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检索条件"任意字段=9th International Conference on Distributed Computing Systems"

共 7450 条记录，以下是31-40 订阅

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Integrating Serverless and DRL for Infrastructure Management in Streaming Data Processing across Edge-Cloud Continuum 44

Integrating Serverless and DRL for Infrastructure Management...

引用

44th IEEE international conference on distributed computing systems (ICDCS)

作者： Dehury, Chinmaya Kumar Srirama, Satish Narayana Univ Tartu Inst Comp Sci Tartu Estonia Univ Hyderabad Sch Comp & Informat Sci Hyderabad India

ISBN: (纸本)9798350354720;9798350354713

Advancements in stream data processing engines (SDPE) increasingly require the use of modern Deep Reinforcement Learning (DRL)-based management strategies and distributed computing paradigms like serverless computing to enhance efficiency and scalability across the edge-cloud continuum. this study explores the challenges associated with this integration, especially with Apache Spark. Despite their advanced capabilities, modern SDPEs still lack full maturity in terms of efficiently managing dynamic resource demands and seamlessly integrating with other technologies. To fill this gap, we propose the architecture of ISIM-SDP, acronym for Integrating Serverless and DRL for Infrastructure Management in Streaming Data Processing across edge-cloud continuum. By implementing a DRL-based approach, the system dynamically adjusts resource allocation in real-time, enhancing the flexibility and scalability of computational resources. ISIM-SDP leverages serverless frameworks to reduce operational overhead and improve system responsiveness. Experimental results demonstrate the effectiveness of ISIM-SDP in optimizing resource usage and improving the throughput and latency of stream processing tasks.

关键词： distributed System Cloud computing Serverless computing Stream Processing Deep Reinforcement Learning Apache Spark Kubernetes Knative Edge-cloud Continuum Edge computing IoT

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Development of a Visualization for Developers of a distributed System Including Idle Computers 25

Development of a Visualization for Developers of a Distribut...

引用

25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Tsutsumi, Shosuke Higashinc, Masayuki Takahashi, Kenichi Kawamura, Takao Tottori University Department of Engineering Tottori Japan

ISBN: (纸本)9798350330014

this paper proposes a visualization function for developers in volunteer computing. In recent years, information systems such as e-Learning systems and groupware have been introduced in companies and educational institutions to promote DX, but the introduction and operation of these systems incur a large cost burden. therefore, we are developing a distributed information system that can be introduced and operated at low cost by utilizing surplus computers (idle computers) in an organization. Idle computers are unstable depending on the operations of their original users and have different performances. In order to use idle computers efficiently, it is necessary to improve the performance of the system and modify it to add new functions, taking into account the differences in behavior and performance of each machine. In this paper, visualization for developers to easily check the behavior and performance of a computer in an idle state is developed, and its usefulness is evaluated through experiments using a real system. © 2023 IEEE.

关键词： Visualization

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Optimal Sizing and Location of Battery Energy Storage systems in Distribution systems connected with distributed Generation using Salp Swarm Algorithm 9

Optimal Sizing and Location of Battery Energy Storage System...

引用

9th international Youth conference on Energy (IYCE)

作者： Wichitkrailat, Krit Premrudeepreechacharn, Suttichai Khunkitti, Sirote Siritaratiwat, Apirat Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai Thailand Khon Kaen Univ Fac Engn Dept Elect Engn Khon Kaen Thailand

ISBN: (纸本)9798350372397;9798350372380

this study presents an optimal sizing and location of battery energy storage systems (BESSs) in distribution systems connected with distributed generation (DG) to improve distribution system performance. the objective function aims to minimize system costs including installation cost and operation and maintenance costs of the BESSs. Moreover, by minimizing operation and maintenance costs, the system performance can be improved in terms of transmission line loss, voltage deviation and peak demand reductions. the optimization problems have been solved by salp swarm algorithm (SSA). the IEEE 33-bus distribution system integrated with photovoltaic (PV) and wind turbine (WT) is employed to evaluate the performance of the proposed approach. the results show that after the installation of BESSs by SSA, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased. Moreover, installation of 2 BESSs can more improve performance compared to 1 BESS installation. However, the system costs of the 2-BESS case are also higher than those of 1-BESS case.

