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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149974 条记录，以下是711-720 订阅

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DCScaler: Spatiotemporal Prediction Aided distributed Collaborative Autoscaling of Microservices 10

DCScaler: Spatiotemporal Prediction Aided Distributed Collab...

引用

ieee 10th international conference on Edge computing and Scalable Cloud (ieee EdgeCom) / 11th ieee international conference on Cyber Security and Cloud computing (ieee CSCloud)

作者： Li, Jiangming Li, Sen Tan, Jian Jin, Dong Chen, Shuangwu Yang, Jian Univ Sci & Technol China Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Guangxi Key Lab Digital Infrastruct Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350377149;9798350377132

The autoscaling function dynamically adjusts the resource configuration of microservice applications in response to workload changes, thereby ensuring service quality. However, designing an effective scaling strategy for each service remains challenging due to the heterogeneity of services. To address this challenge, we introduce DCScaler, a distributed collaborative scaler that leverages spatiotemporal predictions to optimize Service Level Agreement (SLA) guarantees and resource utilization by proactive resource allocation adjustments. DCScaler adopts (i) a spatiotemporal graph attention network to learn the spatiotemporal dependencies among service metrics;(ii) a multi-agent Deep Reinforcement Learning (DRL) based scaler to learn the optimal scaling strategy tailored to each service. DC-Scaler accurately predicts future workloads and adjusts resource allocation accordingly for each service. Experimental results obtained in a real microservice environment demonstrate that DCScaler effectively enhances resource utilization and reduces SLA violations.

关键词： Microservices Autoscaling Spatiotemporal Prediction Deep Reinforcement Learning

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Research on distributed Database Synchronization Mechanism in Edge computing Environment

Research on Distributed Database Synchronization Mechanism i...

引用

2024 international conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Wang, Chunxin Jiang, Yuankun Liu, Yuan Zhang, Wei Wang, Zenghai Liu, Liguo Zhao, Zilin Cloud Data Technology Co. Ltd. China State Grid Information & Telecommunication Co. Ltd. Beijing Branch Beijing100031 China

ISBN: (纸本)9798331511296

Challenges like network latency, bandwidth limitations, and varied node resources are encountered by distributed databases in edge computing environments. This paper examines a distributed database synchronization mechanism for edge computing to enhance synchronization efficiency and maintain data consistency. It suggests an optimized design involving the weak consistency model and adaptive synchronization frequency adjustment. By building mathematical models and conducting performance tests, the performance of the synchronization mechanism under different network conditions was evaluated, and the advantages of the adaptive synchronization mechanism in key indicators such as synchronization delay, data consistency, and bandwidth utilization were verified. Experimental results show that this mechanism can significantly reduce synchronization delays, improve bandwidth utilization, and maintain low consistency errors under dynamic network conditions, and is especially suitable for edge computing nodes with limited resources. In addition, through practical application cases of intelligent traffic management systems, the effectiveness and application value of the optimization mechanism are further proved. The research in this article provides theoretical support and technical solutions for distributed database synchronization in edge computing scenarios, which helps to improve the data management efficiency and reliability of the system. © 2024 ieee.

关键词： Data consistency

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Knowledge Extraction from distributed Heterogeneous Data Sources 5

Knowledge Extraction from Distributed Heterogeneous Data Sou...

引用

5th ieee international conference for Emerging Technology, INCET 2024

作者： Ramachandran, Raji Abhijith, K. Karthik, J.J. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361155

The growing volume and complexity of unstructured and semi-structured data pose a significant challenge in extracting meaningful and relevant information. Information Extraction (IE) emerges as a powerful technique to transform unstructured data into structured format, facilitating further analysis and knowledge discovery. IE techniques can effectively extract information from unstructured data in the form of entities, relationships, objects, and events. Our objective is to develop a method for extracting or filtering out the most relevant information from data sources which are heterogeneous in nature as well as they are distributed over many systems and hence build a unified schema based on the collective knowledge extracted from the data sources. Several existing methods have been developed to extract knowledge from heterogeneous datasets. We focuses on datasets in the medical field that are distributed over multiple systems. Specifically, we aim to extract valuable information from patient records, including patient details, medical conditions and medications consumed. The unified schema can be utilized to study a new patient's condition and assist doctors in making a pre-examination of a patient by querying it. © 2024 ieee.

关键词： Data mining

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DQFed: A Federated Learning Strategy for Non-IID Data based on a Quality-Driven Perspective

DQFed: A Federated Learning Strategy for Non-IID Data based ...

