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

共 30762 条记录，以下是561-570 订阅

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distributed Charging Scheduling and Pricing Strategy for Plug-in Electric Vehicles Based on Stackelberg-Nash and Multi-Cluster Aggregative Games

Distributed Charging Scheduling and Pricing Strategy for Plu...

引用

2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Jing, Yuhao Chen, Jianguo Qiao, Li Mo, Huadong Dong, Daoyi School of Engineering and Technology University of New South Wales Canberra Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China School of Systems and Computing University of New South Wales Canberra Australia School of Engineering Australian National University Canberra Australia

ISBN: (纸本)9781665410205

In this paper, we propose a distributed and interactive Plug-in Electric Vehicle (PEV) charging scheduling approach, which is also combined with an optimal pricing strategy. This method tackles challenges such as fluctuations in charging currents, potential supply congestion, and uneven demand distribution that arise as PEV penetration increases. The objective is to improve the robust stability of the charging system while also reducing the costs for PEV users. This study designs a multi-cluster aggregative game mechanism to handle the competitive dynamics among operational clusters and the collective behavior of individual PEVs. Additionally, a strategic pricing method, based on Stackelberg game theory, is designed to refine the determination of basic electricity prices. We further introduce a distributed update method that efficiently seeks the Nash Equilibrium (NE) of the hierarchical game described. The effectiveness of the proposed architecture and solution methodology is validated through experimental studies. © 2024 ieee.

关键词： State of charge

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Diffusing High-level SLO in Microservice Pipelines 18

Diffusing High-level SLO in Microservice Pipelines

引用

18th ieee international conference on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Sedlak, Boris Pujol, Victor Casamayor Donta, Praveen Kumar Dustdar, Schahram Vienna Univ Technol TU Wien Distributed Syst Grp A-1040 Vienna Austria

ISBN: (纸本)9798331539580

Complex interactions within microservice architectures obfuscate the implications of individual services to high-level requirements. This becomes even more grave for multi-tenant and multi-vendor scenarios, like Edge computing, where different stakeholders might specify opposing Service Level Objectives (SLOs), e.g., minimizing both energy consumption and response time. To avoid contradictions within SLOs and to infer how SLOs can be fulfilled, this paper presents a methodology that diffuses high-level SLOs into multiple lower levels of SLOs and parameter assignments. Thus, it becomes clear how individual sub-processes contribute to high-level SLOs, and how these must be configured to foster their fulfillment. We evaluated our methodology for several microservice pipelines, where the challenge is to ensure multiple high-level SLOs (e.g., customer satisfaction) by finding and constraining all influential factors. The results show that by inferring multiple layers of lower-level constraints, we can fulfill high-level SLOs up to 100%. Notably, we could extract that the restrictiveness of low-level SLOs and the occurrence of conflicts have a severe impact on SLO fulfillment.

关键词： Service Level Objectives Microservices Intelligent systems Edge computing Requirements Assurance

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AoI-Aware Markov Decision Policies for Caching 42

AoI-Aware Markov Decision Policies for Caching

引用

42nd ieee international conference on distributed computing systems (ICDCS)

作者： Park, Soohyun Jung, Soyi Choi, Minseok Kim, Joongheon Korea Univ Seoul South Korea Hallym Univ Chunchon South Korea Kyung Hee Univ Seoul South Korea

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

We consider a scenario that utilizes road side units (RSUs) as distributed caches in connected vehicular networks. The goal of the use of caches in our scenario is for rapidly providing contents to connected vehicles under various traffic conditions. During this operation, due to the rapidly changed road environment and user mobility, the concept of age-of-information (AoI) is considered for (1) updating the cached information as well as (2) maintaining the freshness of cached information. The frequent updates of cached information maintain the freshness of the information at the expense of network resources. Here, the frequent updates increase the number of data transmissions between RSUs and MBS;and thus, it increases system costs, consequently. Therefore, the tradeoff exists between the AoI of cached information and the system costs. Based on this observation, the proposed algorithm in this paper aims at the system cost reduction which is fundamentally required for content delivery while minimizing the content AoI, based on Markov Decision Process (MDP) and Lyapunov optimization.

关键词： Costs Connected vehicles Roads Road side unit Markov processes Data communication distributed computing

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Self-Optimizing distributed Cloud computing with Dynamic Neural Resource Allocation and Fault-Tolerant Multi-Agent systems 4

Self-Optimizing Distributed Cloud Computing with Dynamic Neu...

