With the explosive growth of online education resources, traditional education platforms that rely on centralized servers for resource distribution and storage gradually expose issues such as inefficient resource management, lack of trust in sharing, high storage costs, and a high risk of single-point failure. In response, this study designs a decentralized education resource sharing platform by integrating blockchain technology and distributed file systems. It leverages the distributed ledger, immutability, and traceability features of blockchain, along with the high data availability and low storage cost advantages of distributed file systems. Results show that the proposed education platform reaches 1,388 transactions per second when the number of nodes is 200, with a latency response time of 9.8 seconds and an average memory overhead of 268 MB. In practical performance evaluation, the platform achieves a top-10 hit rate of 97.2%, a business interruption probability as low as 4.8% under unexpected conditions, and an average resource throughput efficiency of 176 Mbps. Overall, the platform performs well in education resource sharing and demonstrates strong algorithm fault tolerance, practicality, robustness, and service stability, providing reliable technical support for global education resource sharing.
This paper explores the application of blockchain technology to manage distributed edge computing resources. The core claim is that blockchain can facilitate dynamic resource allocation and efficient utilization within edge computing environments. The proposed mechanism involves constructing a blockchain-based resource management system leveraging smart contracts to automate and optimize resource distribution. This approach addresses the challenges of centralized control, inefficient resource utilization, and security vulnerabilities commonly found in traditional edge computing models. The research investigates the potential benefits of blockchain's decentralized, transparent, and immutable ledger for enhancing edge computing resource management, ultimately leading to improved performance, scalability, and trust within distributed edge systems. The key focus is on establishing a secure and automated framework for resource sharing and access control, significantly improving the overall efficiency and reliability of edge computing deployments. ---
This paper proposes a novel approach to distributed trusted computation leveraging the inherent properties of blockchain technology. The core claim is that blockchain's consensus mechanisms and data integrity guarantees can facilitate a secure and trustworthy distributed computing environment, effectively addressing the trust issues prevalent in cloud computing. The proposed mechanism involves decomposing computational tasks into smaller sub-tasks, which are then collaboratively executed by nodes within a blockchain network. Smart contracts are employed to manage task scheduling and validate the results. This system offers an alternative to traditional trust models, utilizing cryptographic techniques and distributed consensus for enhanced security and transparency. The research explores the potential of blockchain to fundamentally transform the landscape of distributed computing, providing a robust solution for sensitive computations and data processing. The paper focuses on the technical design and theoretical underpinnings of this approach, outlining key components and potential challenges.
Smart contract deployment costs constitute an economic consideration in blockchain ecosystems, yet existing gas optimization approaches primarily focus on execution efficiency while neglecting deployment gas reduction. We analyze Solidity storage internals and identify that conventional variable packing–despite reducing storage slots–often increases deployment costs due to compiler-generated masking and shifting operations. This finding motivates State Variable Mutation , the first systematic approach designed to reduce smart contract deployment costs through guided reordering of state variable declarations. Our approach explores variable orderings to identify layouts that minimize gas-expensive storage operations while preserving semantic equivalence and storage efficiency. We implement our approach in DGRed , an open-source tool, and evaluate it on 300 real-world smart contracts. Results demonstrate deployment gas reductions of up to 32.72%, with an average reduction of 15.65% (52,850 gas units per contract), translating to total savings of 15,854,883 gas units across all contracts. Under high network congestion (200 gwei), these savings correspond to $12,381.4. Compared with state-of-the-art gas optimization tools, DGRed achieves superior deployment gas reductions (15.65% average vs. 4.91% for GasSaver and 2.83% for GASOL) while maintaining 100% semantic preservation and producing valid bytecode for all 300 contracts. In contrast, GasSaver introduces compilation errors in 148 contracts, while GASOL generates invalid bytecode in 234. DGRed 's state variable mutation produces identical execution gas to the original contract in all 100 contracts evaluated for execution gas impact, confirming that deployment optimization does not affect runtime efficiency. Additionally, DGRed 's 15.65% reduction is over 8 \(\times\) larger than the best achievable through Solidity compiler flag tuning alone, demonstrating that the two approaches are complementary. DGRed provides developers with a practical, semantic-preserving solution for deployment cost optimization without modifying contract logic or functionality. Because DGRed only reorders state variable declarations, it introduces no runtime trade-off, and entails no risk of behavioral regression.
