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A scalable parallel Chinese online encyclopedia knowledge denoising method based on entry tags and Spark cluster

APPLIED INTELLIGENCE 2021年第10期51卷 7573-7599页

作者： Wang, Ting Li, Jie Guo, Jiale Capital Univ Econ & Business Sch Management & Engn Beijing 100070 Peoples R China Beijing Jiaotong Univ Sch Software Beijing 100093 Peoples R China

Because of the open-collaborative of online encyclopedias, a large number of knowledge triples are improperly classified in online encyclopedia systems, and it is necessary to denoise and refine the open-domain encyclopedia Knowledge Bases (KBs) to improve the quality and precision. However, the lack and inaccuracy of triple semantic features lead to a poor refinement effect. In addition, considering large-scale encyclopedia KBs, the processing of massive knowledge will lead to too much computing time and poor scalability of the algorithm. To solve the problems of knowledge denoising in the Chinese encyclopedia system, first, based on data field theory, this paper proposes a new Cartesian product mapping-based method (TripleES) to calculate the semantic similarity of entity triples, based on which a method for quantifying the quality of entry tags is proposed. Second, to further improve the denoising effect on KBs, this paper proposes a new method (TriplePV) to compute the potential value of triple based on multi-feature fusion strategy to calculate the semantic distance between the "out-of-vocabulary" entry tags and embeds it into the potential function. Third, to ensure our algorithms have good scalability, the proposed denoising algorithms are implemented and optimized in parallel based on the Spark cluster-computing framework. Specifically, Spark-based TripleES (ES_Spark) and Spark-based TriplePV (PV_Spark) algorithms are proposed to calculate the semantic similarity and potential value of triples respectively. Finally, a comprehensive comparative analysis is performed on the denoising effect and time efficiency with the state-of-the-art distributed Chinese encyclopedia knowledge denoising algorithm. The experimental results on real-world datasets show that the parallel denoising algorithm proposed in this paper can improve the efficiency of knowledge denoising and the accuracy of KBs, which outperforms the state-of-the-art methods.

关键词： Knowledge denoising Chinese online encyclopedia Knowledge base Semantic distance Potential function parallel computing

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Modeling and Control of Delayed, Nonlinear, Uncertain, and Disturbed Air Heater Employing Fuzzy PDC-L1 Adaptive Scheme

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第11期68卷 11328-11338页

作者： Maiti, Roshni Das Sharma, Kaushik Sarkar, Gautam Chandra, Saumyak Univ Calcutta Dept Appl Phys Kolkata 700009 India

In this article, we proposed a scheme of modeling an air heater system with a long airflow duct, predominantly available in many industrial plants to maintain the desired temperature by simultaneously befitting the transport delay, nonlinearities, model uncertainties, and nonuniform disturbances. At first, the nonlinear delayed air heater system is formulated by combining linear fuzzy system models utilizing fuzzy parallel distributed compensation (PDC) logic. Then, a newly developed L-1 adaptive control strategy is modified by replacing its conventional state feedback controller with fuzzy PDC control logic to maintain the desired temperature by nullifying nonlinearities, transport delays, uncertainties, and disturbances. The novelty of the proposed work lies in the fact that the proposed fuzzy PDC law augmented L-1 adaptive scheme explicitly deals with the delays and nonlinearities by means of fuzzy PDC logic as well as eliminates time-varying uncertainties and disturbances utilizing L-1 adaptive control strategy. The results obtained from real-life laboratory-scale experiments suitably demonstrate the usefulness of the proposed approach.

