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SP-Chain: Boosting Intra-Shard and Cross-Shard Security and performance in Blockchain Sharding

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arXiv 2024年

作者： Li, Mingzhe Lin, You Wang, Wei Zhang, Jin The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Institute of High Performance Computing A*STAR Singapore The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

A promising way to overcome the scalability limitations of the current blockchain is to use sharding, which is to split the transaction processing among multiple, smaller groups of nodes. A well-performed blockchain sharding system requires both high performance and high security in both intra- and cross-shard perspectives. However, existing protocols either have issues on protecting security or trade off great performance for security. In this paper, we propose SP-Chain, a blockchain sharding system with enhanced Security and performance for both intra- and cross-shard perspectives. For intra-shard aspect, we design a two-phase concurrent voting scheme to provide high system throughput and low transaction confirmation latency. Moreover, we propose an efficient unbiased leader rotation scheme to ensure high performance under malicious behavior. For cross-shard aspect, a proof-assisted efficient cross-shard transaction processing mechanism is proposed to guard the cross-shard transactions with low overhead. We implement SP-Chain based on Harmony, and evaluate its performance via large-scale deployment. Extensive evaluations suggest that SP-Chain can process more than 10,000 tx/sec under malicious behaviors with a confirmation latency of 7.6s in a network of 4,000 nodes. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

Analytic Modeling of Idle Waves in Parallel Programs: Communication, Cluster Topology, and Noise Impact 36th

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Analytic Modeling of Idle Waves in Parallel Programs: Commun...

36th International Conference on high performance computing, ISC high performance 2021

作者： Afzal, Ayesha Hager, Georg Wellein, Gerhard Erlangen National High Performance Computing Center Erlangen91058 Germany Department of Computer Science University of Erlangen-Nürnberg Erlangen91058 Germany

ISBN: (纸本)9783030787127

Most distributed-memory bulk-synchronous parallel programs in HPC assume that compute resources are available continuously and homogeneously across the allocated set of compute nodes. However, long one-off delays on individual processes can cause global disturbances, so-called idle waves, by rippling through the system. This process is mainly governed by the communication topology of the underlying parallel code. This paper makes significant contributions to the understanding of idle wave dynamics. We study the propagation mechanisms of idle waves across the processes of MPI-parallel programs. We present a validated analytic model for their propagation velocity with respect to communication parameters and topology, with a special emphasis on sparse communication patterns. We study the interaction of idle waves with MPI collectives and show that, depending on the implementation, a collective may be permeable to the wave. Finally we analyze two mechanisms of idle wave decay: topological decay, which is rooted in differences in communication characteristics among parts of the system, and noise-induced decay, which is caused by system or application noise. We show that noise-induced decay is largely independent of noise characteristics but depends only on the overall noise power. An analytic expression for idle wave decay rate with respect to noise power is derived. For model validation we use microbenchmarks and stencil algorithms on three different supercomputing platforms. © 2021, Springer Nature Switzerland AG.

关键词： Analytical models

Anomalous strain effect on the thermal conductivity of low-buckled two-dimensional silicene

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National science Review 2021年第9期8卷 23-31页

作者： Bin Ding Xiaoyan Li Wuxing Zhou Gang Zhang Huajian Gao Institute of High Performance Computing A*STAR Centre for Advanced Mechanics and Materials Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University School of Materials Science and Engineering & Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University

The thermal conductivity of two-dimensional materials, such as graphene, typically decreases when tensile strain is applied, which softens their phonon modes. Here, we report an anomalous strain effect on the thermal conductivity of monolayer silicene, a representative low-buckled two-dimensional (LB-2D)material. Reax FF-based molecular dynamics simulations are performed to show that biaxially stretched monolayer silicene exhibits a remarkable increase in thermal conductivity, by as much as 10 times the freestanding value, with increasing applied strain in the range of [0, 0.1], which is attributed to increased contributions from long-wavelength phonons. A further increase in strain in the range of [0.11, 0.18] results in a plateau of the thermal conductivity in an oscillatory manner, governed by a unique dynamic bonding behavior under extreme loading. This anomalous effect reveals new physical insights into the thermal properties of LB-2D materials and may provide some guidelines for designing heat management and energy conversion devices based on such materials.

关键词： low-buckled two-dimensional materials strain effect thermal conductivity bonding dynamics

CoChain: high Concurrency Blockchain Sharding via Consensus on Consensus

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CoChain: High Concurrency Blockchain Sharding via Consensus ...

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingzhe Li You Lin Jin Zhang Wei Wang Computer Science and Engineering Department Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Computer Science and Engineering Department Hong Kong University of Science and Technology Technology and Research Institute of High Performance Computing and Agency for Science Singapore

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.

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Systematic Review on Reinforcement Learning in the field of Fintech

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arXiv 2023年

作者： Malibari, Nadeem Katib, Iyad Mehmood, Rashid Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah21589 Saudi Arabia

Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforcement learning and Fintech to highlight the prediction accuracy, complexity, scalability, risks, profitability and performance. Major uses of reinforcement learning in finance or Fintech include portfolio optimization, credit risk reduction, investment capital management, profit maximization, effective recommendation systems, and better price setting strategies. Several studies have addressed the actual contribution of reinforcement learning to the performance of financial institutions. The latest studies included in this survey are publications from 2018 onward. The survey is conducted using PRISMA technique which focuses on the reporting of reviews and is based on a checklist and four-phase flow diagram. The conducted survey indicates that the performance of RL-based strategies in Fintech fields proves to perform considerably better than other state-of-the-art algorithms. The present work discusses the use of reinforcement learning algorithms in diverse decision-making challenges in Fintech and concludes that the organizations dealing with finance can benefit greatly from Robo-advising, smart order channelling, market making, hedging and options pricing, portfolio optimization, and optimal execution. © 2023, CC BY.