This paper presents and evaluates a hybrid blockchain architecture for organizational information systems in which PostgreSQL remains the operational database while Ethereum Sepolia is used as an externally verifiable transaction-recording layer. The implemented proof-of-concept is a university wallet system combining a React and TypeScript frontend, an Express.js backend, PostgreSQL with Drizzle ORM, and an OpenZeppelin-based ERC-20 smart contract deployed on Ethereum Sepolia. The system associates successful application transactions with corresponding Ethereum transaction hashes stored in a dedicated relational table. The experimental evaluation uses sequential workloads of 10, 50, and 100 transactions, comprising 160 measured application transactions in total, together with a separate 30-transaction database-mutation experiment. The performance evaluation measures database insertion time, blockchain transaction time, end-to-end execution time, success rate, and gas consumption. All 160 performance-test transactions completed successfully. Mean database insertion time remained below 32 ms, while blockchain transaction time ranged from approximately 15.26 to 21.93 seconds and dominated end-to-end execution time. Mean gas consumption was approximately 40,324 gas per successful transfer. The database-mutation experiment modified the amount field of 7 of 30 successfully recorded transactions after their blockchain references had been established. All seven modified records retained their corresponding blockchain transaction references. However, the experiment did not perform field-level comparison between the modified database records and blockchain event contents and therefore is not presented as a complete cryptographic tamper-detection validation. The implementation and experimental artifacts are publicly available through the associated project repository. The paper presents the work as a proof-of-concept implementation and empirical evaluation of a hybrid database-to-blockchain transaction architecture.
Blessing Emmanuel Oladele, Adekunle Olugbenga Ejidokun, Chukwuemeka O. Agwu
Academic credential verification remains difficult for institutions because manual checks are slow, fragmented, and vulnerable to fraud. Blockchain can improve trust by anchoring credential proofs, but repeated verification requests and growing off-chain repositories can still create performance bottlenecks. This study presents an adaptive blockchain-powered academic credential repository that combines off-chain MySQL storage, Solidity-based hash anchoring, Redis verification caching, and metadata-aware sharding. Full academic records are not stored on-chain or in Redis; only credential hashes, verification responses, and related metadata are used for trust validation and performance optimization. A CodeIgniter 4 prototype was evaluated using synthetic academic credential records and controlled workloads of 1,000, 5,000, and 10,000 verification requests under fresh, mixed, and repeated access patterns. The results show that Redis caching substantially reduced repeated blockchain queries, especially under mixed and repeated workloads, while metadata-aware sharding improved repository organization and supported more targeted credential retrieval. Sepolia testnet validation confirmed smart-contract feasibility, including issuance, verification, revocation, gas use, confirmation time, and event evidence, but was treated separately from scalability testing. The findings indicate that combining blockchain trust anchoring with cache-aware verification and metadata-based repository partitioning can improve the scalability of academic credential repositories, provided that cache consistency, revocation handling, and deployment limitations are carefully managed.
