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检索条件"任意字段=24th IEEE International Conference on Distributed Computing Systems"

共 13138 条记录，以下是31-40 订阅

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Tangram: High-resolution Video Analytics on Serverless Platform with SLO-aware Batching 44

Tangram: High-resolution Video Analytics on Serverless Platf...

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

作者： Peng, Haosong Zhan, Yufeng Li, Peng Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing Peoples R China Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9798350386066;9798350386059

Cloud-edge collaborative computing paradigm is a promising solution to high-resolution video analytics systems. the key lies in reducing redundant data and managing fluctuating inference workloads effectively. Previous work has focused on extracting regions of interest (RoIs) from videos and transmitting them to the cloud for processing. However, a naive Infrastructure as a Service (IaaS) resource configuration falls short in handling highly fluctuating workloads, leading to violations of Service Level Objectives (SLOs) and inefficient resource utilization. Besides, these methods neglect the potential benefits of RoIs batching to leverage parallel processing. In this work, we introduce Tangram, an efficient serverless cloud-edge video analytics system fully optimized for both communication and computation. Tangram adaptively aligns the RoIs into patches and transmits them to the scheduler in the cloud. the system employs a unique "stitching" method to batch the patches with various sizes from the edge cameras. Additionally, we develop an online SLO-aware batching algorithm that judiciously determines the optimal invoking time of the serverless function. Experiments on our prototype reveal that Tangram can reduce bandwidth consumption and computation cost up to 74.30% and 66.35%, respectively, while maintaining SLO violations within 5% and the accuracy loss negligible.

关键词： video analytics batching inference serverless computing

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Efficient Verification of Multi-Agent systems through Parallel 24

Efficient Verification of Multi-Agent Systems Through Parall...

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24th ieee international conference on Software Quality, Reliability and Security (QRS)

作者： Yao, Zhen Liu, Jing Chen, Xiaohong Han, Li Sun, Haiying East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China

ISBN: (纸本)9798350365634

Multi-agent systems (MASs) have garnered significant interest across various academic fields. Although MASs are widely applicable, they continue to encounter several challenges, particularly in terms of security.. Model checking, a verification technique that examines all possible system states, is employed to address these challenges. However, the state space of many practical systems can be prohibitively large, leading to exponential growth in verification time. this study introduces a new method for verifying MASs using Strategy Computation Tree Logic (SCTL) via the connective probe machine, a model of fully parallel computing. this approach is pioneering in utilizing the probe machine to speed up MAS verification, specifically allowing for the parallel resolution of SCTL formulas. Unlike conventional model checkers, our method can uncover multiple counterexamples for specified properties, facilitating the identification of various system flaws. We have developed a model checker named MC2PM based on our approach and have validated its feasibility and efficiency through experiments.

关键词： model checking probe machine temporal logic strategy logic parallel computing

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Demo: Metamorphosis - A Consensus Protocol for distributed Message Brokers 44

Demo: Metamorphosis - A Consensus Protocol for Distributed M...

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

作者： Orlov, Dmitry Rybianov, Anton Yandex Sch Data Anal Moscow Russia MIPT Univ Moscow Russia

ISBN: (纸本)9798350386066;9798350386059

Metamorphosis is a consensus protocol derived from Raft with addition of a witness replica role. In this role, a replica does not store a full copy of data under normal mode of operation. the selection of a replica into this role is dynamic. It only occurs when the set of full replicas fails to complete a write fully within a timeout. Data replication using a consensus protocol with witnesses is attractive for building distributed message brokers. Message brokers are widely used to collect data over geographically separate locations. To achieve fault tolerance, replication creates redundant data copies. Metamorphosis reduces costs associated with redundancy by only storing complete data on a subset of replicas. Metamorphosis achieves near 1/3 cost reduction in operating distributed data ingestion, when compared to Apache Kafka, a popular message broker.

关键词： distributed message broker message queue kafka consensus raft witness cloud

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Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments 24

Apodotiko: Enabling Efficient Serverless Federated Learning ...

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24th ieee/ACM international Symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Chadha, Mohak Jensen, Alexander Gu, Jianfeng Abboud, Osama Gerndt, Michael Tech Univ Munich Chair Comp Architecture & Parallel Syst Garching Germany Huawei Technol Munich Germany

ISBN: (纸本)9798350395679;9798350395662

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, current serverless FL systems still suffer from the presence of stragglers, i.e., slow clients that impede the collaborative training process. While strategies aimed at mitigating stragglers in these systems have been proposed, they overlook the diverse hardware resource configurations among FL clients. To this end, we present Apodotiko, a novel asynchronous training strategy designed for serverless FL. Our strategy incorporates a scoring mechanism that evaluates each client's hardware capacity and dataset size to intelligently prioritize and select clients for each training round, thereby minimizing the effects of stragglers on system performance. We comprehensively evaluate Apodotiko across diverse datasets, considering a mix of CPU and GPU clients, and compare its performance against five other FL training strategies. Results from our experiments demonstrate that Apodotiko outperforms other FL training strategies, achieving an average speedup of 2.75x and a maximum speedup of 7.03x. Furthermore, our strategy significantly reduces cold starts by a factor of four on average, demonstrating suitability in serverless environments.

关键词： Federated learning Deep learning Serverless computing Function-as-a-service Straggler mitigation

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Leaky-Integrate-Fire Neuron Based on Antiferromagnetic Skyrmion Under Strain Gradient 24

Leaky-Integrate-Fire Neuron Based on Antiferromagnetic Skyrm...