关键词： Reinforcement learning

DL-Chain: Scalable and Stable Blockchain Sharding with high Concurrency via Dual-Layer Consensus

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arXiv 2024年

作者： Lin, You Li, Mingzhe Wei, Qingsong Liu, Yong Goh, Siow Mong Rick Zhang, Jin The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Institute of High Performance Computing A*STAR Singapore Singapore The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Sharding enhances blockchain scalability by partitioning nodes into multiple groups for concurrent transaction processing. Configuring a large number of small shards helps improve the transaction concurrency of a sharding system. However, it increases the fraction of malicious nodes within each shard, easily leading to shard corruption and jeopardizing system security. Some existing works have attempted to improve concurrency by reducing the shard size while maintaining security. However, they often require frequent and time-consuming recovery of corrupted shards, leading to severe system stagnation. Also, they usually require network-wide consensus to guarantee security, which limits scalability. To address these issues, we propose DL-Chain, a blockchain sharding system that can securely provide high concurrency with stable and scalable performance. Our core idea is a Dual-Layer architecture and consensus, which consists of numerous smaller proposer shards (PSs) for transaction processing and multiple larger finalizer committees (FCs) for transaction finalization. To avoid system stagnation and thus guarantee stable performance, we ensure PSs’ liveness even if they are corrupted through the cooperation of PSs and FCs, thus eliminating the recovery process of corrupted PSs. To better trade-off security and scalability, we fine-tune the FCs to enable multiple FCs to coexist securely. As a result, DL-Chain allows a larger fraction of malicious nodes in each PS ( Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

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arXiv 2023年

作者： Alaql, Abeer Abdullah Alqurashi, Fahad Mehmood, Rashid Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia High Performance Computing Center King Abdulaziz University Jeddah21589 Saudi Arabia

The current aggressive capitalist approaches have affected social, economic, and planet sustainability. Economic issues, such as inflation, energy costs, taxes, and interest rates, are a constant presence in our daily lives and have been exacerbated by global events such as pandemics, environmental disasters, and wars. A sustained history of financial crises (the 1970s, 2008, FTX, etc.) reveals significant weaknesses and vulnerabilities in the foundations of modern economies. Another significant issue currently is people quitting their jobs in large numbers (Great Attrition). Moreover, many organizations have a diverse workforce comprising multiple generations (e.g., Generations X, Y, Z, alpha) posing new challenges. Transformative approaches in economics and labour markets are needed to protect our societies, economies, and planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multigenerational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of science for the period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorised them into 5 macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues, and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualisation methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of AI-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour

关键词： Data Analytics

Preparing a commercial quantum key distribution system for certification against implementation loopholes

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Physical Review Applied 2024年第4期22卷 044076页

作者： Vadim Makarov Alexey Abrikosov Poompong Chaiwongkhot Aleksey K. Fedorov Anqi Huang Evgeny Kiktenko Mikhail Petrov Anastasiya Ponosova Daria Ruzhitskaya Andrey Tayduganov Daniil Trefilov Konstantin Zaitsev Skolkovo Moscow 121205 Russia Vigo Quantum Communication Center NTI Center for Quantum Communications Department of Physics Faculty of Science Institute for Quantum Computing Department of Physics and Astronomy Quantum technology foundation (Thailand) Bangkok 10110 Thailand QRate Skolkovo Moscow 143026 Russia Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology Moscow 119991 Russia atlanTTic Research Center School of Telecommunication Engineering Department of Signal Theory and Communications Moscow 101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarization encoding, a qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and postprocessing. We analyze the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure.

关键词： Quantum communication Quantum cryptography Quantum protocols

From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments 24

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From Failure to Insight: Analyzing Disk Breakdowns in Large-...

Proceedings of the SC '24 Workshops of the International Conference on high performance computing, Network, Storage, and Analysis

作者： Anjus George Meng Wang Jesse Hanley Garrett Wilson Ransom John Bent Christopher Zimmer National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Department of Computer Science University of Chicago Chicago IL USA HPC Infrastructure Los Alamos National Laboratory Los Alamos NM USA High Performance Computing Los Alamos National Laboratory Los Alamos NM USA

ISBN: (纸本)9798350355543

Disk failure data provides valuable insights for preventing failures, enhancing storage robustness, guiding system design and deployment, and ensuring reliable operations at data centers. This paper introduces two disk failure datasets collected from large-scale HPC production environments over the past five years, comprising over 5,000 failure records from more than 40,000 disks. We analyzed these datasets across multiple dimensions, including temporal, spatial, and relational trends, and performed a comprehensive reliability assessment. Our analysis yielded numerous observations and insights that influence various operational aspects of HPC storage systems. We believe this study offers a holistic understanding of disk failure trends likely to interest the HPC storage community.

关键词： Cause effect analysis