The concept of Cloud-Edge computing has proven to be an efficient paradigm for dealing with latency-sensitive applications through closer access to computational services. However, the selection of the right Edge service provider is difficult because of the dynamic nature of resource availability, quality of service, workload variability, provider reliability, and requirements for different applications. Current approaches to Edge service allocation concentrate on cost minimization or scheduling but do not offer sufficient assistance in transparent decision-making, provider verification, adaptive Edge service allocation, monitoring, and intelligent payment management. Such approach leads to the inefficiency of the allocation process, interruptions in the execution of tasks, and increased human intervention. The paper proposes the Explainable Agent AI-Based Intelligent Edge Service Allocation Framework for Cloud-Edge Computing. The Explainable Agent AI-Based Intelligent Edge Service Allocation involves the implementation of an autonomous Explainable Agent AI in the cloud that is responsible for the provider registration, provider verification, intelligent Edge service allocation, data-aware classification of services, reliability assessment, explainable decision-making, smart contract creation, escrow payment management, and continuous monitoring of services. The Agent AI conducts a preliminary assessment of the live status of the registered providers and performs evaluations for budget compatibility, computation, storage, bandwidth, reliability, scalability, workload, past performance history of the services, service rating, connectivity reliability, and QoS to determine the best available provider of Edge service. Throughout the process, the framework keeps an eye on the quality of the services and reassigns the rest of the workload to a new provider in case of any drop in the service quality or even failure. Escrow based smart contracts ensure that the payment is done only after the successful completion of the task, which ensures fair reward and prevention of financial loss. Experimental analysis shows enhancements in the efficiency of Edge service allocation, resource utilization, service reliability, service continuity, quality of data transmission, and operational performance.
This paper extends BlockSim by introducing energy-consumption and carbon-footprint metrics that are tightly integrated with its event-driven execution model, enabling sustainability-aware evaluation alongside conventional performance metrics. The proposed extension instruments core simulation events to estimate computational and communication energy at both node and network levels, then converts electricity demand to CO 2 emissions using an emission-factor formulation that can be configured to represent different grid carbon intensities. Using the extended simulator, controlled experiments are conducted on representative PoW and PoS consensus scenarios under varying miner populations and workloads. The framework reports aggregate energy and CO 2 , as well as normalized indicators per block and per transaction, supporting reproducible “what-if” analysis without external post-processing and enabling direct comparison of protocol configurations as networks scale. The framework additionally distinguishes economically driven Proof-of-Work (PoW) energy consumption, in which the expected mining reward and cryptocurrency price are explicit inputs, from validator-count-driven Proof-of-Stake (PoS) energy consumption; it varies the carbon emission factor across grid scenarios; and it is positioned as a scenario-based evaluation tool for preliminary what-if analysis rather than a precise real-world energy estimator.
In recent years the growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI) and edge intelligence has been increasing, and with it the need for portable, scalable and secure computing infrastructures that can process vast amounts of data that is dispersed, and has very low latency. Traditional cloud infrastructures are typically based on central server deployments which can be costly to deploy, immobile, have potentially greater communication latency, and waste resources in dynamic workload environments. In this paper, we introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture. In conventional architectures, there is no intelligent resource orchestration mechanism, which can provide flexible allocation of computational resources according to the property of workload, thermal status, energy consumption, network availability and so on. The architecture also features an adaptive security layer leveraging multiple layers of authentication, secure communication protocols, blockchain for integrity verification and on-the-fly system health monitoring to enhance cyber resilience. Simulations are conducted with varying workloads to gauge the effectiveness of the proposed architecture, and compared to traditional cloud and edge-cloud architectures with the metrics of latency, throughput, CPU utilization, response time, energy consumption, thermal efficiency, and resource utilization. Experiments demonstrate significant energy savings, scalability, responsiveness of the system and efficiency of computations using secure distributed processing. The suggested architecture is viable for the coming intelligent cloud infrastructures that are essential for smart city, industrial IoT, digital healthcare, education and enterprise computing.