引用

4th international conference on Ubiquitous computing and Intelligent Information systems, ICUIS 2024

作者： Tripura, N. Divya, P. Chaganti, Koushik Reddy Rao, Kota Venkateswara Rajyalakshmi, P. Naresh, P. Vignan Institute of Technology And Science Dept of Mba Hyderabad India University of the Cumberlands Dept. of Computer and Information Systems KY United States Andhra Loyola Institute of Engineering and Technology Dept of Cse Vijayawada India Tkr College of Engineering and Technology Dept of Cse Hyderabad India Dayananda Sagar University Dept of Cse Bangalore India

ISBN: (纸本)9798331529635

The exponential growth of distributed cloud systems necessitates an intelligent form of workload management that ensures optimum performance, scalability, and reliability. Traditional approaches based on static or heuristic resource allocation and fault-tolerance mechanisms cannot capture the dynamic nature of modern workloads and heterogeneous infrastructures. Drawing from this source of inspiration, this work develops a holistic framework integrating machine learning models that facilitate self-optimizing distributed cloud computing for dynamic workload management. Three components are significant in the proposed framework: (1) DNRA;this uses a hybrid GNN and RL architecture for real-time resource allocation, offering up to 25% improvement in resource utilization and a 30% reduction in task latency. (2) Workload-Aware Transfer Learning Scheduler (WATLS), leveraging transfer learning to predict workload dynamics across heterogeneous nodes, achieving 20% higher task throughput and 15% energy savings through proactive scheduling. (3) Multi-Agent Predictive Fault-Tolerant System (MAP-FTS), combining multi-agent systems with predictive fault detection to preemptively redistribute tasks and schedule maintenance, reducing system downtime by 50% and enhancing fault detection accuracy to 90%. The framework integrates novel methodologies towards answering the main challenges of resource allocation, workload prediction, and fault tolerance. The amount of progress achieved through cumulative contributions includes improvement in system reliability and scalability up to 5x their nodes and respective scalable energy efficiency without any degradation of performance. This work paves the pathway for an adaptive and intelligent next generation of distributed cloud systems. © 2024 ieee.

关键词： distributed cloud

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Visualizing Kubernetes distributed systems: an exploratory study

Visualizing Kubernetes Distributed Systems: an exploratory s...

引用

ieee Working conference on Software Visualization (VISSOFT)

作者： Balreira, Dennis Giovani Araujo, Thiago da Silva Petrillo, Fabio Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Ecole Technol Super Dept Genie Logiciel & TI Montreal PQ Canada

ISBN: (纸本)9798350308297

distributed applications running on virtualizationbased systems and cloud computing have become popular solutions, allowing developers to focus on application logic rather than dealing with the complexities of distributed systems. However, these applications often become increasingly complex, presenting multiple management challenges. To address this issue, software visualization approaches offer valuable solutions by providing real-time insights into resources and their functionalities, offering a comprehensive overview. This study aims to analyze and evaluate existing software visualization tools for distributed applications on the Kubernetes platform. The objective is to comprehensively examine these tools' features, capabilities, and limitations to understand their effectiveness in visualizing complex distributed systems. Our findings provide valuable insights into the strengths and weaknesses of the available visualization tools, enabling researchers and practitioners to make informed decisions and advancements in software visualization for distributed applications on the Kubernetes platform. Our research identified eight Kubernetes visualization tools, which were examined and compared based on relevant characteristics related to distributed applications and software visualization standards. However, it is worth noting that despite the excellent work done by the community in establishing these first proposals, these tools currently only support, on average, a visualization of 9% of the total resource types available, as mentioned in the official documentation. Therefore, we propose guidelines followed by a synthesized visualization that can guide further research and development in this area. Our study will assist users in selecting the most suitable Kubernetes visualization tool and encourage researchers and the community to explore new approaches in Kubernetes visualization.

关键词： software visualization exploratory study distributed systems kubernetes

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Unleashing Performance Insights with Online Probabilistic Tracing 12

Unleashing Performance Insights with Online Probabilistic Tr...

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12th international conference on Cloud Engineering

作者： Toslali, M. Qasim, S. Parthasarathy, S. Oliveira, F. A. Huang, H. Stringhini, G. Liu, Z. Coskun, A. K. Boston Univ Boston MA 02215 USA IBM Res Yorktown Hts NY USA Univ Maryland College Pk MD USA

ISBN: (纸本)9798331528690;9798331528706

distributed tracing has become a fundamental tool for diagnosing performance issues in the cloud by recording causally ordered, end-to-end workflows of request executions. However, tracing workloads in production can introduce significant overheads due to the extensive instrumentation needed for identifying performance variations. This paper addresses the trade-off between the cost of tracing and the utility of the "spans" within that trace through Astraea, an online probabilistic distributed tracing system. Astraea is based on our technique that combines online Bayesian learning and multi-armed bandit frameworks. This formulation enables Astraea to effectively steer tracing towards the useful instrumentation needed for accurate performance diagnosis. Astraea localizes performance variations using only 20-35% of available instrumentation, markedly reducing tracing overhead, storage, compute costs, and trace analysis time.

