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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721-730 订阅

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Reinforcement Learning based Matching for Parallel Computation Offloading in Dynamic Fog computing Networks 10

Reinforcement Learning based Matching for Parallel Computati...

引用

international conference on Smart computing (SMARTCOMP)

作者： Tran-Dang, Hoa Kim, Dong-Seong Kumoh Natl Inst Technol Gumi Si Gyeongsangbuk D South Korea

ISBN: (纸本)9798350349955;9798350349948

Matching theory has been efficiently applied in fog computing networks (FCNs) to design distributed task offloading algorithms in the presence of selfishness and rationale of fog nodes. Given the dynamic nature of fog computing environment, it is challenging to obtain the stable matching since the preference relations of two sides of matching game is unknown a prior. To address this challenge, this paper proposes RE-MATCH, a framework for parallel computation offloading in dynamic fog computing networks (FCNs). RE. MATCH is based on the matching theory and Thompson Sampling (TS) empowered Multi-Armed Bandit (MAR) learning to deal with the inherent challenges allowing task nodes with needed computation tasks to estimate the informed preference relations of helper nodes with available computing resource quickly and accurately. Extensive simulation results demonstrate the potential advantages of the TS based learning over the epsilon-greedy and upper confidence bound (UCB) based baselines.

关键词： Fog computing network Thompson sampling Parallel Computation Offloading Stable Matching

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OEDASA: Optimization Enabled Error-Diluted Approximate Systolic Array Design for an Image Processing Application 37

OEDASA: Optimization Enabled Error-Diluted Approximate Systo...

引用

37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded systems (ES)

作者： Devi, Dantu Nandini Kumar, Gandi Ajay Gowda, Bindu G. Rao, Madhav IIIT Bangalore Bangalore Karnataka India

ISBN: (纸本)9798350384406

Systolic Array (SA) architecture is a hardware accelerator for running Artificial Intelligence (AI) workloads. Although approximate computing offers hardware and performance benefits, it often sacrifices accuracy, limiting its application to error-resilient tasks. Many inexact multipliers in SA exhibit one-sided Error Distribution (ErD), resulting in significant accumulated errors. Approximate computing proves valuable in error-resilient image processing applications, fostering research into various approximation techniques for enhanced hardware performance. This paper explores SA architecture with different configurations of approximate multipliers (AMs) featuring distinct ErDs. It employs a meta-heuristic optimization algorithm, Particle Swarm Optimization (PSO), for image smoothing tasks. The study delves into trade-offs between hardware acceleration and accuracy, offering insights for advancing approximate computing in SA-based AI workloads. The PSO-evolved SA configuration showcases a 3.05% performance improvement, 10.63% silicon footprint reduction, and 10.32% power savings compared to the exact SA. The PSO-derived SA structure also offers notable hardware gains when compared with other state-of-theart (SOTA) benchmark designs. Additionally, it demonstrates a better Structural Similarity Index (SSIM) compared to SA with one-sided error-distributed AMs. The proposed spatially optimized SAs with AMs of different ErDs represent a step towards reliable hardware design and establishing a nearly exact AI accelerator system. All design files and results are shared openly with the research and design community for easy adoption and further exploration.

关键词： Systolic Array Convolution Approximate computing Image Processing Particle Swarm Optimisation (PSO)

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A distributed Coordinated Control Strategy for Unbalanced Voltage Suppression in Bipolar DC Microgrids with Flexible ZIP Load

A Distributed Coordinated Control Strategy for Unbalanced Vo...

引用

international conference on Smart Energy systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Zhang, Xuefei Zhou, Niancheng Wang, Qianggang Liao, Jianquan Zhou, Yiyao Xu, Xiaolong Yang, Meihui Chongqing Univ State Key Lab Power Transmiss Equipment Technol Chongqing Peoples R China Sichuan Univ Coll Elect Engn Chongqing Peoples R China