引用

ieee international conference on Fuzzy systems (FUZZ-ieee)

作者： Bernardi, Mario Luca Cimitile, Marta Usman, Muhammad Univ Sannio Dept Engn Benevento Italy UnitelmaSapienza Univ Dept Law & Digital Soc Rome Italy

ISBN: (纸本)9798350319552;9798350319545

The increasing amount of available smart objects produces a huge amount of data that can be successfully managed using Federated Learning (FL) approaches. FL is a distributed deep learning framework where several devices are trained with a local model on local data, and a central server aggregates them in a global model. These strategies are successfully used in several contexts ensuring privacy preservation and high effectiveness in data analysis. However, the FL strategies show variable accuracy in case of a huge presence of non-independent-and-identically-distributed (Non-IID) data. This challenge has been largely explored in the last years by researchers and developers who propose new FL strategies for non-IID data. This study introduces a novel FL approach, called DQFed, that aggregates the local models on the base of their weights computed according to a quality-driven model. The surrounding idea is that the performance of the general model can be improved by aggregating the clients' models giving higher importance to the clients that use high-quality data. The DQFed strategy is evaluated on five datasets (both Non-IID and IID datasets are considered) obtained starting from a real dataset. The results show an improved F1-score compared to the considered baseline for both Non-IID and IID data.

关键词： federated learning Non-IID data quality-driven federated learning distributed learning

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The Impact of Low-Entropy on Chunking Techniques for Data Deduplication 17

The Impact of Low-Entropy on Chunking Techniques for Data De...

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ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Al Jarah, Mumen Udayashankar, Sreeharsha Baba, Abdelrahman Al-Kiswany, Samer Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350368543;9798350368536

While numerous Content-Defined Chunking (CDC) algorithms exist for data deduplication, their relative performance has not been analyzed in the presence of low-entropy induced byte-shifting. This paper explores and evaluates hash-based and hashless CDC algorithms in the presence of low-entropy data regions, using synthetic datasets. Our evaluation shows that modern CDC algorithms are poor at handling low-entropy blocks when the block sizes are small and that their low-entropy detection ability depends upon the expected average chunk size. Contrary to previous studies focusing on conventional byte-shifting, hash-based algorithms achieve poor space savings compared to their hashless counterparts when low-entropy induced byte-shifting is involved. This can be explained by the greater variability in chunk sizes and the higher percentage of artificial boundaries they exhibit in the presence of these regions. All of these factors together highlight the need for specialized CDC algorithms to detect and eliminate low-entropy data blocks during the deduplication process.

关键词： Data Deduplication Content-Defined Chunking Low-Entropy Storage systems Cloud computing

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A Strategy for Implementing Gaussian distributed Chaotic Signals Based on the Lorenz System 9

A Strategy for Implementing Gaussian Distributed Chaotic Sig...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Yang, Zaixue Liu, Bin Chen, Yanming School of Electrical Engineering Guangxi University Guangxi Zhuang Autonomous Region Nanning China

ISBN: (纸本)9798350376548

Gaussian distributed random signal has broad prospects in various applications. Random signal generators make it easy to generate Gaussian distributed random signals, greatly promoting the widespread application of Gaussian distributed random signals. Research has shown that chaotic signals not only possess randomness but also other characteristics that random signals do not have. In some application scenarios, chaotic signals are superior to random signals. However, chaotic signals are typically not in accordance with Gaussian distribution due to the invariant distribution of chaotic systems. Therefore, this paper proposes a strategy for implementing Gaussian-type chaotic signals based on the Lorenz system to address the acquisition problem of Gaussian distributed chaotic signals. Firstly, the Lorenz chaotic system is selected as the intrinsic signal source, and a non-parametric estimation algorithm is used to solve its probability density function and probability distribution function. Then, a transformation algorithm is proposed to transform the intrinsic signal into Gaussian distributed chaotic signals. Finally, the effectiveness of the proposed algorithm is verified by implementing Gaussian distributed chaotic signals with different parameters. © 2024 ieee.

关键词： Probability density function

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ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments 17

ArcaDB: A Disaggregated Query Engine for Heterogenous Comput...

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ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Ruiz-Rohena, Kristalys Rodriguez-Martinez, Manuel Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00680 USA Univ Puerto Rico Dept Comp Sci & Engn Mayaguez PR USA

ISBN: (纸本)9798350368543;9798350368536

Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related to their operations. Nowadays, clusters and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale with the data processing demands in many applications related to social media, bioinformatics, surveillance systems, remote sensing, and medical informatics. Given this new scenario, the architecture of data analytics engines must evolve to take advantage of these new technological trends. In this paper, we present ArcaDB: a disaggregated query engine that leverages container technology to place operators at compute nodes that fit their performance profile. In ArcaDB, a query plan is dispatched to worker nodes that have different computing characteristics. Each operator is annotated with the preferred type of compute node for execution, and ArcaDB ensures that the operator gets picked up by the appropriate workers. We have implemented a prototype version of ArcaDB using Java, Python, and Docker containers. We have also completed a preliminary performance study of this prototype, using images and scientific data. This study shows that ArcaDB can speed up query performance by a factor of 3.5x in comparison with a shared-nothing, symmetric arrangement.