Multi-cloud computing is becoming a prominent paradigm to improve scalability, flexibility, reliability and costeffectiveness by leveraging services from multiple cloud providers. But distributed resource management with strong security is a big challenge in multi-cloud scenarios, which are heterogeneous and dynamic. This review paper provides an all inclusive overview on various multi-cloud architectures, deployment models,resource allocation techniques, optimization methods, and security assurance mechanisms. It covers the major resource allocation strategies such as provisioning, scheduling, load balancing, resource scaling and intelligent optimization through machine learning and metaheuristicalgorithms to optimize resource utilization and Quality of Service (QoS). Additionally, the article delves into significant security methods for protecting decentralized cloud systems, including authentication, authorization, encryption, intrusion detection, trust management, and zero-trust designs. Also, through the comparison of the most recent literature, the current research trends, challenges and limitations for optimizing resources while keeping security in mind are pointed out. According to the review, combining AI-powered optimisation with sophisticated security frameworks has the potential to enhance the performance, resilience and reliability of multi-cloud environments. Last but not least, the paper outlines future research avenues for explainable AI, federated learning, blockchain-based trust management, energy-efficient resource allocation, and autonomous cloud orchestration to enable secure, scalable, and sustainable next-generation multi cloud computing environments.
Metaverse interoperability has become necessary for uniform virtual experiences between different metaverse ecosystems. Blockchain technology along with decentralized oracle network have played a key role in representing and transferring metaverse assets across different metaverse environments. This paper discusses the concept of interoperability with respect to metaverse, representation of metaverse assets on blockchains, and shows the application of decentralized oracles in achieving metaverse interoperability. Various standards for representing assets in blockchain and their comparison is performed. In the paper, the ERC-721 standard was used for tokenizing assets and transferring them from the Avalanche Fuji testnet to the Ethereum Sepolia testnet and Arbitron Sepolia to Ethereum Sepolia. Chainlink CCIP was used to transfer NFTs between blockchains. A comparison in term of the total gas consumed in both the transactions was performed. The paper has shown that Distributed Oracle Networks (DON) have become a useful solution in providing accurate and secure data feeds as well as transferring assets between different blockchain networks.
Dileep Kumar Murala, G. Siva Krishna, P. N. V. M. Syamala Rao, Madhusudana Rao Nalluri · 5 authors
Integrating blockchain technology with cloud computing has enabled Blockchain as a Service (BaaS), a cloud-based paradigm that allows users to design, deploy, and manage customized blockchain applications, including smart contracts and domain-specific business functionalities. BaaS providers manage infrastructure provisioning, maintenance, and scalability while ensuring quality of service (QoS) compliance through service level agreements (SLAs). However, existing resource management approaches often struggle to satisfy dynamic performance requirements in a cost-efficient manner without increasing operational overhead or reducing provider efficiency. This paper presents a structured Adaptive SLA-based assistance framework for deploying Hyperledger Fabric on cloud platforms. The framework integrates automated performance monitoring using Hyperledger Caliper, SLA violation detection through programmed SLA chaincode, and automated VM scaling via the OpenStack4J API within a 3E (effective–efficient–economical) verification methodology. A greedy multi-objective scheduling mechanism guides parameter optimization by selecting scaling actions according to marginal performance gain per unit cost. Experiments were conducted on the Nectar Research Cloud using Hyperledger Fabric 2.5 to evaluate the impact of VM size, block size, peer count, and storage configuration on throughput (TPS) and average latency. Results demonstrate that the framework consistently achieves performance targets of 50%, 100%, and 200% above the baseline configuration through adaptive resource reconfiguration. Among evaluated strategies, Comb2, which combines dual block size adjustment with VM scaling, emerged as the optimal balanced configuration in terms of performance and cost efficiency. While automated monitoring, SLA enforcement, and VM scaling are fully implemented, continuous online scheduling under dynamically changing workloads remains future work. The proposed framework establishes a practical foundation for SLA-driven blockchain optimization in cloud environments and supports future extensibility to additional BaaS platforms.
The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model.
This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.