ISBN: (纸本)9798350386509;9798350386493

With the growing integration of distributed generations (DGs) and renewable energy sources, bipolar DC microgrids have attracted increased attention due to their high efficiency, cost-effectiveness, and flexibility. However, bipolar DC microgrids are susceptible to severe unbalanced voltage problems that threaten their safe and stable operation. To tackle this challenge, this paper introduces a distributed coordinated control strategy for unbalanced voltage regulation in bipolar DC microgrids featuring flexible ZIP loads. The proposed strategy comprises primary and secondary control mechanisms. The proposed control facilitates power distribution among DGs while maintaining the average bus voltage at its rated value. Meanwhile, the secondary control exploits the consensus theory and the relationship between bipolar voltages and the voltage unbalanced factor (v(hb)%), enabling convergence of v(hb)% across poles and maintaining of the average bus voltage at its rated value. Simulation results validate the effectiveness of the proposed strategy, demonstrating its ability to effectively mitigate unbalanced voltage and enhance voltage stability in bipolar DC microgrids.

关键词： bipolar DC microgrid unbalanced voltage distributed coordinated control consensus theory ZIP load

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BiP: Bit-Phase-Flip Error Mitigation in Quantum Communications

BiP: Bit-Phase-Flip Error Mitigation in Quantum Communicatio...

引用

ieee international conference on Communications (ieee ICC)

作者： Li, Kai Chaudhary, Vini Chowdhury, Kaushik R. Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

ISBN: (纸本)9781538674628

Quantum links are inherently noisy and quantum information bits (qubits) suffer upto 13% degradation in their entangled states within time-scale of 0.5 ms. Thus, mitigating errors becomes essential for reliable end-to-end data communication in a multi-hop quantum network. Compared to the typical operations performed within the contained environment of a single quantum computer, removal of both bit- and phase-flip errors in a distributed network of such computers is challenging due to the stochastic variations in the noise at each intermediate link. This paper describes a scheme that determines both the bit- and phase-flip errors (abbreviated as 'BiP') and mitigates them for distributed and networked quantum systems. To achieve this, we model the environment noise using general error models and obtain error calibration matrices in different computational bases for bit-phase-flip errors. Results reveal that BiP improves the fidelity beyond 95% for the received qubits compared to the state-of-the-art error mitigation method by correcting the elevation theta and azimuthal angles phi in the Bloch sphere representation.

关键词： Quantum communication network quantum error mitigation quantum computing

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A Lightweight Smart Agent for Detecting Attacks in distributed Machine Learning

A Lightweight Smart Agent for Detecting Attacks in Distribut...

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2024 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2024

作者： Fomicheva, S.G. Bardovsky, A.A. Saint Petersburg University Of Aerospace Instrumentations Department Of Information Security Saint Petersburg Russia

ISBN: (纸本)9798350364989

A lightweight intelligent security agent is proposed that functions within the boundaries of a trusted execute environment and controls the detection of anomalies in computing processes when solving object detection tasks in a distributed IT infrastructure. The agent controls the stress factors of the processes and signals their deviations from the standard values using its functionality in the form of an adaptive neuro-fuzzy inference system. The agent's ability to detect rootkits with an accuracy of 96.4 % at 3% overhead on processor performance when working with a timeout of 5 seconds is shown. A test bench has been proposed and implemented to study the tasks of trusted distributed machine learning using edge and fog computing. © 2024 ieee.

关键词： Fog computing

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Selective Preemption of distributed Deep Learning Training 16

Selective Preemption of Distributed Deep Learning Training

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ieee 16th international conference on Cloud computing (ieee CLOUD)

作者： Go, Younghun Shin, Changyong Lee, Jeunghwan Yoo, Yeonho Yang, Gyeongsik Yoo, Chuck Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350304817

As more distributed deep learning (DDL) jobs run in public clouds, their effective scheduling becomes a major challenge. Current studies prioritize the execution of jobs with less remaining time, which is known to be the best in reducing average job completion time (JCT). However, we observe that this approach does not work when the preemption for pausing and loading jobs weighs in;sometimes, the preemption overheads of DDL jobs take up to hundreds of seconds. This results in very ineffective scheduling, so in some cases, the first-in-first-out policy performs much better. This paper proposes a new scheduling framework called Xion that takes into account the preemption overheads and only preempts DDL jobs when it is beneficial. Our evaluation results demonstrate that Xion effectively reduces the average JCT by 19% and improves the waiting time by 1.64x.

