The modern in-memory database(IMDB)can support highly concurrent on-line transaction processing(OLTP)workloads and generate massive transactional logs per ***-based replication protocols such as Paxos or Raft have bee...
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The modern in-memory database(IMDB)can support highly concurrent on-line transaction processing(OLTP)workloads and generate massive transactional logs per ***-based replication protocols such as Paxos or Raft have been widely used in the distributed databases to offer higher availability and ***,it is non-trivial to replicate IMDB because high transaction rate has brought new ***,the leader node in quorum replication should have adaptivity by considering various transaction arrival rates and the processing capability of follower ***,followers are required to replay logs to catch up the state of the leader in the highly concurrent setting to reduce visibility ***,modern databases are often built with a cluster of commodity machines connected by low configuration networks,in which the network anomalies often *** this case,the performance would be significantly affected because the follower node falls into the long-duration exception handling process(e.g.,fetch lost logs from the leader).To this end,we build QuorumX,an efficient and stable quorum-based replication framework for IMDB under heavy OLTP *** combines critical path based batching and pipeline batching to provide an adaptive log propagation scheme to obtain a stable and high performance at various ***,we propose a safe and coordination-free log replay scheme to minimize the visibility gap between the leader and follower *** further carefully design the process for the follower node in order to alleviate the influence of the unreliable network on the replication *** evaluation results with the YCSB,TPC-C and a realistic microbenchmark demonstrate that QuorumX achieves the performance close to asynchronous primary-backup replication and could always provide a stable service with data consistency and a low-level visibility gap.
In the design of modern compilers, the generation and optimization of intermediate code play a crucial role. Serving as a bridge between source code and target machine code, intermediate code provides ample opportunit...
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Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of ...
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Video captioning is a complex process, involving the automatic generation of descriptive, natural language narratives for video content. The current landscape of video captioning models primarily emphasizes crafting c...
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Knowledge graphs play a pivotal role in information retrieval, yet their effectiveness can be further optimized. This paper introduces a comprehensive approach to enriching knowledge graphs, addressing contemporary ch...
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This research explores the application of AI-driven techniques for automated anomaly detection in ultrasound scans during pregnancy. Leveraging a substantial dataset of labeled ultrasound images, this study delves int...
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Numerous bankruptcy prediction models have been proposed and tested. Among them, the influential Altman Z-Score stands out as a powerful tool for assessing a business's financial health and gauging its vulnerabili...
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Supply chain management has a lot of logistical challenges, which can significantly affect the efficacy and efficiency of logistics operations as a whole. Machine learning techniques are increasingly popular because t...
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The Retrieval Augmented Generation (RAG) framework utilizes a combination of parametric knowledge and external knowledge to demonstrate state-of-the-art performance on open-domain question answering tasks. However, th...
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Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes,but model complexity often poses challenges to the practical implementation and ...
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Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes,but model complexity often poses challenges to the practical implementation and efficiency of these *** paper presents an innovative method for optimizing simulation model parameters,focusing on achieving consistent contact area and the accurate prediction of the material removal function while preventing increase in model ***,controllable and uncontrollable factors in bonnet simulations are analyzed,and then a simplified contact model is developed and applied under constant force *** characterize the bonnet's contact performance,a contact area response curve is introduced,which can be obtained through a series of single spot contact ***,a rubber hyperelastic parameter optimization model based on a neural network is proposed to achieve optimal matching of the contact area between simulation and *** average deviation of the contact area under different conditions was reduced from 22.78%before optimization to 3.43%after optimization,preliminarily proving the effectiveness of the proposed simulation optimization ***,orthogonal experiments are further conducted to validate the proposed *** comparison between the experimental and predicted material removal functions reveals a high consistency,validating the accuracy and effectiveness of the proposed optimization method based on consistent contact *** research provides valuable insights into enhancing the reliability and effectiveness of bonnet polishing simulations with a simple and practical approach while mitigating the complexity of the model.
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