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EdgeHydra: Fault-Tolerant Edge data distribution Based on Erasure Coding

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第1期36卷 29-42页

作者： He, Qiang Zhang, Guobiao Wang, Jiawei Luo, Ruikun Dai, Xiaohai Hu, Yuchong Chen, Feifei Jin, Hai Yang, Yun Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp L Wuhan 430074 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia Huazhong Univ Sci & Technol Sch Comp Sci & Technol Key Lab Informat Storage Syst Minist Educ China Wuhan 430074 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3125 Australia

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

关键词： Servers Delays Costs Encoding Runtime Fluctuations Fault tolerant systems Fault tolerance Steiner trees Edge computing Edge data caching erasure coding data distribution distributed transmission

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A data distribution-Based Ensemble Generation Applied to Wind Speed Forecasting 34th

A Data Distribution-Based Ensemble Generation Applied to Win...

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34th Brazilian Conference on Intelligent Systems

作者： Almeida, Diogo M. de Mattos Neto, Paulo S. G. Cunha, Daniel C. Univ Fed Pernambuco UFPE Ctr Informat CIn Recife PE Brazil

ISBN: (纸本)9783031790287;9783031790294

Variability in wind intensity and direction causes unstable electricity supply to the power system, making integrating this energy into the electrical system a significant challenge for operations and planning practices. Ensembles can be used as an alternative to address the complex patterns over time in wind speed time series. The appropriate size of the training partition for ensemble models depends on dataset characteristics. And, to enhance model training efficiency, it is important to minimize repetitive data. By maintaining a concise training set, it becomes easier to meet the requirements of software and hardware constraints. Mainly because, there is a growing interest in deploying machine learning models on edge devices. For these reasons, a new method called Local distribution (LocDist) has been introduced in this paper to predict wind speed, utilizing local pattern recognition based on data distribution. In testing with three wind speed time series, LocDist created a compact training subset with less than 20% of the training data. The Diebold-Mariano hypothesis test was employed to assess the significance of the forecast errors of the proposal compared to individual and bagging methods that use the entire training set. The LocDist method with long shortterm memory (LSTM) and gated recurrent unit (GRU) won in 100% of the cases. Additionally, the LocDist with extreme learning machines (ELM) and autoregressive integrated moving average (ARIMA) won or tied in 83% and 66% of the cases, respectively.

关键词： Time series Forecasting Ensembles data distribution Wind speed

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Positive discrimination of minority classes through data generation and distribution: A case study in olive disease classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： El Akhal, Hicham Ben Yahya, Aissa El Alaoui, Abdelbaki El Belrhiti Moulay Ismail Univ Meknes Fac Sci Comp Networks & Syst Lab PB 11201 Meknes 50000 Morocco

Deep learning models have achieved remarkable success in various tasks, especially in classification. This success is particularly evident in the precise classification of plant diseases, which is crucial for effective agricultural management. However, accurate classification faces challenges, particularly in data collection, where certain classes are underrepresented, namely the minority classes. This issue can significantly impact model performance. To tackle this challenge, this paper introduces a novel methodology that differs from existing approaches. We focus on addressing the issue of minority classes in image-based classification tasks, particularly for olive diseases. We employ data generation methods, including basic transformations, to produce augmented data and utilize Deep Convolutional Generative Adversarial Networks (DCGAN) to produce generated data. Next, we apply the Frechet Inception Distance (FID) to the generated dataset to select the highest-quality images. We then distribute varying percentages (25%, 50%, 75%, 100%) of this new data into the minority classes of the original dataset. Our data distribution strategies involve incorporating specific amounts of (1) augmented data, (2) generated data, and (3) a combination of both augmented and generated data to achieve target percentages (T.P) in the resulting dataset. Our experiments focus on classifying olive diseases into seven distinct categories using a pre-trained Convolutional Neural Network (CNN) architecture. We observe significant improvements in the model's performance, particularly in the accurate classification of minority classes. This approach enhances diagnostic accuracy and optimizes data distribution, which is crucial for effectively addressing the challenges posed by minority classes.