关键词： distributed deep learning GPU cloud Job scheduling GPU scheduling SRTF Preemption

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Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets using Renewable Energy 21

Sensing Together: Cooperative Task Adaptation and Scheduling...

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21st ieee international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Liri, Elizabeth Ramakrishnan, K. K. Kar, Koushik Esposito, Flavio Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA Rensselaer Polytech Inst Elect Comp & Syst Engn Dept Troy NY USA St Louis Univ Comp Sci Dept St Louis MO 63103 USA

ISBN: (纸本)9798350363999;9798350364002

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our "Sensing Together" solution includes two algorithms: distributed Task Adaptation (DTA) and distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed "token passing" protocol. DTA adapts the sensing rates from their "raw" values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling.

关键词： multi-sensor IoT distributed scheduler energy efficiency task adaptation cooperative sensing comb placement problem

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Research on distributed Task Allocation Algorithm for UAV Swarm Based on Evolutionary Game 7

Research on Distributed Task Allocation Algorithm for UAV Sw...

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7th international conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Kong, Yansong Chang, Qiang Wang, Hongliang Defense Innovation Institute Chinese Academy Of Military Science Intelligent Game And Decision Laboratory Beijing China

ISBN: (纸本)9798350361445

With the advancements in technologies such as artificial intelligence, communication, and microelectronics, unmanned systems have rapidly developed and gradually replaced humans in certain challenging and adversarial domains, taking on increasingly complex tasks. Constrained by energy supply, unmanned systems, represented by UAVs, have limited payload, computing power, and autonomy. Faced with intricate tasks in high-dynamic environments, people address the challenges by scaling up unmanned systems to achieve victory through quantity. When employing massive unmanned swarms to execute complex missions, a primary concern is how to efficiently and reasonably allocate tasks. This paper, grounded in evolutionary game theory, proposes a novel method for task allocation in UAV swarms. Each UAV evaluates the task completion of neighboring UAVs and adjusts local tasks based on replicator dynamics. Through multiple iterations, the proposed approach achieves global optimal task allocation. Finally, the effectiveness of the algorithm proposed in this paper is validated through simulations. The research findings presented herein can provide support for distributed task allocation in massive unmanned swarms under conditions of limited computing power and restricted communication. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Architectural Vision for Quantum computing in the Edge-Cloud Continuum

Architectural Vision for Quantum Computing in the Edge-Cloud...

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2nd ieee international conference on Quantum Software (ieee QSW)

作者： Furutanpey, Alireza Barzen, Johanna Bechtold, Marvin Dustdar, Schahram Leymann, Frank Raith, Philipp Truger, Felix TU Vienna Distributed Syst Grp Vienna Austria Univ Stuttgart Inst Architecture Applicat Syst Stuttgart Germany

ISBN: (纸本)9798350304794

Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.

关键词： Quantum computing Edge computing Compute Continuum Split computing Circuit Cutting Task Partitioning DNN Partitioning Classical-Quantum Hybrid Machine Learning Quantum Neural Networks Warm-Starting

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Open Architecture for distributed and Cooperative Media Algorithms 3

Open Architecture for Distributed and Cooperative Media Algo...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Bhambu, Pawan Manjunatha, R. Srivastava, Pratima Vivekananda Global University Department of Computer Science and Engineering Jaipur India School of Sciences Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Arka Jain University Department of Biotechnology Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

The Open structure for allotted and Cooperative Media Algorithms (OADCMA) is an open-deliver framework imparting a plug-in platform that lets customers, without problem, develop distributed and cooperative media algorithms to utilize the computational assets of diverse heterogeneous structures. It's miles designed for operations along with video capture, transmission, and machine operations. OADCMA affords unified APIs for allotted and cooperative media algorithms, which simplifies the implementation of distributed and cooperative media algorithms. Additionally, OADCMA permits green parallel computing while averting scalability problems. OADCMA helps a widespread fashion of verbal exchange networks, including allotted finite discipline computing. It is appropriate for an extensive kind of allotted and cooperative media programs together with video surveillance and broadcasting. © 2024 ieee.

关键词： Security systems

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