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检索条件"任意字段=Proceedings of the First International Conference on Parallel and Distributed Information Systems"

共 6615 条记录，以下是131-140 订阅

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Domestic Intranet Cloud Design for Connecting District Health information systems for Capacity Development of Healthcare in Developing Countries 1

Domestic Intranet Cloud Design for Connecting District Healt...

引用

1st international conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Qureshi, Noman Sohaib Midhun Chakkaravarthy, Divya Jamal, Nasir Lincoln University College Malaysia Petaling Jaya Malaysia

ISBN: (纸本)9798350348637

The capacity development for healthcare (CDH) requires strengthening and diversification of national health information systems (HIS). It will facilitate achieving the United Nation's (UN) sustainable development goal-3 (SDG-3). This paper presents the design for a distributed communication services programming interface (DCSPI) that is intended for use at district level for forming an intranet of HIS with the feature for patient scheduling and management in addition to other features including collaboration between health professionals. In the first phase of this research, the DCSPI individual components are designed as a real-time system using Finite State Machines (FSMs) and formal testing for distributed reactive system is performed. The refinements are done in the design as per testing results. In next phase, after formal testing the DCSPI will be programmed and deployed for testing at district level in Pakistan for CDH. The outcome of this research will be useful primarily for developing countries for capacity development and enhancement in healthcare. Also, it will contribute to achieving targets of UN's SDG-3 globally. © 2024 IEEE.

关键词： Electronic health record

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A Latency Guaranteed Scheduling Strategy under Performance Constraints in Big Data Stream Computing Environments 29

A Latency Guaranteed Scheduling Strategy under Performance C...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Sun, Dawei Guan, Chengjun Fan, Yinuo Rung, Jia Gao, Shang China University of Geosciences School of Information Engineering Beijing100083 China Monash University Department OfData Science and Al ClaytonVIC3800 Australia DeakinUniversity School of Information Technology Waum PondsVIC3216 Australia

ISBN: (纸本)9798350330717

Efficient utilization of computing resources in a stream computing environment is crucial for system performance. Existing scheduling strategies can hardly guarantee latency under performance constraints, let alone accounting for the communication cost incurred by scheduling itself. To address these issues, we propose Lg-Stream, a latency guaranteed scheduling strategy under performance constraints. This paper discusses the Lg-Stream strategy from the following aspects: (1) We model the topology as a queuing network to evaluate the system latency;(2) For scenarios with limited resources and latency constraints, we ensure that each executor-to-component allocation optimizes the system's processing latency to the maximum, consequently altering the components' parallelism;(3) We place executors that communicate with each other on the same node as much as possible. Experimental results demonstrated that in comparison to existing state-of-the-art scheduling strategies, it reduces the average system latency up to 30%. © 2023 IEEE.

关键词： Big data parallelism Resource scheduling Storm Stream processing

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Latency-Optimized Multi-User Task Offloading Scheme Using Dynamic Priority and Duplication in Edge Computing 29

Latency-Optimized Multi-User Task Offloading Scheme Using Dy...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Peng, Bo Chen, Cheng Li, Qiang Peng, Shilin Lei, Xiang Southwest University of Science and Technology School of Information Engineering Mianyang China

ISBN: (纸本)9798350330717

The extensive development of complex applications in embedded devices has driven the rapid development of edge computing, which provides powerful processing capabilities to the edge network. In this context of development, task offloading has received widespread attention as one of the key issues in edge computing. However, since a task usually consists of multiple subtasks with dependencies, the current computing subtask on the local or edge server must wait for the completion of the previous dependent subtask. As a result, existing offloading schemes are often constrained by the complexity of the task topology. Therefore, this paper is aimed at tasks with dependencies described as Directed Acyclic Graphs (DAGs) in devices. first, we optimize the order of subtasks with dynamic priorities. Meanwhile, we employ task duplication to reduce communication latency and thus overall task completion time. Additionally, to support the edge computing environment of multi-user and multi-server, game theory is used to find the optimal offload position for each user, so as to obtain the minimum average computing delay of all tasks. Experimental results show that the proposed algorithm outperforms existing algorithms in terms of task completion delay. © 2023 IEEE.

关键词： directed acyclic graphs edge computing task offloading

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parallel and distributed Machine Learning Techniques for Anomaly Detection systems 10

Parallel and Distributed Machine Learning Techniques for Ano...

