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检索条件"任意字段=International Symposium on Software Engineering for Parallel and Distributed Systems"

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Assessing the Performance of Docker in Docker Containers for Microservice-Based Architectures

Assessing the Performance of Docker in Docker Containers for...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Felipe Bedinotto Fava Luiz Felipe Laviola Leite Luís Fernando Alves Da Silva Pedro Ramires Da Silva Amalfi Costa Angelo Gaspar Diniz Nogueira Amanda Fagundes Gobus Lopes Claudio Schepke Diego Luis Kreutz Rodrigo Brãndao Mansilha Graduate Software Engineering (PPGES) - Laboratory of Advances Studies in Computation (LEA) Federal University of Pampa (UNIPAMPA) Alegrete Brazil

We provide a comprehensive and updated assessment of Docker versus Docker in Docker (DinD), evaluating its impact on CPU, memory, disk, and network. Using different workloads, we evaluate DinD's performance across distinct hardware platforms and GNU/Linux distributions on cloud Infrastructure as a Service (laaS) platforms like Google Compute Engine (GCE) and traditional server-based environments. We developed an automated tools suite to achieve our goal. We execute four well-known benchmarks on Docker and its nested-container variant. Our findings indicate that nested-containers require up to 7 seconds for startup, while the Docker standard containers require less than 0.5 seconds for Debian and Alpine operating systems. Our results suggest that Docker containers based on Debian consistently outperform their Alpine counter-parts, showing lower CPU latency. A key distinction among these Docker images lies in the varying number of installed libraries (e.g., stretching from 13 to 119) across different Linux distributions for the same system (e.g., MySQL). Furthermore, the number of events and CPU latency indicates that the influence of DinD over Docker proves that it is insignificant for both operating systems. In terms of memory, running containers of Debian-based images consume 20% more size of memory than those based on Alpine. No significant differences are between nested-containers and Dockers for disk and network IO. It is worth emphasizing that some of the disparities, such as a bigger memory footprint, appear to be a direct result of the software stack in use, including different kernel versions. libraries. and other essential packages.

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Feedback from a data center for education at CentraleSupélec engineering school

Feedback from a data center for education at CentraleSupéle...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Jé ré my Fix Stephane Vialle Ré mi Hellequin Claudine Mercier Patrick Mercier Jean-Baptiste Tavernier LORIA CNRS Universit&#x00E9 de Lorraine CentraleSup&#x00E9 lec Gif-sur-Yvette France LISN CNRS Universit&#x00E9 Paris-Saclay CentraleSup&#x00E9 Information systems department CentraleSup&#x00E9

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

This paper describes the hardware and software architecture of the Data Center for Education (DCE) built up at CentraleSupélec as well as our educational objectives. Students experiment in the context of their studies with clusters of CPU and servers of GPU. They have different technical backgrounds from beginners to experts in computer science. The lectures have also different experimental requirements: from single host long running jobs for machine learning to multiple hosts short running HPC algorithms benchmarks, with lab works and projects that need to share the same pool of machines.

关键词： Data centers distributed processing Machine learning algorithms software architecture Education Graphics processing units Machine learning

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FaultyRank: A Graph-based parallel File System Checker

FaultyRank: A Graph-based Parallel File System Checker

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international symposium on parallel and distributed Processing (IPDPS)

作者： Saisha Kamat Abdullah Al Raqibul Islam Mai Zheng Dong Dai Computer Science Department University of North Carolina at Charlotte Department of Electrical and Computer Engineering Iowa State University

Similar to local file system checkers such as e2fsck for Ext4, a parallel file system (PFS) checker ensures the file system's correctness. The basic idea of file system checkers is straightforward: important metadata are stored redundantly in separate places for cross-checking; inconsistent metadata will be repaired or overwritten by its ‘more correct' counterpart, which is defined by the developers. Unfortunately, implementing the idea for PFSes is non-trivial due to the system complexity. Although many popular parallel file systems already contain dedicated checkers (e.g., LFSCK for Lustre, BeeGFS-FSCK for BeeGFS, mmfsck for GPFS), the existing checkers often cannot detect or repair inconsistencies accurately due to one fundamental limitation: they rely on a fixed set of consistency rules predefined by developers, which cannot cover the various failure scenarios that may occur in *** this study, we propose a new graph-based method to build PFS checkers. Specifically, we model important PFS metadata into graphs, then generalize the logic of cross-checking and repairing into graph analytic tasks. We design a new graph algorithm, FaultyRank, to quantitatively calculate the correctness of each metadata object. By leveraging the calculated correctness, we are able to recommend the most promising repairs to users. Based on the idea, we implement a prototype of FaultyRank on Lustre, one of the most widely used parallel file systems, and compare it with Lustre's default file system checker LFSCK. Our experiments show that FaultyRank can achieve the same checking and repairing logic as LFSCK. Moreover, it is capable of detecting and repairing complicated PFS consistency issues that LFSCK can not handle. We also show the performance advantage of FaultyRank compared with LFSCK. Through this study, we believe FaultyRank opens a new opportunity for building PFS checkers effectively and efficiently.