When we consider the PoW (proof-of-work) in the Bitcoin blockchain, how is the work calculated? How does this work convert to energy quantities? This paper demonstrates that in the Bitcoin blockchain, "Proof-of-Work" (PoW) is not a complex calculus equation, but rather a probabilistic brute-force search. Miners repeatedly run block header data through a cryptographic hash function, tweaking variables until they output a number that meets a strict network threshold. In the Bitcoin blockchain, Proof-of-Work (PoW) is a probabilistic brute-force search where miners repeatedly run block headers through a double SHA-256 hash function to find an output below a global target threshold. The mathematical "work" is quantified by the network Difficulty (D), requiring roughly D × 2³² expected hashes per block. To convert this cryptographic effort into physical energy, the global network hashrate is first derived by dividing total block hashes by Bitcoin’s 10-minute target block time (600 seconds). This computational rate is then bridged to the physical world using hardware efficiency—measured in Joules per Terahash (J/TH)—multiplied by operational time. Because modern semiconductor ASICs operate roughly seven orders of magnitude above the absolute thermodynamic limits outlined by Landauer's principle, nearly all electricity consumed by this cryptographic pipeline directly converts into waste heat. The calculation of this work, how it translates mathematically to network metrics, and how those metrics convert into physical energy quantities is the discussion of this paper.<b>Part 1: How the "Work" is Calculated</b><b>1. The Hashing Puzzle (Double SHA-256)</b>A miner constructs a block header containing transaction data, a timestamp, the hash of the previous block, and a changing variable called a nonce. They pass this header through the SHA-256 algorithm twice:<br>H(x) = SHA-256(SHA-256(Block Header))The resulting output is a 256-bit unsigned integer, typically represented as a 64-character hexadecimal string.<b>2. The Target (</b><b>T</b><b>)</b>The network enforces a global threshold called the Target (T). For a block to be accepted, the hash output interpreted as a massive 256-bit integer must satisfy:<br>Hash Output ≤ T<br>Because the output of a cryptographic hash function is completely random and uniformly distributed, miners cannot predict the output. Finding a valid hash is essentially a Bernoulli trial (like rolling a die with an astronomical number of sides).<b>3. Mathematical Definition of Difficulty (D)</b>Because the Target T is a massive 256-bit number that changes every 2,016 blocks, Bitcoin uses a human-readable metric called Difficulty (D), scaled relative to a baseline "genesis" target (T<sub>max</sub>).<br>T<sub>max</sub> = 0x00000000FFFF0000000000000000000000000000000000000000000000000000The difficulty formula is D = T<sub>max</sub>/TAs the network gains more miners, T drops (becomes smaller), making hashes harder to find, which increases D.<br>The expected number of hashes E[hashes] required to find a valid block at a given difficulty is proportional to D:E[hashes] = D × 2³² × T/T<sub>max</sub> (scaled to baseline expectations)<br>More simply, the total expected hashes per block is roughly:Expected Hashes ≈ D × 4.295 × 10⁹<b>Part 2: From Computational Work to Energy Quantities</b>Energy consumption is a byproduct of hardware efficiency operating over a span of time to execute these hash attempts. There is no direct algorithmic conversion from a hash to Joules in the protocol code; instead, the conversion bridges cryptographic operations and thermodynamic hardware efficiency.<b>Step 1: Calculate Total Network Hashrate (H</b><sub><strong>net</strong></sub><b>)</b>The global hashrate represents the total number of hashes computed per second across all active machines globally. It is derived directly from the current difficulty (D) and Bitcoin's target block time (t = 600 seconds or 10 minutes):<br>Hashes per block = D × 2³²<br>Network Hashrate (H<sub>net</sub>) = D × 2³²/600 [hashes/second or H/s]<b>Step 2: Factor in Hardware Efficiency (EF)</b>ASIC (Application-Specific Integrated Circuit) miners dominate Bitcoin mining. Their electrical efficiency is measured in Joules per Terahash (J/TH) or Watts per Gigashash. Let the aggregate hardware efficiency of the network be denoted as EF (expressed in Joules per