关键词： Heating systems Atmospheric modeling Delays Heat transfer Uncertainty Adaptation models Ducts Air heater fuzzy modeling fuzzy parallel distributed compensation (PDC) controller L-1 adaptive controller model uncertainties nonlinearities nonuniform disturbances transport delay

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PA-SPS: A predictive adaptive approach for an elastic stream processing system

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JOURNAL OF parallel AND distributed COMPUTING 2024年 192卷

作者： Wladdimiro, Daniel Arantes, Luciana Sens, Pierre Hidalgo, Nicolas Sorbonne Univ CNRS LIP6 4 Pl Jussieu F-75005 Paris France Univ Diego Portales Ave Manuel Rodriguez 415 Santiago Chile

Stream Processing systems (SPSs) dynamically process input events. Since the input is usually not a constant flow, presenting rate fluctuations, many works in the literature propose to dynamically replicate SPS operators, aiming at reducing the processing bottleneck induced by such fluctuations. However, these SPSs do not consider the problem of load balancing of the replicas or the cost involved in reconfiguring the system whenever the number of replicas changes. We present in this paper a predictive model which, based on input rate variation, execution time of operators, and queued events, dynamically defines the necessary current number of replicas of each operator. A predictor, composed of different models (i.e., mathematical and Machine Learning ones), predicts the input rate. We also propose a Storm -based SPS, named PA-SPS, which uses such a predictive model, not requiring reboot reconfiguration when the number of operators replica change. PA-SPS also implements a load balancer that distributes incoming events evenly among replicas of an operator. We have conducted experiments on Google Cloud Platform (GCP) for evaluation PA-SPS using real traffic traces of different applications and also compared it with Storm and other existing SPSs.

关键词： Big data distributed stream processing Self-adaptive system Dynamic traffic Predictive algorithm Google cloud platform

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A parallel Filtering-Communication-Based Cardinality Consensus Approach for real-time distributed PHD Filtering

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IEEE SENSORS JOURNAL 2020年第22期20卷 13824-13832页

作者： Li, Tiancheng Mallick, Mahendra Pan, Quan Northwestern Polytech Univ Key Lab Informat Fus Technol Minist Educ Sch Automat Xian 710072 Peoples R China

This paper proposes a new cardinality consensus (CC) approach called "prior-CC" to distributed probability hypothesis density (PHD) filtering based on a decentralized sensor network. In our approach, network-wide average consensus is sought with respect to the prior cardinality estimate. Unlike existing serial filtering-communication approaches, the prior-CC scheme allows the internode communication to be performed in parallel with the local filter calculation and requires only a small amount of interfacing fusion calculation and communication. This enables real time filtering that minimizes data delay and is of great significance in realistic tracking systems. We provide details of the Gaussian mixture implementation of the proposed prior-CC-PHD filter based on a diffuse target birth model and analyze the filtering-communication parallelization. In addition, we evaluate the gain of the prior-CC scheme with respect to the filtering accuracy in comparison with the standard CC scheme via simulations using a stationary linear sensor network and a dynamic nonlinear sensor network, respectively.

关键词： Sensors Pediatrics Filtering Target tracking real-time systems Delays distributed tracking multisensor fusion average consensus Gaussian mixture probability hypothesis density

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A Runtime Manager Integrated Emulation Environment for Heterogeneous SoC Design with RISC-V Cores

A Runtime Manager Integrated Emulation Environment for Heter...

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IEEE International Symposium on parallel and distributed Processing workshops and Phd Forum (IPDPSW)

作者： H. Umut Suluhan Serhan Gener Alexander Fusco Joshua Mack Ismet Dagli Mehmet Belviranli Cagatay Edemen Ali Akoglu Electrical and Computer Engineering Department The University of Arizona Tucson AZ USA Computer Science Department Colorado School of Mines Golden CO USA Electrical and Electronics Engineering Department Ozyegin University Istanbul Turkey