关键词： Minority class data generation techniques Deep learning model data distribution Olive diseases classification

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data distribution-Based Curriculum Learning

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IEEE ACCESS 2024年 12卷 138429-138440页

作者： Chaudhry, Shonal Sharma, Anuraganand Univ South Pacific Sch Informat Technol Engn Math & Phys Laucala Campus Suva Fiji

The order of training samples can have a significant impact on a model's performance. Curriculum learning is an approach for gradually training a model by ordering samples from 'easy' to 'hard'. This paper proposes the novel idea of a curriculum learning strategy called data distribution-based Curriculum Learning (DDCL). DDCL uses the inherent data distribution of a dataset to build a curriculum based on the order of samples. Our proposed approach is innovative as it incorporates two distinct scoring methods known as DDCL-Density and DDCL-Point to determine the order of training samples. The DDCL-Density method assigns scores based on the density of samples favoring denser regions that can make initial learning easier. Conversely, DDCL-Point utilizes the Euclidean distance from the centroid of the dataset as a reference point to score samples providing an alternative perspective on sample difficulty. We evaluate the proposed DDCL approach by conducting experiments across various classifiers using a diverse set of small to medium-sized medical datasets. Results show that DDCL improves the classification accuracy, achieving increases ranging from 2% to 10% compared to baseline methods and other state-of-the-art techniques. Moreover, analysis of the error losses for a single training epoch reveals that DDCL not only improves accuracy but also increases the convergence rate, underlining its potential for more efficient training. The findings suggest that DDCL can specifically be of benefit to medical applications where data is often limited and indicate promising directions for future research in domains that involve limited datasets.

关键词： Training data Transfer learning Random forests Visualization Robots Legged locomotion Classification algorithms Curriculum development Machine learning Support vector machines Classification curriculum learning data distribution machine learning neural network random forest support vector machine

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data distribution for dense factorization on computers with memory heterogeneity

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PARALLEL COMPUTING 2007年第12期33卷 757-779页

作者： Lastovetsky, Alexey Reddy, Ravi UCD Sch Comp Sci & Informat Dublin 4 Ireland

In this paper, we study the problem of optimal matrix partitioning for parallel dense factorization on heterogeneous processors. First, we outline existing algorithms solving the problem that use a constant performance model of processors, when the relative speed of each processor is represented by a positive constant. We also propose a new efficient algorithm, called the Reverse algorithm, solving the problem with the constant performance model. We extend the presented algorithms to the functional performance model, representing the speed of a processor by a continuous function of the task size. The model, in particular, takes account of memory heterogeneity and paging effects resulting in significant variations of relative speeds of the processors with the increase of the task size. We experimentally demonstrate that the functional extension of the Reverse algorithm outperforms functional extensions of traditional algorithms. (c) 2007 Elsevier B.V. All rights reserved.

关键词： heterogeneous systems scheduling and task partitioning load balancing and task assignment data distribution parallel algorithms LU factorization

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data distribution for Heterogeneous Storage Systems

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IEEE TRANSACTIONS ON COMPUTERS 2023年第6期72卷 1747-1762页

作者： Zhou, Jiang Chen, Yong Zheng, Mai Wang, Weiping Chinese Acad Sci Inst Informat Engn Beijing 100864 Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

The exponential growth of data in many science and engineering domains poses significant challenges to storage systems. data distribution is a critical component in large-scale distributed storage systems and plays a vital role in placing petabytes of data and beyond, among tens to hundreds of thousands of storage devices. Meantime, heterogeneous storage systems, such as those having devices with hard disk drives (HDDs) and storage class memories (SCMs), have become increasingly popular for massive data storage due to their distinct and complement characteristics. This paper presents a new data distribution algorithm called SUORA (Scalable and Uniform storage via Optimally-adaptive and Random number Addressing) specifically for heterogeneous devices to maximize the benefits of them. SUORA provides a fully symmetric, highly efficient methodology to distribute data across a hybrid and tiered storage cluster. It divides heterogeneous devices into different buckets and segments, and adopts pseudo-random functions to map data onto them with the balanced consideration of capacity, performance and life-time. By analyzing hotness and access patterns, SUORA gradually moves hot data from HDDs to SCMs to optimize the throughput, and moves cold data reversely for load balance. It combines data replication with migration to significantly reduce movement overhead while making data placement more adaptive to different workloads. Extensive evaluations on simulation and Sheepdog storage system show that, with considering distinct characteristics of various devices thoroughly, SUORA improves the overall performance efficiency of heterogeneous storage systems.