关键词： distributed systems Performance Diagnosis Microservices Cloud computing online Bayesian learning

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ECdo: An Edge computing distributed Data-Driven Evolutionary Optimization Platform

ECdo: An Edge Computing Distributed Data-Driven Evolutionary...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Zeng, Qing-Ye Wei, Feng-Feng Guo, Xiao-Qi Chen, Wei-Neng School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

ISBN: (纸本)9798350337020

Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular method to solve data-driven optimization problems (DOPs), which are common in industry. However, with the development of the Internet of Things, data are collected, processed, and stored in a distributed manner, leading a new optimization paradigm for SAEAs. To make SAEAs adapt to these distributed DOPs, this paper employs the edge computing paradigm to develop a platform that provides technical support for SAEAs with distributed structures, named ECdo. Specifically, the platform utilizes KubeEdge, an open-source edge computing framework, to mount the cluster and combines microservice interface design with the containerization strategy to offer a flexible deployment approach for distributed SAEAs. In addition, an efficient and stable internal communication mechanism is designed for the interaction between distributed components within the platform. To demonstrate the application of ECdo, we take the examples of a class of distributed DOPs, in which the objective and constraints are expensive and need to be approximated by accumulated data. These problems are known as distributed and expensive constrained optimization problems (DECOPs). We implement a distributed SAEA on ECdo to address DECOPs in real-world scenarios. Experiments show that the ECdo can provide the expected implementation for distributed SAEAs with good network tolerance under tough network conditions. © 2023 ieee.

关键词： distributed optimization edge computing expensive optimization surrogate-assisted evolutionary algorithm

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Proceedings - 2024 international conference on Cyber-Enabled distributed computing and Knowledge Discover, CyberC 2024

Proceedings - 2024 International Conference on Cyber-Enabled...

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16th international conference on Cyber-Enabled distributed computing and Knowledge Discover, CyberC 2024

ISBN: (纸本)9798331506896

The proceedings contain 37 papers. The topics discussed include: few-shot image classification method with label consistent and inconsistent self-supervised learning;a high performance ai-powered cache mechanism for IoT devices;Q-MMT: Qinqiang Opera generation based on multi-track music transformer;research on human combination continuous motion recognition method;multispectral registration algorithm based on fusion of infrared and color images;improved detection of forged and generated facial images based on ResNet-50;intelligent spectrum management for UAV swarm with multiple combat missions: a DQN-based solution;multispectral registration algorithm based on fusion of infrared and color images;and OCDE: adaptive differential evolution algorithm in distributed intelligent systems.

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A Secure Information Management and Interoperability Framework: Enabling distributed Fusion Engines for Military IoT Applications

A Secure Information Management and Interoperability Framewo...

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2024 international conference on Military Communication and Information systems, ICMCIS 2024

作者： Penders, Ate Van Der Horst, Max Van Der Linden, Alex De Graaf, Maurits Quillinan, Thomas Pavlin, Gregor Thales Nederland B.V Delft Netherlands Thales Nederland B.V Huizen Netherlands

ISBN: (纸本)9798350373196

This paper presents a secure and flexible process integration approach enabling distributed data fusion in military IoT applications. It seamlessly combines two recently developed technologies, the Dynamic Process Integration Framework and Martello, a Data Centric Security approach. The emphasis is on secure composition of heterogeneous services, corresponding to different types of algorithms as well as data sources that can be distributed over a system of networked computing nodes. The approach supports a relevant class of decision support functions that distil actionable information in environments characterized through dynamic constellations of sensors and computing services, limited communication bandwidth and stringent security requirements. Such functions contribute key elements of Information Driven Operation that can significantly improve the speed, quality and coverage of the OODA loops in challenging settings, such as Federated Mission Networks. © 2024 ieee.

关键词： Information services

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PSeer: Performance Prediction for Partially Co-located distributed Deep Learning 23

PSeer: Performance Prediction for Partially Co-located Distr...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ding, Weijia Ding, Ziwei Zhao, Laiping Qiu, Tie College of Intelligence and Computing Tianjin University Tianjin Key Laboratory of Advanced Networking China

ISBN: (纸本)9781665494571

This paper studies the problem of predicting the deep learning (DL) jobs' training time which is the fundamental guide for training resources allocating and job scheduling. The existing prediction approaches, however, are greatly challenged for distributed deep learning (DDL) jobs due to the partial interference, namely, the resource contention that only occurs at partial time or locations of a job. We observe that the maximum difference in training time under different combinations of partial interference DDL can reach 2.2×. Thus partial interference is not a negligible factor for predicting the training time of DDL jobs. In this paper, we present PSeer, a partial interference-aware training time predictor for DDL workloads. It adopts a combined dual-submodel (the step number submodel and the step speed submodel), which is highly accurate in predicting by encoding the spatially and temporally varied partial interference into the model design. It is also lightweight: the step number submodel only requires the initial training loss value, and the step speed submodel can be constructed using the CPU and GPU resource utilization information of a few pre-trained steps. Empirical evaluation shows the prediction error generated by PSeer could achieve as low as 3.93% when employing RFR in the training speed submodel, and the prediction overhead is © 2021 ieee.

关键词： Training Deep learning Graphics processing units Interference Predictive models Encoding Resource management

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