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

RISC-V based architectures have gained attention for deployment on platforms from SoC to HPC scale to meet the demands of a wide range of computational workloads that may require specialized hardware tailored to their specific performance goals. In previous works, we introduced CEDR, an open-source, unified compilation and runtime framework designed to facilitate productive hardware-agnostic application deployment on heterogeneous architectures, and lift the barriers to research on the design and evaluation of scheduling heuristics and accelerators for heterogeneous systems. In this study, we demonstrate the capability of CEDR in supporting heterogeneous systems composed of RISC-V cores and a pool of accelerators. We investigate the impact of employing customized lightweight RISC-V cores for managing accelerators individually. Such management approach, compared to centralized accelerator management model, scales down the stress on the runtime environment where compute resources are shared among multiple applications each with varying degrees of task-level parallelism. We perform evaluations on a heterogeneous SoC emulated on the Xilinx Virtex-7 FPGA based on execution of applications from radar, signal processing, and autonomous vehicles domains. We observe that releasing CPU-based compute cores from accelerator management through lightweight RISC-V cores results with up to 47% reduction in time spent on accelerator management. Since CPU-based compute cores exclusively become available for application task execution and contention on resource management is reduced, we observe speedup of up to 2.2x in application execution time based on experiments conducted with workloads consisting of dynamically arriving real-life application scenarios.

关键词： Runtime environment Processor scheduling Computer architecture parallel processing Radar signal processing Hardware Resource management

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Decentralized parallel Blockchain Agricultural Product Traceability System Security Analysis

Decentralized Parallel Blockchain Agricultural Product Trace...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sun Fenglin Guangxi Normal University Guilin City China

This research work presents a decentralized parallel blockchain-based agricultural product traceability system, which aims to enhance information security and data model efficiency, and achieve real-time tracking of agricultural products. Through an in-depth analysis of decentralized blockchain applications, the system adopts distributed ledger processing for low-cost transactions. The Digital Promise technology ensures the credibility of the data transmission and privacy protection. The proposed decentralized parallel blockchain structure shows significant traceability results, ensuring transparent traceability of agricultural products. Experimental results demonstrate the effectiveness of the system, improving food safety and quality management. The decentralized nature reduces the risk of data tampering, thereby enhancing public trust. Experimental results show short query response times, efficient data transfers, and zero failures, highlighting the system's excellent performance in handling traceability queries. These results underscore the remarkable advantages of blockchain technology in increasing the efficiency and reliability of traceability systems.

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Ghost: A General Framework for High-Performance Online Similarity Queries over distributed Trajectory Streams

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Proceedings of the ACM on Management of Data 2023年第2期1卷 1-25页

作者： Ziquan Fang Shenghao Gong Lu Chen Jiachen Xu Yunjun Gao Christian S. Jensen Zhejiang University Hangzhou China Aalborg University Aalborg Denmark

Trajectory similarity queries, including similarity search and similarity join, offer a foundation for many geo-spatial applications. With the rapid increase of streaming trajectory data volumes, e.g., data from mobile phones, vessel monitoring, or traffic systems, many location-based services benefit from online similarity analytics over trajectory data streams, where moving objects continually emit real-time position data. However, most existing studies focus on offline settings, and thus several major challenges remain unanswered in an online setting. To this end, we describe Ghost, a distributed stream processing framework that enables generic, efficient, and scalable online trajectory similarity search and *** propose a novel incremental online similarity computation (IOSC) mechanism to accelerate pair-wise streaming trajectory distance calculation, which supports a broad range of trajectory distance metrics. Compared with previous studies, IOSC reduces the complexity from quadratic to linear in terms of trajectory length. Building on this foundation, we propose histogram-based algorithms that exploit histogram indexes and a series of pruning bounds to enable streaming trajectory similarity search and join. Finally, we extend our methods to the distributed platform Flink for scalability, where a CostPartitioner is developed to ensure parallel processing and workload balancing. An experimental study using two real-life and one synthetic datasets shows that Ghost (i) acquires 6-20× efficiency/throughput gains and one order of magnitude memory overhead savings over state-of-the-art baselines, (ii) achieves 3--8× workload balancing gains on Flink, and (iii) exhibits low parameter sensitivity and high robustness.