引用

10th international Scientific conference "information Technology and Implementation", IT and I 2023

作者： Khlevna, Iulia Koval, Bohdan Taras Shevchenko National University of Kyiv Volodymyrska str. 60 Kyiv01033 Ukraine

Efficient processing of extensive datasets is crucial in data-driven applications, particularly for anomaly detection. This article explores the application of parallel and distributed machine learning techniques to enhance anomaly detection systems. parallel training, including model and data parallelism, significantly reduces processing times in data preprocessing, feature engineering, and model training. Experiments demonstrate notable efficiency improvements, especially for extensive datasets. distributed training is crucial in scenarios with time-intensive training, storage constraints, data localization, or RAM limitations. The approach to parallelize learning process has been demonstrated, which allowed to speed up the processing time by 2x via allocating 4x resources. Also, it depicts how the process can be spread across multiple machines using distributed techniques. In conclusion, this article emphasizes the pivotal role of parallel and distributed machine learning techniques in accelerating the development of anomaly detection systems. These techniques empower organizations to address challenges posed by extensive datasets and real-time data streams effectively. As data-driven applications evolve, these advanced computing methods promise more effective and timely responses to anomalies, solidifying their significance in the era of big data. © 2023 Copyright for this paper by its authors.

关键词： Machine learning

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Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework 29

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Jie Song, Jian Li, Xiang Tian, Xuesen Zhao, Zhigang Wu, Lu Wang, Chunxiao Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model's performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers. © 2023 IEEE.

关键词： Contrastive Learning HPC Power Consumption Prediction

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Position Determination from CSI Measurements of Multiple UEs under Unaware Environment 16

Position Determination from CSI Measurements of Multiple UEs...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Wang, Chenchen Ma, Yubin Yu, Fangrui Wen, Hao Liu, Weiyu Wan, Qun Nation Key Laboratory on Blind Signal Processing School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

In urban environment, communication signals encompass non-line-of-sight (NLOS) paths that contain environmental information. This paper investigates utilizing NLOS component signal to estimate the environment parameters, and uses these environmental parameters to achieve blind UEs (User Equipment) positioning. Specifically, this paper first analyzes the relationship between environmental parameters and the time of arrival (TOA) of NLOS signals. Second, an algorithm is proposed that utilizes the TOA of one-order NLOS signals in uplink signals between distributed UEs and base stations (BS) to estimate environmental parameters via maximum likelihood estimation. Subsequently, leveraging the obtained environmental parameters, achieving the positioning of blind UEs. Finally, numerical simulations validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Reflection Non line of sight propagation Mirrors Reconstruction algorithms Environmental sensing Mirror surfaces Point clouds LIDAR Monte Carlo methods Numerical simulations

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Addressing Client Heterogeneity in Synchronous Federated Learning: The CHAFL Approach 29

Addressing Client Heterogeneity in Synchronous Federated Lea...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Yu, Miri Kwon, Oh-Kyoung Oh, Sangyoon Ajou University Suwon Korea Republic of Korea Institute of Science and Technology Information Daejeon Korea Republic of

ISBN: (纸本)9798350330717

Federated learning (FL) is proposed to address the security vulnerabilities of conventional distributed deep learning. Since the capabilities of participating FL clients are highly variable in terms of both statistical and system aspects, FL training will face diminished convergence accuracy and speed. Hence, we propose CHAFL (client heterogeneity aware federated learning) to address client heterogeneity (i.e., statistical and system heterogeneity) in synchronous FL. CHAFL selects clients based on global loss with contribution, defined as local loss, enabling higher round-to-accuracy for the global model than previous studies. Additionally, to handle system heterogeneity, it proposes a lightweight algorithm that eliminates the profiling process previously employed to calculate adaptive local epochs in existing studies, thereby improving time-to-accuracy. In order to verify the effectiveness of CHAFL, we exploit three benchmark datasets on non-IID and system heterogeneous setting for empirical evaluation. Compared to the baseline, CHAFL achieves an accuracy improvement of 3.1 to 5.7%, along with 1.04 to 1.73x higher round-to-accuracy and 1.03 to 1.98x higher time-to-accuracy. © 2023 IEEE.

