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

共 149810 条记录，以下是401-410 订阅

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A distributed Lyapunov-Learning Scheme for Computation Offloading in Vehicular Networks 59

A Distributed Lyapunov-Learning Scheme for Computation Offlo...

引用

59th Annual ieee international conference on Communications (ieee ICC)

作者： Guo, Chi Wang, Cong Li, Juan Jilin Univ Coll Commun Engn Changchun Peoples R China

ISBN: (纸本)9798350304060;9798350304053

Mobile edge computing (MEC) facilitates user vehicles (UVs) to offload computation tasks by exploiting its collaborations with edge servers for meeting the stringent requirements. Previous works mainly explore computation offloading without queueing or ensuring queue stable under centralized control. However, they generally fall short of stability guarantee and distributed implementation. To tackle these issues, we formulate a stochastic computation offloading problem, which maximizes the average computation rate under queue stability constraints. To address it, we develop a distributed Lyapunov-learning scheme with federated learning (DLLS-FL), where Lyapunov optimization provides an online decomposition method and achieves performance tradeoff bounds under stability guarantee. Moreover, edge learning framework including actor-critic and federated learning enables distributed updating and convergence guarantee, respectively. Furthermore, DLLS-FL outperforms other centralized task offloading schemes in terms of data queueing and energy consumption.

关键词： Computation offloading

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distributed Dependent Task Offloading in CPU-GPU Heterogenous MEC: A Federated Reinforcement Learning Approach 22

Distributed Dependent Task Offloading in CPU-GPU Heterogenou...

引用

ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Huang, Hualong Duan, Zhekai Zhan, Wenhan Liu, Yichen Wang, Zhi Zhao, Zitian Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9798350381993;9798350382006

Mobile edge computing (MEC) has emerged as a promising paradigm to enable computation-intensive and latency-sensitive mobile applications by offloading tasks to proximal edge servers. This paper proposes a novel federated reinforcement learning framework called Transformer-based Federated Soft Actor-Critic (TFSAC) to address a joint computation offloading and resource scheduling problem in a CPU-GPU heterogeneous MEC network while preserving privacy. Specifically, a graph attention network (GAT) extracts high-dimensional features from the task dependency graph. Rather than simply averaging weights, TFSAC applies transformer encoders to learn contextual relationships between agents and enable selective aggregation of relevant knowledge during federated model training to preserve agents' privacy. Experiments on real-world trace data demonstrate TFSAC's superiority over benchmarks in maximizing quality-of-service (QoS) across configurations.

关键词： Mobile edge computing computation offloading federated reinforcement learning privacy

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Lightweight Operating System Services for Incremental Checkpointing in Speculative Discrete Event Simulation on Linux Platforms 30

Lightweight Operating System Services for Incremental Checkp...

引用

30th ieee international conference on Parallel and distributed systems, ICPADS 2024

作者： Montesano, Federica Marotta, Romolo Quaglia, Francesco Tor Vergata University of Rome Dicii Italy Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Italy

ISBN: (纸本)9798331515966

One way for supporting incremental checkpointing is the exploitation of classical memory protection services - in particular the mprotect (...) system call offered by Posix compliant operating systems - for intercepting memory-writes and identifying dirty pages in the address space. However, this solution involves Inter-Processor-Interrupt (IPI) and the associated handling mechanisms, which show costs that increase when scaling up the level of parallelism in the underlying hardware architecture. For HPC contexts like speculative (aka optimistic) parallel discrete event simulation, where checkpointing and state restore massively take place in order to maintain causality among the concurrent simulation objects, these costs may impact performance in a non-negligible manner. In this work, we present the design of operating system services that enable write-protection and the tracing of dirtied pages via per CPU setup of the MMU (Memory Management Unit), completely avoiding the usage of IPI and their management. Hence, we provide a solution where the cost for setting up the incremental checkpointing support of a simulation object processed by a specific thread at a given time is definitely limited, compared to the aforementioned classical case. Our design has been devised for Linux, although it can be ported to other operating systems, and has been integrated for its testing in the USE (Ultimate Share Everything) open source parallel simulation environment. © 2024 ieee.

关键词： Linux

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A BDI Agent-Based Asynchronous Scheduling Framework For Cloud computing

A BDI Agent-Based Asynchronous Scheduling Framework For Clou...

