root Nodes, Registry Service Nodes, Discovery Service Nodes, and Enterprise Intelligent Service Hubs for private domain integration. Finally, we detail the dynamic operational workflows-including zero-trust identity issuance, globally coordinated semantic taxonomy synchronization, intent-driven semantic discovery, and trusted metering for commercial settlement-that drive the network. This comprehensive architecture provides a robust, scalable, and secure foundation for the future of global agentic collaboration.
Xavier Casas-Moreno, Komal Thareja, Pablo de Juan Vela, Rajiv Mayani · 9 authors
The Compute Continuum—spanning IoT, Edge, Cloud, and HPC resources—is reshaping how hyper-distributed applications are designed and orchestrated. Traditional service orchestrators and workload management systems rely on centralized runtimes; however, the emerging paradigm requires decentralized coordination, where autonomous agents cooperate to achieve common goals and dynamically distribute workloads. Consensus algorithms play a crucial role in multi-agent systems (MAS), as they enable agents to reach agreement on how to coordinate and execute functionalities in a cooperative manner. While consensus has previously been applied to distributed job selection, here we extend its use to swarm environments. In this setting, agents autonomously decide which service functionalities (i.e., roles) to execute based on their capabilities and the real-time quality of service (QoS). Functionalities can be elastically activated or terminated as application needs evolve. To support this model, we leverage the COLMENA framework, a programming environment for defining and managing such dynamic services. We apply a greedy consensus-based approach to modern power systems, which are increasingly decentralized due to the large-scale integration of renewable energy sources. Centralized power plants are giving way to distributed, intermittent resources that require decentralized control paradigms. To demonstrate this, we simulate the Northeastern Power Coordinating Council’s (NPCC) 140-bus grid using the ANDES simulator in conjunction with the COLMENA middleware. We deploy this use case across six different sites in the FABRIC testbed, using up to 60 different nodes. Our results show that, under contingency scenarios such as load and generator disconnections, agents self-organize, elect local leaders, and execute optimization algorithms to stabilize grid frequency. Detection and organization times remain below 10s across all experiments, even as the number of agents per area scales from 3 to 10. Stability is restored within approximately 27s and 40s for the respective cases. Resource overhead is minimal, with CPU and memory usage remaining below 7.5% and 2%, respectively. Experiment automation and reproducibility are ensured through Kiso. These findings indicate that role-based programming models complement traditional workflows and that consensus-driven coordination can effectively decentralize decision-making in swarm environments. This approach represents a step toward enabling resilient, decentralized power systems.
Today, many computing workloads are executed in loosely coupled, geographically distributed environments where resources are owned by different organizations. Examples include inter-institutional research infrastructures, community-operated clusters, and edge deployments. As disconnections are frequent in such environments, ensuring reliable task execution remains a fundamental challenge. Kubernetes, the de facto standard for cluster orchestration, provides centralized control and strong consistency, but suffers from slow recovery when node failures occur frequently. At the opposite extreme, blockchain-based orchestration removes centralized control but incurs substantial latency due to global consensus, making it unsuitable for time-sensitive task scheduling. This paper presents Mutual Cloud, a decentralized orchestration framework that operates between these two extremes. Mutual Cloud adopts a hybrid architecture where task admission and queue management are handled in a centralized manner similar to conventional public clouds, whereas most scheduling functions, including execution-node selection and failure handling, are performed in a decentralized manner by autonomous agents using a distributed hash table. We implement a prototype of Mutual Cloud and evaluate its performance through large-scale simulation studies. The results show that Mutual Cloud maintains stable performance comparable to centralized baselines under normal conditions while achieving approximately five-second-level recovery latency under substantial node failures.
This PDF document titled "Linux Foundation Hyperledger Fabric Certified Practitioner" serves as a comprehensive resource for individuals seeking to validate their expertise in the Hyperledger Fabric framework. It outlines the certification process, including the prerequisites, learning outcomes, and preparation materials necessary for prospective candidates. The document also details the significance of the certification within the blockchain ecosystem, emphasizing the skills gained through the completion of the course. With a focus on practical applications and real-world scenarios, this PDF is an invaluable tool for developers, architects, and technical leaders aiming to enhance their credentials in distributed ledger technology. Ideal for those preparing for the certification exam, it encapsulates the knowledge and best practices essential for mastering Hyperledger Fabric. Source: https://www.certification-exam.com/en/pdf/linux-foundation-pdf/hfcp-pdf/
ABSTRACT Deterministic execution models used in distributed systems and generative pipelines share a common weakness, fixed concurrency schedules that fail under skewed workloads, hotspots, or noisy inputs. This paper proposes Adaptive Time stamp Informed Deterministic Flow Control (ATDFC), a unified concurrency control framework that combines time stamp‐based ordering with adaptive deterministic flow execution. ATDFC dynamically adjusts segmentation, initialization, and replay based on conflict intensity or input quality, enabling deterministic correctness while eliminating unnecessary computation. Experimental evaluation shows that ATDFC reduces conflict rates by more than half, lowers bandwidth consumption significantly, and maintains higher throughput and lower latency under extreme skew when compared to leading deterministic flow and concurrency control approaches. The results demonstrate that ATDFC offers a domain‐agnostic concurrency model capable of unifying deterministic computation across distributed ledgers, databases, and multimodal generative systems.