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance Computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms Computer architecture Machine learning Matrix decomposition

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Simplified distributed Secondary Control Based on Diffusion Algorithm in Isolated AC Microgrids

Simplified Distributed Secondary Control Based on Diffusion ...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Yawen Ding Fei Gao Jiahao Yu Qipeng Zheng Key Laboratory of Control of Power Transmission and Conversion Ministry of Education College of Smart Energy Shanghai Jiao Tong University Shanghai China Dept. of Electrical Engineering Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Jiao Tong University Shanghai China

To achieve voltage and frequency recovery and accurate reactive power sharing in AC MGs, this paper proposes a simplified distributed secondary control strategy based on dynamic diffusion algorithm which contains only one integrator and one communicated variable in the voltage recovery and reactive power sharing regulator. Compared with traditional consensus-based secondary control method with parallel structure, the proposed control structure is simplified and the communication link burden is reduced. Furthermore, the controller's dynamic performance including convergence rate and convergence region of reactive power sharing and frequency recovery are also improved by adopting dynamic diffusion algorithm in secondary control. To verify the superiority of the proposed strategy, a three-node isolated AC MG is used to test the simplified distributed controller under load changes, communication delay comparing with consensus-based algorithm. Moreover, the proposed controller presents strong robustness under communication link failure and has plug-and-play capability.

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Improving Confidence of Uncertain Knowledge Graphs by Crowdsourcing with Limited Budget

Improving Confidence of Uncertain Knowledge Graphs by Crowds...

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international Conference on parallel and distributed systems (ICPADS)

作者： Haodi Zhang Wenxi Huang Chenyu Xu Zhengbang Yang Hongxin Zhou Fengtian Qi Chen Zhang Kaishun Wu School of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University

Knowledge graphs (KGs), either constructed automatically from texts or collected manually from crowdsourcing workers, may contain uncertainty. The uncertainty may propagate into the knowledge graph embedding and downstream tasks, which is potentially harmful, especially for those confidencesensitive applications such as medical diagnostic suggestion. Crowdsourcing workers with domain knowledge can help improve the data quality of knowledge graphs, by knowledge checking. However, due to the large scale of knowledge graphs and the limitation of adequate crowdsourcing workers, it is unrealistic to check all triplets in a knowledge graph to improve the data quality. Therefore, in this paper, we propose a crowdsourcing framework that efficiently improves the confidence of knowledge graphs with limited budget. We instantiate the framework in the medical domain and conduct a series of experiments with realworld medical data. We deploy the framework for knowledge graph embedding UKGE and corresponding downstream tasks. The experimental results show that the proposed method efficiently improves the quality of the knowledge graphs, and hence improves the performance of probabilistic knowledge graph embedding in the downstream tasks.

关键词： Crowdsourcing Uncertainty Data integrity distributed databases Knowledge graphs Probabilistic logic Medical diagnosis

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Evaluation of SVM Kernels with Multiple Uncorrelated Feature Subsets Selected by Multiple Correlation Methods for Reflection Amplification DDoS Attacks Detection 9th

Evaluation of SVM Kernels with Multiple Uncorrelated Feature...

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9th international symposium on Applied Computing for software and Smart systems, ACSS 2022

作者： Dasari, Kishore Babu Devarakonda, Nagaraju Department of CSE Acharya Nagarjuna University Andhra Pradesh 522510 India School of Computer Science and Engineering VIT-AP University Amaravati522237 India

ISBN: (纸本)9789811967900

Data availability is one of the primary principles of information security. distributed Denial of Service (DDoS) is a typical cyber security attack of the DOS family to deny data availability to legitimate users by flooding the system resources with huge amounts of network traffic. This study is a quantitative research to evaluate SVM kernels for Reflection Amplification DDoS attack detection with different uncorrelated feature subsets. Support Vector Machine (SVM) classification algorithms Linear, RBF, Poly, and Sigmoid kernel functions are used in this study. This research proposed three methods to select uncorrelated subsets for SVM to classify DDoS attacks. This study performs experiments on Reflection Amplification DDoS attacks of SNMP and DNS datasets. The first proposal, selects uncorrelated feature subsets by Pearson, Spearman, and Kendall correlation methods. In the second proposal, common uncorrelated feature subsets of three correlation methods are called PSK-uncorrelated feature subsets. The third proposal, common PSK-uncorrelated feature subsets of SNMP and DNS datasets are called Reflection_DDoS uncorrelated feature subsets. SNMP and DNS data sets are collected from the CIC-DDoS2019 evaluation datasets. Experiment results conclude that Poly and RBF kernels among the SVM kernel functions produce the best classification results and among uncorrelated feature subsets, Pearson uncorrelated feature subset produces the best results in three un-correlated feature subsets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge Computing

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shuaibing Lu Xin Jin Jie Wu Shuyang Zhou Shen Wu Jackson Yang Ran Yan College of Computer Science Beijing University of Technology Beijing China Center for Networked Computing Temple University USA School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.

关键词： Costs Multi-access edge computing Q-learning Heuristic algorithms Memory management Quality of service Dynamic scheduling Dynamic programming Servers Optimization

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zhi Zhang Shiwen Zhang Shuang Chen Wei Liang Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology and Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients’ potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning.

关键词： Training Privacy Data privacy Accuracy Federated learning Data integrity Predictive models Data models Long short term memory Convergence

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Proceedings - 2021 international symposium on Theoretical Aspects of software engineering, TASE 2021

Proceedings - 2021 International Symposium on Theoretical As...

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15th international symposium on Theoretical Aspects of software engineering, TASE 2021

ISBN: (纸本)9781665441636

The proceedings contain 27 papers. The topics discussed include: RE2B: enhancing correctness of both requirements and design models;eliciting timing requirements for cyber-physical systems: a multiform time based approach;research and practice of container system;parametric spatio-temporal modeling and safety verifying for T2T-CBTC systems;a timed automata based automatic framework for verifying STL properties of Simulink models;dynamic verification of C11 concurrency over multi copy atomics;featured games;and a parallel implementation of liveness on knowledge graphs under label constraints.

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