引用

2024 ieee international conference on Agents, ICA 2024

作者： Yang, Yikun Ren, Fenghui Zhang, Minjie Yan, Jun Xie, Fei Gao, Weiwei University of Wollongong Wollongong Australia Wenzhou Business College Wenzhou China

ISBN: (纸本)9798331539917

Task scheduling is a critical challenge in cloud computing, where uncertainties and distributed nature make traditional centralized methods inefficient. This paper presents a decentralized Belief-Desire-Intention agent-based scheduling framework for cloud computing, which excels in distributed environments and handling uncertainties. To avoid communication stuck caused by real-world uncertainties, the framework employs an asynchronous communication protocol with a notify listener. The framework addresses scheduling and rescheduling stages, considering uncertain events that may disrupt task executions and stuck agents. Two algorithms are proposed for scheduling and rescheduling processes respectively, alongside a novel cycle recommendation algorithm to minimize information synchronization issues. The framework has been implemented in JADEX and tested in cloud computing environments. Experimental results demonstrate that the framework minimizes task makespan, balances resource utilization, and maximizes task success rate in resolving uncertain events. © 2024 ieee.

关键词： Multi agent systems

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A Improved Consistent Hash Algorithm in Heterogeneous Edge computing 29

A Improved Consistent Hash Algorithm in Heterogeneous Edge C...

引用

29th ieee international conference on Parallel and distributed systems, ICPADS 2023

作者： Chai, Ruonan Gao, Shuai Hu, Zhenghao Beijing Jiaotong University School of Electronic Information Engineering Beijing China

ISBN: (纸本)9798350330717

Load balancing plays a critical role in large-scale heterogeneous edge computing, aiming to enhance the accuracy of computing resource matching and reduce processing delays caused by imbalanced resource allocation. In highly heterogeneous distributed computing environments, scalability and accuracy in matching computing resources are essential. The Consistent Hashing (CH) algorithm is well-known for its simplicity and strong scalability, enabling extensive and flexible scheduling strategies. It achieves load balancing by evenly distributing requests among different nodes. However, the CH algorithm primarily focuses on load balancing in storage resource scenarios and often neglects load balancing for computing resources, potentially leading to reduced accuracy in matching computing resources and prolonged processing delays. This paper introduces an improved CH algorithm called chordDC, specifically designed for distributed computing scenarios. It takes into account the unified representation of user tasks and computing resources, incorporating additional discriminative criteria in the selection of computing nodes to achieve a more precise resource allocation. Through a comparative analysis with two baseline algorithms, the proposed algorithm demonstrates effective performance improvements, enhancing the accuracy of computing resource matching by 40%-50% and significantly reducing computing processing latency. © 2023 ieee.

关键词： Heterogeneous edge computing Large scale Load balancing Matching accuracy of computing resources

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A Literature Review on AWS-Based Cloud computing: A Case in South Korea

A Literature Review on AWS-Based Cloud Computing: A Case in ...

引用

ieee international conference on Big Data and Smart computing (BigComp)

作者： Lee, Byeongcheon Oh, Jinyeong Shon, Woojin Moon, Jihoon Soonchunhyang Univ Dept AI & Big Data Asan South Korea

ISBN: (纸本)9781665475785

A collection of information technology (IT) services known as "cloud computing" is offered to customers across a network on a subscription basis with the flexibility to scale up or down based on their service needs. The emergence of cloud computing technology, which has tremendous advantages, is one of the major advancements in recent times. Many computers and servers are specifically devoted to meeting the demands of businesses in a cloud computing system for internal communications. Users can access their services via an internet connection. Registered users have remote access to both hardware and software, thanks to the cloud service, which has made essential adjustments to how information is stored and made accessible. This paper investigates the use of Amazon Web Services (AWS) for big data processing and analytics in South Korea. We collected several domestic journal and conference papers that studied local cloud services based on AWS to introduce distributed systems and cloud computing technologies. This study can provide researchers with a compact version of the extensive AWS-based data processing literature and potential future insights. It can also provide stakeholders tailored services, information about cutting-edge solutions that can influence academics, and details about current research needs.

关键词： Amazon Web Services (AWS) big data processing cloud computing distributed system

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distributed Radar Angle Measurement Based on Deep Learning 2

Distributed Radar Angle Measurement Based on Deep Learning

引用

2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Xiao, Jiacheng Zhang, Hongyan Li, Peng Lin, Sen Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368888

distributed radar systems are pivotal in modern military and civilian contexts, with phased array radar technology greatly impacting angle measurement precision for target tracking and identification. This article presents a novel angle measurement approach for distributed radar systems, harnessing deep learning. This technique not only heightens angle measurement precision but also optimizes network efficiency, enhancing overall system performance. To validate our method, we conducted extensive experiments involving diverse angle targets, both large and small, consistently achieving outstanding results across scenarios. This underscores the method's exceptional accuracy and robustness in real-world situations. In summary, our innovative distributed radar angle measurement technique, driven by deep learning, promises improved accuracy and efficiency, bolstering the capabilities of distributed radar systems across applications. © 2024 ieee.