The proliferation of large language model (LLM) based AI agents has created an urgent need for robust orchestration mechanisms that can coordinate heterogeneous agents in complex, real-world environments. Existing approaches to multi-agent task allocation rely predominantly on centralized controllers, which introduce single points of failure, scalability bottlenecks, and rigid coupling between the orchestrator and the agents it manages. This paper introduces the Dynamic Task Orchestration (DTO) framework, a decentralized, capability-aware architecture for assigning tasks to AI agents in real time. The DTO framework models each agent as an autonomous economic actor that participates in a sealed-bid auction mechanism to compete for incoming tasks. Task allocation decisions are driven by three primary factors: the agent's declared capability profile, its current computational and cognitive load, and the estimated complexity of the task. The framework defines a formal task decomposition grammar, a standardized agent capability ontology, and a set of protocol-level contracts that govern bidding, delegation, execution, and result aggregation. We present the theoretical foundations of the framework, provide detailed implementation guidance, and propose a comprehensive evaluation methodology grounded in metrics for throughput, latency, fault tolerance, and resource utilization. Through analytical evaluation and scenario-based discussion, we demonstrate that the DTO framework achieves superior load balancing, resilience to agent failure, and adaptability to changing workloads compared to centralized orchestration baselines. The framework is entirely tool-agnostic and vendor-neutral, designed so that any organization can adopt it to build more robust, efficient, and scalable multi-agent systems.
Abstract. The Bitcoin blockchain is a distributed ledger of transactions maintained by a network of nodes. The protocol assumes that honest nodes control a majority of the network's computing power. Conventional wisdom suggests that a minority group cannot earn revenue disproportionate to its hashing power, implying that the rational strategy is to remain honest. However, Eyal and Sirer (2013) challenged this view by introducing "Selfish Mining," a strategy that enables a minority pool to earn rewards exceeding its share of computing power. This paper replicates the original study using Monte Carlo simulations to verify the threshold at which this attack becomes profitable. The results confirm that a pool controlling more than 1/3 of the network hashrate can theoretically achieve higher returns than honest mining.
Nur Haliza Abdul Wahab, Juniardi Nur Fadila, Nur Faszha Razali, Keng Yinn Wong
High transaction costs remain a major barrier to the scalability of Ethereum-based decentralized applications (DApps), particularly when smart contracts are computationally inefficient. Although the Solidity compiler optimizer can reduce bytecode size and improve some low-level patterns, it does not fully address structural inefficiencies in storage layout and state mutation. This study introduces controlled empirical research on the topic of manual smart contract refactoring approaches with the aim of quantifying their impact on gas usage and execution cost in the Ethereum Virtual Machine (EVM). The Remix Integrated Development Environment (IDE) and a synchronized Go-Ethereum (Geth) node (version 1.13.5) were configured to create a controlled experimental environment. This environment was connected to the Sepolia Testnet to approximate conditions similar to the Ethereum Mainnet. The role of high-cost storage operations such as SSTORE was analyzed using opcode-level transaction traces, which were collected using debug_traceTransaction. The proposed refactoring plan implies the alignment of storage slots by systematically packing the variables and data location optimization (calldata and memory) to minimize unnecessary memory allocation. The experiments show gas reductions of up to 40.68% for storage-intensive functions, with an average reduction of 28.5% across all evaluated test cases. Moreover, the findings at the opcode level have shown that it is possible to reduce the costs of unnecessary storage writes without impacting the correct functional performance of the execution. Overall, the findings show that storage-aware manual refactoring is a viable strategy for improving runtime efficiency and reducing the execution cost of Layer-1 smart contracts.