关键词： battery energy storage systems (BESSs) distributed generation (DG) optimization salp swarm algorithm (SSA)

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Balancing Voltage Stability and Communication Cost in Interconnected DERs: A Double Deep Q-Network Approach to Data Traffic Scheduling 9

Balancing Voltage Stability and Communication Cost in Interc...

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9th international conference on Computer and Communication systems (ICCCS)

作者： Chen, Yongwen Qu, Chunxiao Zhou, Qingfeng Liu, Chanzi Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Dongguan Univ Technol Sch Elect Engn & Intelligentizat Dongguan Peoples R China

ISBN: (纸本)9798350350227;9798350350210

In order to increase the reliability and consistency of distributed generation systems (DGSs), deep reinforcement learning (DRL) is frequently used in energy and communication systems. Previous studies presented an awareness method for data transmission in interconnected large-scale distributed energy resources (DERs) based on the delay-tolerant Kalman filter (DTKF). However, it is not practical to use an excessive amount of communication resources for data transmission, and offline scheduling strategies cannot be dynamically adapted to the state of the system. this study presents a methodology for evaluating scheduling strategies and modeling a double deep Q-network (DDQN) that balances both voltage stability and communication costs. Simulation results demonstrate the effectiveness of the proposed methodology in dynamically stabilizing the voltage with reasonable communication cost.

关键词： distributed energy resources deep reinforcement learning communication strategy data transmission

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An Energy-efficient and distributed Self-Adjusting Deployment Approach Based on Virtual Force for Underwater Sensor Networks 9

An Energy-efficient and Distributed Self-Adjusting Deploymen...

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9th international conference on Computer and Communication systems (ICCCS)

作者： Shen, Zhongwei Yin, Hongxi Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

Deployment for underwater sensor networks (UWSNs) is one of the key issues for the topology management, which determines the overall network coverage and profoundly affects the network data collection performance. this paper proposes a distributed energy-efficient self-adjusting UWSN deployment algorithm with the full consideration of the UWSN application scenarios, which includes two phases. In the initial deployment phase, the nodes at different positions will be assigned differential initial energy levels in different methods in accordance with the subsequent routing principles. During the redeployment phase, based on the virtual force theory, nodes will adjust their positions in a distributed way considering their neighbor nodes' positions, as well as the area and layer boundaries. through the simulation experiments, the proposed algorithm can effectively improve the network coverage performance and significantly benefit the network routing process compared with the benchmark UWSN self-adjusting deployment algorithms.

关键词： deployment routing underwater sensor network virtual force

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EdgeCam: A distributed Camera Operating System for Inference Scheduling and Continuous Learning 9

EdgeCam: A Distributed Camera Operating System for Inference...

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9th ACM/IEEE conference on Internet of things Design and Implementation (IoTDI)

作者： Doug, Yuqi Gao, Guanyu Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350370256;9798350370263

Deep Neural Networks (DNNs) are commonly used in camera systems for video surveillance. However, the computational demands of DNN inference pose challenges for on-edge video analytics due to potential delay. Additionally, edge cameras typically employ lightweight models, which are susceptible to data drift. In this demo, we present EdgeCam, an open-source distributed camera operating system that incorporates inference scheduling and continuous learning for video analytics. EdgeCam comprises multiple edge nodes and the cloud, enabling collaborative video analytics. Edge nodes also collect drift data to support continuous learning and maintain recognition accuracy. We have implemented essential functionalities and algorithms, ensuring modularity and ease of configuration. the source code of EdgeCam is at https://***/MSNLAB/EdgeCam.

关键词： Video analytics edge/cloud computing machine learning inference computer vision applications

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Towards a Federated Intrusion Detection System based on Neuromorphic computing 9

Towards a Federated Intrusion Detection System based on Neur...