关键词： cloud computing data analytics query engine DBaaS cloud accelerators

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In-Situ Model Validation for Continuous Processes Using In-Network computing 7

In-Situ Model Validation for Continuous Processes Using In-N...

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ieee 7th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Kunze, Ike Scheurenberg, Dominik Tirpitz, Liam Geisler, Sandra Wehrle, Klaus Rhein Westfal TH Aachen Commun & Distributed Syst Aachen Germany Rhein Westfal TH Aachen Inst Automat Control Aachen Germany Rhein Westfal TH Aachen Data Stream Management & Anal Aachen Germany

ISBN: (纸本)9798350363029;9798350363012

The advancing industrial digitalization enables evolved process control schemes that rely on accurate models learned through data-driven approaches. While they provide high control performance and are robust to smaller deviations, a larger change in process behavior can pose significant challenges, in the worst case even leading to a damaged process plant. Hence, it is important to frequently assess the fit between the model and the actual process behavior. As the number of controlled processes and associated data volumes increase, the need for lightweight and fast reacting assessment solutions also increases. In this paper, we propose CIVIC, an in-network computing-based solution for Continuous In-situ Validation of Industrial Control models. In short, CIVIC monitors relevant process variables and detects different process states through comparison with a priori knowledge about the desired process behavior. This detection can then be leveraged to, e.g., shut down the process or trigger a reconfiguration. We prototype CIVIC on an Intel Tofino-based switch and apply it to a lab-scale water treatment plant. Our results show that we can achieve a high detection accuracy, proving that such monitoring systems are feasible and sensible.

关键词： Plant shutdowns

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TurboFed: A Federated Learning Approach to The PHM of distributed Wind Turbines 10

TurboFed: A Federated Learning Approach to The PHM of Distri...

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10th ieee international conference on High Performance and Smart computing (ieee HPSC)

作者： Chen, Bo Zhu, Yongxin Guo, Yu Xu, Shiyuan Chinese Acad Sci Shanghai Adv Res Inst Shanghai Peoples R China Shanghai Nucl Engn Res & Design Inst Co LTD Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350389463;9798350389470

This paper proposes one approach with federated learning technique to address practical challenges faced by the emerging green energy industries, i.e., wind turbines in terms of Predictive Health Management (PHM). Not as many federated learning applications being used in the scenarios only for simulation, the application of federated learning in this paper is focused on the real industrial problems with raw data collected from the fields. Huge amount of real data was collected by sensors on more than ten wind turbines across different areas in China and transmitted to the storage for in-time processing. The framework proposed in this paper called TurboFed, can handle the raw data and achieves good prediction performance in the practical wind generated power systems. The framework showed its help on improving the efficiency of the wind turbines. The paper has brought three novel results. First, as far as known, the framework here is the first federated learning framework addressing position adjustment of wind turbines in the real environment. Second, it deploys customized recurrent neural computing models to the wind turbines which are considered the client devices under the federated learning paradigm. Finally, it incorporates new customized aggregation algorithms on the sever side.

关键词： Customized computing Edge computing Federated Learning Machine Learning Green Energy Wind Energy

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distributed Data Storage Architecture Model and Data Platform Design for Power Grid Dispatching 3

Distributed Data Storage Architecture Model and Data Platfor...

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3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Xu, Yuan Su, Yinsheng Zhao, Ligang Zhou, Baorong Tang, Yingqi Huang, Guanbiao Hu, Jiaming Li, Kaihang CSG Elect Power Res Inst State Key Lab HVDC Technol Guangzhou Peoples R China CSG Power Dispatching & Control Ctr Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the power and computer science fields struggle to meet practical production requirements effectively. This paper constructs a distributed data storage architecture model for power grid dispatching, defining the elements and their relationships within the architecture. Additionally, it proposes methods for managing massive source data and distributed heterogeneous database clusters. Based on these findings, a power grid dispatching business data platform is designed. Test results indicate that the proposed architecture effectively supports the efficient execution of power grid dispatching business, providing a specialized data platform design paradigm for the power industry.

关键词： data architecture model distributed heterogeneous database cluster power grid dispatching data platform design metadata

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