In highly dynamic and interference-prone environments, secure access to wireless communication spectrum and protection of user privacy present significant challenges. To address these issues, this paper proposes a novel framework called the Secure and Privacy-Enhanced Channel Transmission Architecture (SPECTRA). SPECTRA enables fine-grained access control policies defined by a trusted authority and enforces them through distributed Semi-Trusted Distributed Servers (STDSs) using Attribute-Based Encryption and Zero-Knowledge Proofs. This design allows for dynamic spectrum authorization without revealing the requester's sensitive attributes. Furthermore, the frequency-hopping process is modeled as a Markov Decision Process (MDP), and optimized through Proximal Policy Optimization (PPO) to adaptively enhance communication reliability and resistance to interference. SPECTRA also supports both secure replacement and secure incremental update mechanisms, incorporating hash aggregation and elliptic curve signature-based batch verification to improve the efficiency and security of multi-request authentication. Extensive simulations demonstrate that SPECTRA achieves high-accuracy access control, effective communication privacy preservation, and stable frequency-hopping performance under adversarial and uncertain conditions.
Taras Maksymyuk, Francesco Meloni, Matias Torres Diaz, Domenico Romano · 6 authors
This paper presents a blockchain-centered system architecture for cultural heritage provenance that replaces fragmented, paper-based tracking with a tamper-evident, auditable digital workflow. We assume that each object can be reliably bound to a stable physical fingerprint through an established scan-based pipeline, and we focus on how that fingerprint is represented, stored, and verified within a practical distributed ledger design. The proposed framework separates high-assurance settlement events, such as registration and ownership transfer, from high-volume operational records, such as condition updates and monitoring logs, by routing data across multiple layers and committing verifiable summaries of frequent activity to a high-security anchor chain. We also describe a deployable decentralized application stack that integrates standard token interfaces for asset representation, event-driven synchronization for user-facing services, and scalable node access to reduce read latency without requiring institutions to maintain their own node infrastructure. The result is a concrete system model that clarifies how the end-to-end provenance trail remains verifiable under realistic performance constraints.
<b>RESUMO</b>O presente artigo analisa as estratégias de otimização de gas na rede Ethereum, focando na interação técnica entre os opcodes da Ethereum Virtual Machine (EVM) e a eficiência das estruturas de dados. Com a evolução da rede e a implementação de atualizações críticas como o EIP-1559 e o upgrade Dencun, a economia de recursos computacionais tornou-se um imperativo não apenas para a viabilidade financeira das transações, mas também para a escalabilidade e segurança de contratos inteligentes. O estudo detalha os custos associados às operações de armazenamento (Storage), memória volátil (Memory) e calldata, explorando o impacto de novas funcionalidades como o armazenamento transitório (EIP-1153). Através de uma revisão sistemática de literatura técnica e benchmarks algorítmicos, demonstra-se que a escolha criteriosa de tipos de dados, o empacotamento de variáveis (variable packing) e a substituição de padrões de iteração por mapeamentos podem reduzir significativamente o consumo de gas. Conclui-se que a otimização de alto nível deve ser acompanhada por uma compreensão profunda da arquitetura de baixo nível da EVM, assegurando que a redução de custos não comprometa a integridade lógica do sistema.<br>
This thesis investigates how GossipSub configuration choices influence data dissemination for a Data-Availability Sampling (DAS) workload under strict consensus time bounds. Using a configurable PeerSim-based simulator, we model a FullDAS-like setting in which a block producer erasure-codes a blob into a 2D extended matrix of share segments, parti- tions row/column segments into topics (custody-style sharding), and executes a two-phase workflow: seeding, where share segments are disseminated over topic meshes, and sampling, where validators must retrieve uniformly random share segments within a T_DAS = 4 s deadline. We systematically vary topic granularity (TOPICS), segmentation (segment amount, SA), replication (K-copies), bandwidth caps, and omission fault rate α, and measure phase success rates, completion-time distributions (with emphasis on tail latency), bandwidth consumption, and duplication overhead. The results show that segmentation and replication dominate performance and overhead: increasing SA from coarse to moderate values reduces duplication with diminishing returns beyond SA ≈ 1-16, while larger K increases redundancy and overhead and mainly provide a robustness margin under adverse conditions. Seeding completes quickly and remains resilient for SA≥ 4 even at high omission, whereas sampling is tail-latency dominated and degrades more sharply as α increases, leading to widespread deadline misses near α = 0.5. Based on these findings, we adopt TOPICS= 256, SA= 8, K = 4, and a conservative per-node bandwidth cap of 60 Mbit/s for faulted multi-slot experiments to isolate GossipSub dynamics from bandwidth saturation.