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9th international conference on Smart and Sustainable Technologies (SpliTech)

作者： Lofu, Domenico Sorino, Paolo Di Noia, Tommaso Di Sciascio, Eugenio Politecn Bari Dept Elect & Informat Engn DEI Via E Orabona 4 I-70125 Bari Italy

ISBN: (纸本)9798350390797;9789532901351

Intrusion Detection systems (IDSs) have emerged as essential tools for detecting cyber attacks and safeguarding sensitive data. Over time, there has been a shift towards designing IDSs that leverage Federated Learning (FL) methods, enabling them to detect attacks across distributed environments while upholding privacy-preserving manner. Concurrently, selecting the appropriate algorithm for Host execution, ensuring data privacy, low power consumption, and swift execution, has become a promising challenge. Recently, there has been a growing interest in Spike Neural Networks (SNNs) due to their ability to directly generate spikes and closely emulate human brain functions. SNN-based models are optimized to achieve energy efficiency by representing computations through asynchronously generated spikes. To tackle these challenges, we propose a theoretical approach for implementing a Federated Intrusion Detection System (IDS) that gathers data from different geographical locations, based on Neuromorphic computing principles.

关键词： Neuromorphic computing Artificial Intelligence Federated Learning Intrusion Detection System

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CCIOT 2024 - Proceedings of 2024 9th international conference on Cloud computing and Internet of things

CCIOT 2024 - Proceedings of 2024 9th International Conferenc...

引用

9th international conference on Cloud computing and Internet of things, CCIOT 2024

ISBN: (纸本)9798400717161

the proceedings contain 18 papers. the topics discussed include: research on RedCap UE’s performance indicators in real network to support IoT applications;machine learning-based crop recommendation for IoT-enabled smart agriculture;cloud provisioning: a highly optimized, cost-effective, and efficient deployment model;smart pill box: an IoT-integrated application for monitoring patient medication usage at home;analyzing cybersecurity risk with a phishing simulation website;agile infrastructure: adapting systems to changing business requirements;a secure and efficient privacy scheme for location-based services in cloud environments;and research on garbled circuits-based identity authentication and ciphertext hidden access control in vehicular networks.

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A General-Purpose Middleware System for Edge-side Data Processing 9

A General-Purpose Middleware System for Edge-side Data Proce...

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9th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Satoh, Ichiro Natl Inst Informat 2-1-2 HitotsubashiChiyoda Ku Tokyo 1018430 Japan

ISBN: (纸本)9798350366495;9798350366488

Edge computing, including fog computing, processes data measured by their sensing systems. Such data often contain noise, losses, and non-canonical representations, necessitating preliminary processing to enhance data quality and convert it into certain formats. While data processing tasks have been centralized in data centers so far, edge computing, including fog computing, decentralizes these tasks to edge nodes. this paper presents a data processing framework for edge computing. the contribution of the framework is to adapt existing popular data processing approaches, originally designed for use in data centers consisting of high-performance servers connected through wide-band networks, for use in edge computing. the framework hides network configurations and node failures, enabling developers to focus solely on data processing tasks without worrying about underlying infrastructural complexities. this paper details the framework's architecture, discusses its integration with edge and fog computing environments, and evaluates its performance, demonstrating its superior efficiency and effectiveness in handling edge-based data processing challenges.

关键词： Data processing framework MapReduce edge computing

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Task offloading of edge computing network based on Lyapunov and deep reinforcement learning 9

Task offloading of edge computing network based on Lyapunov ...

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9th international conference on Computer and Communication systems (ICCCS)

作者： Qiao, Xudong Zhou, Yongxin Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Hainan Prov Fire Rescue Brigade Informat & Commun Dept Haikou Hainan Peoples R China

ISBN: (纸本)9798350350227;9798350350210

Reinforcement learning based task offloading is a promising research direction in edge computing. this paper proposes a Deep Reinforcement Learning (DRL) Task Offloading framework (LyDDPG) based on Lyapunov optimization, which leverages the strengths of both Lyapunov optimization and DRL. LyDDPG aims to minimize device energy consumption and reduce queue backlog under long-term data queue stability and delay constraints by decoupling the original optimization problem into an independent slot task offloading optimization problem. A multi-user edge computing network with time-varying wireless channels and random user task data arriving in a sequence time range is considered in this experiment. the simulation results show that the LyDDPG algorithm minimizes the energy consumption and queue backlog under the condition of satisfying the long-term stability constraints. the framework improves the adaptability and performance of the system in a dynamic network environment, and provides an efficient way to solve the problem of task offloading and resource allocation.

关键词： Task offloading Edge computing Lyapunov optimization Deep Reinforcement Learning

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