Hash, J/H):EF = Total Power Consumption (Watts)/Hashrate (H/s)<b>Step 3: Energy Derivation Formula</b>To calculate the total energy consumed by the entire Bitcoin network over a specific timeframe (e.g., 1 second, 1 day, or 1 year), we multiply the network hashrate by the hardware efficiency and time (t):<br>Energy (E) = H<sub>net</sub> × EF × Δ tSubstituting H<sub>net</sub> into the equation:<br>E = (D · 2³²/600) × EF × Δ t<br>For example, assume a network difficulty (D) of roughly 80 × 10¹² (80 trillion). Also, assume an average fleet hardware efficiency (EF) of 25 Joules per Terahash (25 × 10⁻¹² J/H). Calculate energy consumed over 1 day (Δ t = 86,400 seconds):Hashes/sec = 80 × 10¹² × 4,294,967,296/600 ≈ 5.72 × 10²⁰ H/sPower (Watts) = (5.72 × 10²⁰ H/s) × (2.5 × 10⁻¹¹ J/H) ≈ 14,300,000,000 W = 14.3 GWEnergy over 1 day = 14.3 GW × 24 hours ≈ 343.2 GWhThe summary of the conversion pipeline may be expressed as<br>Target (T) ⟶ Difficulty (D) ⟶ Network Hashrate (H<sub>net</sub>) ⟶× Hardware Efficiency (J/H)⟶ Power (Watts) ⟶× Time⟶ Energy (Joules/kWh)<b>Part 3: Thermodynamic Limits and Efficiency Bounds (Landauer's Principle)</b>To fully connect cryptographic work to physical energy, we can look at the theoretical minimum energy required by the laws of physics to perform computation.<b>1. Landauer's Principle</b>Landauer's principle establishes the minimum possible amount of energy required to erase or irreversibly manipulate a bit of information at a given temperature (T<sub>temp</sub>):<br>E<sub>min</sub> = k<sub><em>B</em></sub> T<sub>temp</sub> ln(2)k<sub><em>B</em></sub> is the Boltzmann constant (1.380649 × 10⁻²³ J/K).T<sub>temp</sub> is the absolute temperature of the environment (e.g., 300 K).For a single bit modification at room temperature, this absolute thermodynamic floor is roughly 2.8 × 10⁻²¹ Joules per bit.<b>2. Comparing SHA-256 to the Thermodynamic Limit</b>A single SHA-256 calculation involves processing a 512-bit message block through 64 rounds of complex logical operations (bitwise additions, rotations, and shifts), manipulating hundreds of thousands of bits cumulatively.Theoretical minimum energy per hash: Factoring in the sheer number of bit operations inside SHA-256, even a reversibly ideal computer would require thousands of bit manipulations, putting a strict physical floor on a single hash well above Landauer's limit (roughly on the order of 10⁻¹⁹ to 10⁻¹⁸ Joules per hash under optimal theoretical conditions).Actual ASIC efficiency: Modern state-of-the-art ASIC miners (like the Bitmain Antminer S21 series) operate around 15 to 20 J/TH (1.5 × 10⁻¹¹ Joules per hash).Comparing real-world hardware (10⁻¹¹ J/H) to absolute physical limits (10⁻¹⁸ J/H) reveals that current silicon-based semiconductor technology is roughly 7 orders of magnitude away from theoretical thermodynamic efficiency—meaning nearly all energy put into Bitcoin mining converts directly into waste heat.<b>Part 4: Complete Comprehensive Master Equation</b>Combining all components into a single macro-equation, the total daily electrical energy (E<sub>day</sub>) consumed by the global Bitcoin network can be calculated directly from the network's current Difficulty (D) and the average hardware efficiency fleet-wide (EF<sub>avg</sub> in J/TH):E<sub>day</sub> = (D · 2³²/600) × (EF<sub>avg</sub> × 10⁻¹²) × 86,400<br>Where:<br>D · 2³² / 600 yields the Network Hashrate (hashes/sec).EF<sub>avg</sub> × 10⁻¹² scales Joules-per-Terahash down to Joules-per-Hash.86,400 converts seconds into one full day.This mathematical coupling ensures that as network security (Difficulty D) scales up over time to attract more capital and hashpower, energy consumption scales linearly with it, modulated only by the parallel improvement rate of semiconductor manufacturing efficiency (EF<sub>avg</sub>).To recap the end-to-end framework:The Work: Quantified by the difficulty D and scaled via 2³² to determine total expected hashes per block.The Hashrate: Derived by dividing total hashes per block by the target 10-minute block time (600 seconds).The Energy Conversion: Bridged physically using the hardware's efficiency metric (Joules per Terahash, or J/TH) multiplied over time.The Physical Bound: Bounded by thermodynamic limits like Landauer's principle, explaining why modern ASICs produce the massive amounts of waste heat characteristic of the Bitcoin network.