Resilience is the ability of a (distributed) system to withstand any stressful situation without imposing massive restrictions and, above all, without long-term consequences. Permissioned distributed ledgers based on state machine replication (SMR) offer a promising approach to achieving high resilience and fairness in federated systems. SMR provides a fault-tolerant service for clients by relying on all replicas being in a consistent state. The consistent state is achieved through a consensus algorithm, typically an atomic broadcast, that decides on a total order of client requests. In the Byzantine fault model, replicas are assumed to be potentially malicious; a Byzantine fault-tolerant (BFT) protocol withstands a fixed share of malicious actors. Classic BFT SMR protocols require $n>3t$ replicas and multiple rounds of communication to withstand $t$ faulty replicas, making the implementation complex and limiting achievable throughput and increasing latency. Trusted Execution Environments (TEEs) allow to implement SMR in the so-called hybrid fault model in which replicas are assumed to be potentially Byzantine but the TEE is restricted to only fail by crashing. In the hybrid fault model, SMR requires less communication and can be implemented with a fault tolerance of $n>2t$ replicas. While many proposals aim to optimize BFT SMR by using TEEs, they still rely on a so-called leader that coordinates the agreement process among the replicas. The leader is known to be a bottleneck and, if it fails, the system has to recover from the failure and elect a new leader. The additional coordination required to elect a new leader can cause significant performance degradation, limiting the achieved resilience. Asynchronous protocols based on directed acyclic graphs (DAGs) eliminate the reliance on distinguished replicas by allowing all replicas to participate equally in the agreement process. While asynchronous approaches and the hybrid fault model independently contribute to increasing the resilience of BFT SMR systems, their combination has largely been unexplored. This dissertation aims to fill this gap by answering the following research question: What is the achievable performance and resilience of DAG-based, hybrid fault-tolerant state machine replication and under which preconditions can the leaderless nature be safely exploited to maximize throughput? We proceed in three steps to enhance the resilience and performance of BFT SMR systems and to identify potential trade-offs that arise from the assumption of TEEs and asynchrony in BFT SMR. First, we investigate the fit of TEE-based SMR for consortium-operated applications using the example of Mobility-as-a-Service ticketing systems. We propose an SMR application that uses TEEs to protect sensitive customer and mobility provider data while limiting possibilities for fraud by both customers and mobility providers, and ensuring correct billing. We find that as long as secure multiparty computation is not competitive in terms of performance, TEE-based SMR can provide significant advantages in terms of efficiency and resilience while providing reasonable confidentiality guarantees. We describe the characteristics of the Mobility-as-a-Service use case and identify similar use cases from other domains, e.g., central bank digital currencies, allowing us to conclude that our findings generalize. In the second step, we establish the foundation for a comprehensive analysis by proposing and proving TEE-Rider, the first hybrid fault-tolerant, asynchronous, and DAG-based atomic broadcast protocol. TEE-Rider builds upon the DAG-Rider protocol family and an optimized, DAG-aware, and TEE-based causal order broadcast we propose and prove. We then identify fundamental issues that arise from the combination of TEEs and asynchrony in BFT SMR. These are the impossibility of a fault-tolerant setup and the impossibility of garbage collection. Furthermore, we prove that for partially synchronous, TEE-based reliable broadcast it is impossible to reinitialize a TEE after a crash without relying on the participation of all $n$ replicas. We conclude the theoretical contributions with the proposal of the NxBFT SMR framework. Following an assumption-algorithm co-design, NxBFT is built upon TEE-Rider for the "Not eXactly Byzantine" (NxB) operating model to maximize throughput without sacrificing resilience. Moreover, NxBFT leverages SMR state transfer to circumvent the limitations imposed by TEEs and asynchrony and provides, under the assumption of partial synchrony, garbage collection, recovery, and reconfiguration. Finally, we contribute an extensive empirical evaluation. To this end, we develop the ABCperf evaluation framework focusing on the fair and straightforward comparison of fault-tolerant SMR and agreement protocols. We investigate the performance characteristics of NxBFT and find that cryptographic operations for signature creation and verification are the main bottleneck. We compare the performance of NxBFT with the state-of-the-art leader-based, hybrid fault-tolerant protocols MinBFT and Chained-Damysus and investigate the impact of the SMR client model (BFT vs. NxB), payload sizes, network sizes, network latencies, and crash faults. While all algorithms can benefit from the NxB client model, NxBFT achieves the highest throughput in all scenarios with up to $\sim500\,000$ requests per second. All algorithms show an improvement of the end-to-end latency when using the BFT instead of the NxB client model. When small latencies are required, MinBFT and Damysus are at an advantage with Damysus showing competitive throughput and impressively low latencies for small deployments. In contrast to leader-based approaches, NxBFT's performance is almost not impacted when actual crash faults occur.
Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has emerged due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further ensured by a zero-trust security framework based on distributed ledger technology. When combined, these elements enable increasingly autonomous workload orchestration, trading centralized control for adaptive, decentralized operation with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum.
Serverless computing promises on-demand elasticity and simplified deployment, yet today's production-grade serverless platforms remain tied to a single-provider, centrally scheduled control plane. This centralized scheduling model faces mounting challenges in handling heterogeneous policies, data governance constraints, and dynamic workloads for the modern web, where applications increasingly span multiple geo-distributed autonomous administrative domains. In this paper, we present Mocha, a decentralized, policy-aware framework for scheduling serverless functions across a federated ecosystem. At its core, Mocha proposes a hierarchically structured distributed hash table that embeds geographical and organizational context to facilitate locality-aware scheduling without any central authority. By implementing a formally specified compliance engine at each domain, Mocha guarantees that all regulatory, locality, and resource constraints are honored for function placement decisions. Experiments show that Mocha reduces scheduling tail latency by 4–9× compared to alternatives while maintaining full policy adherence.
Z. Liang, Bin Chen, Litao Ye, Chen Sun · 6 authors
The ERC4907 standard enables rentable Non-Fungible Tokens (NFTs) but is limited to single-user, single-time-slot authorization, which severely limits its applicability and efficiency in decentralized multi-slot scheduling scenarios. To address this limitation, this paper proposes Multi-slot ERC4907 (M-ERC4907) extension method. The M-ERC4907 method introduces novel functionalities to support the batch configuration of multiple time slots and simultaneous authorization of multiple users, thereby effectively eliminating the rigid sequential authorization constraint of ERC4907. The experiment was conducted on the Remix development platform. Experimental results show that the M-ERC4907 method significantly reduces on-chain transactions and overall Gas consumption, leading to enhanced scalability and resource allocation efficiency.
The artificial intelligence (AI) infrastructures have been centralized leading to limited accessibility, monopoly of computational resources, and an uneven distribution of services. CloudChain is a decentralized AI compute market that is made out of blockchain and can solve these challenges with a transparent, trustless, and fair system. It brings together decentralized storage, smart contracts, as well as token incentives to allow fairness, privacy, and auditing. Privacy is ensured through encryption and zero-knowledge proofs, task allocation, distribution of rewards and enforcement of SLA is automated through smart contracts. The performance metrics measured in a 30-day simulation of the major cloud providers (AWS, Google Cloud, Azure, Hetzner, Lambda Labs) and the community nodes included the performance measures of latency, throughput, and SLA compliance, as well as token allocation and resource utilization. Findings indicate that CloudChain does provide the necessary balance in the workloads, high quality in the service delivery, and equitable rewards among heterogeneous members. The suggested framework envisioned will create a democratized, secure, and sustainable platform of decentralized AI, enabling innovation, openness, and diversity of global AI ecosystems.
This study examines the relationship between gas prices and transaction values on the Ethereum blockchain, providing a detailed analysis of transaction dynamics and the factors influencing gas price determination. The correlation coefficient between gas prices and transaction values is -0.0273, indicating a very weak and negative relationship. Instead, gas prices are driven by factors such as computational intensity, network congestion, and user prioritization. Functions with higher computational demands, such as mint, recorded the highest mean gas price of 120.45 Gwei, with a standard deviation of 15.30 Gwei, while functions like approve and transfer exhibited mean gas prices of 98.30 Gwei and 110.80 Gwei, respectively. Recipient address analysis reveals a strong concentration of transaction values, with the top recipient address receiving 49.95 ETH consistently, indicating high-value operations directed toward specific accounts. High-gas transactions, defined as those above the 90th percentile, displayed a mean gas price of 191.96 Gwei with minimal variability, while their corresponding transaction values varied widely, with a mean of 23.91 ETH and a standard deviation of 13.66 ETH. These findings provide critical insights into Ethereum transaction behavior, emphasizing the role of function type and user prioritization in shaping gas price decisions. Future research should investigate the impact of network upgrades such as EIP-1559, the adoption of Layer-2 scaling solutions, and temporal trends in transaction behavior to enhance network scalability and cost efficiency as Ethereum continues to evolve.