关键词： adaptive local epoch client selection federated learning systems and statistical heterogeneity

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OSDP: Optimal Sharded Data parallel for distributed Deep Learning 32

OSDP: Optimal Sharded Data Parallel for Distributed Deep Lea...

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32nd international Joint conference on Artificial Intelligence (IJCAI)

作者： Jiang, Youhe Fu, Fangcheng Miao, Xupeng Nie, Xiaonan Cui, Bin Peking Univ Sch CS Beijing Peoples R China Peking Univ Key Lab High Confidence Software Technol MOE Beijing Peoples R China Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA USA Peking Univ Qingdao Inst Computat Social Sci Qingdao Peoples R China

ISBN: (纸本)9781956792034

Large-scale deep learning models contribute to significant performance improvements on varieties of downstream tasks. Current data and model parallelism approaches utilize model replication and partition techniques to support the distributed training of ultra-large models. However, directly deploying these systems often leads to sub-optimal training efficiency due to the complex model architectures and the strict device memory constraints. In this paper, we propose Optimal Sharded Data parallel (OSDP), an automated parallel training system that combines the advantages from both data and model parallelism. Given the model description and the device information, OSDP makes trade-offs between the memory consumption and the hardware utilization, thus automatically generates the distributed computation graph and maximizes the overall system throughput. In addition, OSDP introduces operator splitting to further alleviate peak memory footprints during training with negligible overheads, which enables the trainability of larger models as well as the higher throughput. Extensive experimental results of OSDP on multiple different kinds of large-scale models demonstrate that the proposed strategy outperforms the state-of-the-art in multiple regards.

关键词： Personnel training

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2nd international conference on Emerging Trends in information Technology and Engineering, ic-ETITE 2024

2nd International Conference on Emerging Trends in Informati...

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2nd international conference on Emerging Trends in information Technology and Engineering, ic-ETITE 2024

ISBN: (纸本)9798350328202

The proceedings contain 306 papers. The topics discussed include: IoT in agriculture 4.0: farmer adoption, barriers, and the path to sustainable farming;an impact of evasion attacks on machine learning algorithms;parallel convolutional neural network for brain tumor using MR images;voice-based common cold detection;review on IOMT security through distributed machine learning;performance analysis for diving sport using YoLoV8, OpenPose and fuzzy logic;reliability analysis of PMSM drives processor for commercial electric vehicle utility;H-Blockenc: a pioneering blockchain integrated cryptographic security model for IoT-healthcare systems;early detection of down syndrome through ultrasound imaging using deep learning strategies — a review;route optimization using clustering and K Opt optimization for garbage collection;and preventing network attacks using support vector machine (SVM) and software defined networking (SDN) integration.

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FedDyn: A dynamic and efficient federated distillation approach on Recommender System 28

FedDyn: A dynamic and efficient federated distillation appro...

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IEEE 28th international conference on parallel and distributed systems (IEEE ICPADS)

作者： Jin, Cheng Chen, Xuandong Gu, Yi Li, Qun Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665473156

Federated Learning (FL) is a popular distributed machine learning paradigm that enables devices to work together to train a centralized model without transmitting raw data. However, when the model becomes complex, mobile devices' communication overhead can be unacceptably large in traditional FL methods. To address this problem, Federated Distillation (FD) is proposed as a federated version of knowledge distillation. Most of the recent FD methods calculate the model output (logits) of each client as the local knowledge on a public proxy dataset and do distillation with the average of the clients' logits on the server side. Nevertheless, these FD methods are not robust and perform poorly in the non-IID (data is non-independent and non-identically distributed) scenario such as Federated Recommendation (FR). In order to eliminate the non-IID problem and apply FD in FR, we proposed a novel method named FedDyn to construct a proxy dataset and extract local knowledge dynamically in this paper. In this method, we replaced the average strategy with focus distillation to strengthen reliable knowledge, which solved the non-IID problem that the local model has biased knowledge. The average strategy is a dilution and perturbation of knowledge since it treats reliable and unreliable knowledge equally important. In addition, to prevent inference of private user information from local knowledge, we used a method like local differential privacy techniques to protect this knowledge on the client side. The experimental results showed that our method has a faster convergence speed and lower communication overhead than the baselines on three datasets, including MovieLens-100K, MovieLens-1M and Pinterest.

关键词： distributed Machine Learning Federated Learning Recommender System Knowledge Distillation

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