In the Bitcoin system, transactions arrive continuously at miners' mempools and await inclusion in future blocks. Every non-coinbase transaction must spend one or more unspent outputs created by previous transactions, inducing dependency constraints among transactions in the mempool. At the same time, miners are economically incentivized to prioritize transactions with higher fee rates, measured as transaction fee per unit size. This paper formulates the mempool linearization problem: given a set of transactions with associated fees, sizes, and dependency relationships, compute a dependency-respecting transaction ordering that maximizes fee-rate efficiency while supporting efficient updates as the mempool evolves dynamically. The problem is characterized through a partition of transactions into disjoint dependency-respecting subsets ordered by decreasing aggregate fee rate, together with an equivalent LP formulation. Motivated by structural properties of basic feasible solutions in the simplex method, a new algorithm called spanning forest linearization (SFL) is developed. Operating directly on the transaction dependency graph, SFL iteratively merges and splits chunks of transactions to refine a global ordering, and is guaranteed to terminate at an optimal solution. Evaluation on both synthetic and real-world Bitcoin mempool data shows that SFL consistently computes optimal linearizations with substantially lower runtime than competing approaches, including a method based on the parametric preflow algorithm of Gallo, Grigoriadis, and Tarjan. These results indicate that SFL provides a practical and scalable framework for transaction prioritization by decentralized miners in large and rapidly evolving mempools. SFL has also been incorporated into the Bitcoin Core codebase for transaction cluster linearization.
Abstract Stateless Ethereum is one of the most promising directions for improving blockchain scalability, but witness size remains a major deployment challenge. Existing witness generation techniques often treat all transaction workloads uniformly, resulting in unnecessary communication overhead and reduced verification efficiency. This paper presents AWCV (Adaptive Witness Compression for Stateless Ethereum Using Verkle Trees), an adaptive compression framework that reduces witness size while preserving efficient verification. AWCV combines locality-aware transaction clustering, subtree deduplication, adaptive witness construction, and delta encoding to eliminate redundant proof data without modifying Ethereum's verification semantics. The proposed framework is evaluated across representative blockchain workloads and demonstrates significant reductions in witness size, bandwidth requirements, and verification latency compared with conventional witness generation approaches. AWCV remains fully compatible with Ethereum's Verkle Tree roadmap while improving the practicality of stateless client synchronization and scalable decentralized applications. The results suggest that adaptive witness compression provides an effective and practical path toward efficient Stateless Ethereum deployments without sacrificing security or correctness.
Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog-cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog-cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals λ = 0.5-2 tasks/s, random waypoint mobility 1-5 m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~ 92%, reduces energy consumption by approximately 28%, and improves latency by ~ 22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains ~ 100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving ~ 65% residual energy versus ~ 45% in conventional systems.