关键词： distributed processing flink similarity metrics trajectory streams

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Efficient, Reliable, and Scalable distributed Data Processing Based on Hadoop

Efficient, Reliable, and Scalable Distributed Data Processin...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Yingwei Liang Taipeng Zhu Chenghui Liu Yue Chen Yufei Yang CSG Guangdong Power Grid Co. Ltd. Information Center Guangzhou Guangdong China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With the rapid growth of information technology and the intelligent upgrading of terminal devices, the amount of data in modern society is showing an explosive growth trend. These data are not only large-scale, but also structurally diverse. This data characteristic poses significant challenges to traditional data processing and storage methods. Although traditional distributed database and data warehouse technologies can to some extent cope with large-scale data processing, efficiency and flexibility are still limited when dealing with unstructured and massive data. In this context, the emergence of Hadoop and Map Reduce technologies has provided new solutions for large-scale data processing. Hadoop has become one of the mainstream technologies for handling big data due to its simple programming model, good scalability, and powerful fault tolerance. This article designs a distributed data processing system based on Hadoop, which fully utilizes the efficiency, high reliability, and high scalability of Hadoop, aiming to meet the needs of large-scale data processing. The experimental results show that the system in this paper has demonstrated excellent performance and stability in practical applications, and can process massive data, providing an effective solution for large-scale data processing.

关键词： Fault tolerance Scalability Fault tolerant systems distributed databases Programming parallel processing Data processing Reliability engineering Stability analysis real-time systems

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Nona: A Stochastic Congestion-Aware Job Scheduler for real-time Inference Queries

Nona: A Stochastic Congestion-Aware Job Scheduler for Real-T...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Benoit Pit-Claudel Derya Malak Alejandro Cohen Muriel Medard Manya Ghobadi MIT Cambridge USA EURECOM Sophia Antipolis France Technion Haifa Israel

ISBN: (数字)9798350376562

ISBN: (纸本)9798350376579

This paper proposes a novel queueing-theoretic approach to enable stochastic congestion-aware scheduling for distributed machine learning inference queries. Our proposed framework, called Nona, combines a stochastic scheduler with an offline optimization formulation rooted in queueing-theoretic principles to minimize the average completion time of heterogeneous inference queries. At its core, Nona incorporates the fundamental tradeoffs between compute and network resources to make efficient scheduling decisions. Nona's formulation uses the Pollaczek-Khinchine formula to estimate queueing latency and to predict system congestion. Builind upon conventional Jackson networks, it captures the dependency between the computation and communication operations of interfering jobs. From this formulation, we derive an optimization problem and use its results as inputs for the scheduler. We introduce a novel graph contraction procedure to enable cloud providers to solve Nona's optimization formulation in practical settings. We evaluate Nona with real-world machine learning models (AlexNet, ResNet, DenseNet, VGG, and GPT2) and demonstrate that Nona outperforms state-of-the-art schedulers by up to 350×.

关键词： Processor scheduling Computational modeling Memory management Stochastic processes Machine learning parallel processing real-time systems Probability distribution Optimization

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Cache-aware scheduling of scientific workflows in a multisite cloud

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 122卷 172-186页

作者： Heidsieck, Gaetan de Oliveira, Daniel Pacitti, Esther Pradal, Christophe Tardieu, Francois Valduriez, Patrick Univ Montpellier LIRMM CNRS INRIA Montpellier France Fluminense Fed Univ Comp Sci Niteroi RJ Brazil CIRAD UMR AGAP Inst F-34398 Montpellier France Univ Montpellier Inst Agro INRAE LEPSE Montpellier France

Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resources available at multiple geo-distributed data centers. Since it is common for workflow users to reuse code or data from previous workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. However, caching intermediate data and scheduling workflows to exploit such caching in a multisite cloud is complex. In particular, workflow scheduling must be cache-aware, in order to decide whether reusing cache data or re-executing workflows entirely. In this paper, we propose a solution for cache-aware scheduling of scientific workflows in a multisite cloud. Our solution includes a distributed and parallel architecture and new algorithms for adaptive caching, cache site selection, and dynamic workflow scheduling. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation in a three-site cloud with a real application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of the same input data for each new execution. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multisite cloud distributed caching Scientific workflow Workflow system Workflow scheduling

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