关键词： Angle measurement

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Intelligent Multi-Domain Edge Orchestration for Highly distributed Immersive Services: An Immersive Virtual Touring Use Case 7

Intelligent Multi-Domain Edge Orchestration for Highly Distr...

引用

7th ieee international conference on Edge computing and Communications (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Benmerar, Tarik Zakaria Theodoropoulos, Theodoros Fevereiro, Diogo Rosa, Luis Rodrigues, Joao Taleb, Tarik Barone, Paolo Tserpes, Konstantinos Cordeiro, Luis ICTFICIAL Oy Espoo Finland Harokopio Univ Athens Athens Greece OneSource Coimbra Portugal Cyango Lisbon Portugal Univ Oulu Oulu Finland Hewlett Packard Enterprise Milan Italy

ISBN: (纸本)9798350304831

Edge cloud technologies in tandem with AI-enabled solutions can contribute to overcoming the challenges that pertain the distributed execution of immersive services and contribute towards providing a positive experience for the end-users. Intelligent resource management, orchestration, and prediction systems can optimize the deployment of services, adapt to changing demands, and ensure that the services are running smoothly. This paper introduces a novel architectural paradigm capable of facilitating multi-domain edge orchestration for highly distributed immersive services by incorporating a plethora of AI solutions and technological enablers that can support multi-domain edge deployments. The proposed architecture is designed to operate on the basis of multi-level specification blueprints, which decouple the simple high-level user-intent infrastructure definition from the AI-driven orchestration and the final execution plan. The Application Management Framework (AMF) offers a visual language and tool that can be used as an alternative to a formal method for creating the intent blueprint. In the frame of this work, the latter is validated by an immersive virtual touring use-case scenario.

关键词： Edge cloud immersive service orchestration cluster Kubernetes centralized management decentralized management

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Make Federated Learning a Standard in Robotics by Using ROS2 10

Make Federated Learning a Standard in Robotics by Using ROS2

引用

16th ieee/ACM international conference on Utility and Cloud computing (UCC) / 10th ieee/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Marino, Roberto Carnevale, Lorenzo Fazio, Maria Villari, Massimo Univ Messina Phys Sci & Earth Sci Dept Math & Comp Sci Messina Italy

ISBN: (纸本)9798400704734

The use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately there are a lack of robot oriented framework for federated learning that use state of the art machine learning libraries. ROS2 (Robot Operating systems) is a standard de-facto in robotics for building up teams of robots in a multi-node fully distributed manner. In this paper we presents the integration of ROS2 with PyTorch allowing an easy training of a global machine learning model starting from a set of local datasets. We present the architecture, the used methodology and finally we discuss the experimentation results over a well-known public dataset.

关键词： Federated Learning Robotics ROS PyTorch distributed Intelligence Machine Learning

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Edge-to-Cloud Continuum Orchestrator for distributed Video Applications 19

Edge-to-Cloud Continuum Orchestrator for Distributed Video A...

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19th ieee international Symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： Ruiu, Pietro Rubattu, Claudio Anedda, Matteo Lagorio, Andrea Popescu, Vlad Amarie, Nicusor Giusto, Daniele Fadda, Mauro Univ Sassari Sassari Italy Univ Cagliari UdR CNIT Cagliari Cagliari Italy Transilvania Univ Brasov Brasov Romania

ISBN: (纸本)9798350364279;9798350364262

The current paper presents the implementation of an orchestrator for distributed edge-to-cloud systems based on heterogeneous nodes in urban environments. The orchestrator considers available resources, specifically video feeds from surveillance cameras, distributed across levels (i.e., from cloud to edge and viceversa) as a continuum and organizes them, taking into account their properties such as distance from the data source, processing latency, involved resources, and functionalities. The continuum from edge to cloud enables distributed processing of the video signal and network infrastructure management to optimize performance and energy efficiency, supporting mobility and workload balancing.

关键词： Video Processing Smart Cities Edge computing Hardware Acceleration Container Orchestration

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