Zhengqing Liu, Alberto Sonnino, Igor Zablotchi, Eleftherios Kokoris-Kogias · 5 authors
Modern blockchains rely on a modular architecture that decouples consensus from execution. Recent advances in consensus algorithms have shifted the bottleneck to the execution layer, which must deterministically follow the consensus order and handle increasingly complex, compute-intensive smart contracts. We identify that single-node validators cannot keep up, motivating the need for a scale-out design. We design Remora, a scale-out smart contract execution engine. Remora adopts an efficient asymmetric architecture with centralized transaction dispatching and distributed execution, and depends on an object versioning scheme with a strict ownership model to guarantee deterministic scale-out execution. Remora achieves up to 3x throughput improvement compared to state-of-the-art deterministic execution schemes, scales up to 250k TPS, matching modern consensus performance, and reduces latency by up to 5ms. We also show that Remora elastically adapts to bursty workloads and dynamic access patterns using real-world traces. Remora's main performance benefits come from a novel stateless-stateful separation during smart contract execution, which overlaps the execution of state-independent tasks with consensus, and a new locality-aware and load-balanced scheduling scheme.
X3Sync is a research proof-of-concept for federated cloud storage aggregation across multiple free-tier providers (Google Drive, Dropbox, Koofr). Files are chunked, compressed (zstd), and encrypted client-side using AES-256-GCM before distribution. The system introduces a dual-mode decryption architecture: Sovereign Mode, where ciphertext is relayed to the client for local decryption, and Edge Mode, where an ephemeral X25519 key exchange enables worker-side decryption. The backend runs on Cloudflare Workers with Neon PostgreSQL for metadata storage. This paper details the system architecture, security model, provider abstraction layer, and a commutative storage model for heterogeneous provider aggregation.
We study the evolution of transaction speed and fees from January 2024 through March 2026, comparing Ethereum Mainnet and its Layer 2 (L2) networks, as well as Solana and Polygon. Ethereum has undergone upgrades that have increased block size and blob count. These upgrades have doubled transactions per second (TPS) on both the Mainnet and the L2 networks. Mainnet median fees have fallen from over \$2 to under \$0.02, and L2 median fees have fallen more than 95% from \$0.05 to \$0.0015. We forecast that Mainnet median fees will converge with Solana in August 2027, but TPS will remain below 100 until 2034. The L1 Strawmap, proposing EIP-7938, a potential exponential increase in the gas limit, brings the Mainnet to only 100 TPS in January 2028. With continued blob expansion, L2s will surpass Solana TPS in March 2029 and have lower median fees by October 2026.
Gas metering on EVM-compatible blockchains assumes that execution conditions are stable: that the resource mix is constant enough to justify collapsing execution costs into a single scalar with fixed relative prices, and that state drift between submission and execution does not materially alter a transaction's outcome. We measure the extent to which this assumption fails. We present a trace-level measurement study of EVM workloads on Ethereum (L1) and Base (L2) throughout 2025, sampling 3,000 blocks per day per chain. We decompose each transaction into opcode-level execution gas, intrinsic gas, refunds, and persistent state deltas. To measure state sensitivity, we re-execute transactions from September 2025 on older states and record how gas usage and storage access patterns change. We find the resource mix to be far from stable: on Base, storage reads and compute account for 29.2% and 24.3% of execution gas, while Ethereum devotes 34.9% to storage writes. Ethereum's gas limit doubling during 2025 shifted its own profile toward compute-heavier, Base-like patterns. Base also exhibits a higher fraction of cold storage reads (49.7% versus 39.6% on Ethereum). Persistent state growth, a permanent cost priced as a transient one, reaches 456 GB on Base versus 38 GB on Ethereum. Execution outcomes are equally unstable: gas estimates vary across nearby historical states for 46.0% of transactions on Base, compared to 13.9% on Ethereum, with especially high sensitivity for MEV and DeFi activity. Storage access patterns also diverge across states, limiting the effectiveness of access lists and complicating parallel execution. Our work provides an empirical foundation for multi-dimensional gas metering and explicit pricing of state growth. They show that state-sensitive execution behavior complicates workload estimation, directly affecting transaction predictability and user experience.