关键词： Performance evaluation Clustering algorithms Nonvolatile memory Costs Throughput Hash functions Servers Parallel/distributed file systems data distribution data placement heterogeneous storage data replication

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data distribution BY SATELLITE

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JOURNAL OF THE INSTITUTION OF ELECTRONIC AND RADIO ENGINEERS 1988年第3期58卷 109-116页

作者： GARRETT, P FIRMAN, D HODSON, K Communication Systems Research Ltd. Ilkley UK

The current environment of satellite communications and the opportunities for new satellite based services are described. data distribution is one such service which can be offered on a flexible basis with call-by-call and demand assignment. The paper outlines the main features of a data distribution system known as Pandata, which combines the use of spread spectrum modulation with a multiplex and calling unit.

关键词： Satellite communication systems satellite relay systems satellite communications data communication systems demand assignment Pandata multiplex spread spectrum modulation Other data transmission data distribution calling unit call-by-call

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data distribution for Phylogenetic Inference with Site Repeats via Judicious Hypergraph Partitioning 33

Data Distribution for Phylogenetic Inference with Site Repea...

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33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Baar, Ivo Huebner, Lukas Oettig, Peter Zapletal, Adrian Schlag, Sebastian Stamatakis, Alexandros Morel, Benoit Karlsruhe Inst Technol Inst Theoret Informat Karlsruhe Germany Heidelberg Inst Theoret Studies Computat Mol Evolut Grp Heidelberg Germany

ISBN: (纸本)9781538655559

The so-called site repeats (SR) technique can be used to accelerate the widely-used phylogenetic likelihood function (PLF) by identifying identical patterns among multiple sequence alignment (MSA) sites, thereby omitting redundant calculations and saving memory. However, this complicates the optimal data distribution of MSA sites in parallel likelihood calculations, as the cost of computing the likelihood for individual sites strongly depends on the sites-to-cores assignment. We show that finding a 'good' sites-to-cores assignment can be modeled as a hypergraph partitioning problem, more specifically, a specific instance of the so-called judicious hypergraph partitioning problem. We initially develop, parallelize, and make available HyperPhylo, an efficient open-source implementation for this flavor of judicious partitioning where all vertices have the same degree. Using empirical MSA data, we then show that sites-to-core assignments computed via HyperPhylo are substantially better than those obtained via a previous naive approach for phylogenetic data distribution under SRs.

关键词： phylogenetic inference data distribution parallel efficiency judicious hypergraph partitioning

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data distribution and Competition Law Issues

Data Distribution and Competition Law Issues

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9th IEEE International Conference on Big data (IEEE Bigdata)

作者： Arai, Koki Kyoritsu Womens Univ Fac Business Studies Tokyo Japan

ISBN: (纸本)9781665439022

This study summarizes the issues in the promotion of data distribution, how huge digital platformers conduct in the market, and the actual regulations and initiatives for them. In particular, it briefly examines the actual status of litigation under US antitrust law and the issues. The study discusses the following five points: First, it highlights the delineation of the relevant market, which is also the first entry point in competition analysis. Second, it undertakes the existence of monopoly power, which requires the analysis of dominant firms. Third, it analyzes the conduct of forming, maintaining, and strengthening market power. Fourth, it analyzes the consumer damages. Fifth, it discusses whether antitrust actions are needed to solve the problems and remedies.

关键词： antitrust law competition law data distribution data market

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data distribution: normal or abnormal? What to do about it

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TRANSFUSION 2013年第4期53卷 701-702页

作者： Kandane-Rathnayake, R. K. Enticott, J. C. Phillips, L. E. Monash Univ Transfus Res Unit Dept Epidemiol & Prevent Med Sch Publ Hlth & Prevent Med Melbourne Vic 3004 Australia Monash Univ Southern Acad Primary Care Res Unit Melbourne Vic 3004 Australia

The article discusses the options for addressing normal or abnormal data distribution. One option is the use of statistical tests not dependent on assumptions associated to data distribution including Spearman's rank correlation coefficient, Kaplan-Meier estimation, and McNemar's test. The transformation of numerical variable into another scale for assumption satisfaction is considered as the alternative to these tests.

关键词： data distribution QUANTITATIVE research CORRELATION (Statistics) STATISTICS KAPLAN-Meier estimator TRANSFORMATIONS (Mathematics)

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