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ieee 24th international conference on Nanotechnology (NANO)

作者： Raj, Ravish Kumar Verma, Ravi Shankar Saini, Shipra Kumar, Mohit Shukla, Alok Kumar Kaushik, Brajesh Kumar Indian Inst Technol Roorkee Elect & Commun Engn Dept Roorkee Uttarakhand India

ISBN: (纸本)9798350386257;9798350386240

Antiferromagnetic (AFM) skyrmion-based spintronic devices are favored over their ferromagnetic (FM) counterparts due to compelling characteristics including robust exchange interaction, interfacial Dzyaloshinskii-Moriya interaction (DMI), minimal stray fields, low susceptibility to external fields, and inherent non-volatility. the straight trajectories followed by AFM skyrmions prevent their annihilation at the edges of nanoscale racetracks, making them a more promising candidate than FM skyrmions for future spintronic applications. In this work, AFM skyrmion dynamics under strain gradient is studied. It is observed that creating the strain gradient is an efficient way to drive AFM skyrmions with a high longitudinal speed in the order 800 m/s. Furthermore, a leaky integrate-and-fire (LIF) neuron is proposed based on strain gradient for enhancing energy efficiency in neuromorphic computing systems. this opens a new alternative way to manipulate AFM skyrmions for the development of energy-efficient AFM skyrmion based devices in neuromorphic computing.

关键词： Antiferromagnetic Skyrmion Strain gradient Leaky-integrate-fire Neuromorphic computing Spin transfer torque

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Acoustic vibrations sensitivity of negative curvature hollow core optical fibres (Invited) 24

Acoustic vibrations sensitivity of negative curvature hollow...

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24th international conference on Transparent Optical Networks (ICTON)

作者： Honzatko, Pavel Borodkin, Andrei Baravets, Yauhen Moravec, Ondrej Podrazky, Ondrej Barton, Ivo Jasim, Ali Czech Acad Sci Inst Photon & Elect Prague Czech Republic

ISBN: (纸本)9798350377330;9798350377323

We report on various aspects of interaction of acoustic waves with light guided in negative curvature hollow core fibres. Backscattering in these fibres is significantly suppressed thus greatly reducing a potential to exploit them for distributed acoustic sensing. In transmission, on contrary, their sensitivity to acoustic waves might even exceed sensitivity of conventional fibres but it can be controlled to some extent by proper design. Keywords: hollow core fibres, distributed acoustic sensing, fibre acoustic sensors.

关键词： hollow core fibres distributed acoustic sensing fibre acoustic sensors

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Demo:SCDRL: Scalable and Customized distributed Reinforcement Learning System 44

Demo:SCDRL: Scalable and Customized Distributed Reinforcemen...

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

作者： Sun, Chuxiong Qiang, Wenwen Li, Jiangmeng Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9798350386066;9798350386059

Reinforcement Learning (RL) has marked significant achievements across a variety of complex tasks in real-world scenarios. However, the efficacy of RL predominantly relies on the availability of extensive datasets and considerable training resources. Hence, there is the critical need for a distributed system capable of generating and processing vast amounts of data with efficiency. In this work, we introduce a Scalable and Customized distributed Reinforcement Learning system (SCDRL). Concretely, we analyze the paradigm of RL and decouple the major RL computations into three main aspects, i.e. environment simulation, policy inference and policy training. Such decouple enables SCDRL to efficiently allocate computing resources (be it CPUs or GPUs of varying computational capabilities) tailored to the specific needs of each component. We demonstrate the effectiveness of our method across several key RL environments, demonstrating that our system not only achieves significant learning outcomes and enhanced throughput but also utilizes computing resources with greater efficiency. Notably, our findings reveal SCDRL's proficiency in optimizing resource use not just in single-machine setups but also in multi-machine configurations, all the while maintaining data efficiency and resource utilization.

关键词： component formatting style styling insert

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DroMPI: Parallel Computation Over Drop computing 24

DroMPI: Parallel Computation Over Drop Computing

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24th ieee/ACM international Symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Grosu, George-Mircea Nistor, Silvia-Elena Ciobanu, Radu-Ioan Dobre, Ciprian Pop, Florin Natl Univ Sci & Technol Politehn Bucharest Bucharest Romania Natl Inst Res & Dev Informat ICI Bucharest Romania Acad Romanian Scientists Bucharest Romania

ISBN: (纸本)9798350377521;9798350377514

With the advancement of technology and the spread of multi-core systems, the need for parallelization arises and the interest in programming models is growing. At the same time, new distributed computing models have been proposed, being in fierce competition to obtain the highest possible performance. the Drop computing Paradigm proposes the idea of decentralized computing over ad-hoc opportunistic networks of mobile and Edge devices. In this respect, the Drop computing model does not only aim to achieve a minimum turnaround time but also to optimize other characteristics related to mobile devices, such as limited resources and opportunistic communication. therefore, it is necessary to define a new programming model called DroMPI that intends to extend the capabilities of current parallel and distributed programming models, based on the Drop computing paradigm. therefore, the solution aims to develop a library that takes advantage of hardware capabilities in the interest of the Drop computing paradigm and also provides programmers with a high-level programming interface. the library's features will be based on the Message Passing Interface (MPI) standard, which will be responsible for inter-node parallelization. the name of the library, DroMPI, is an acronym for Drop computing and MPI. the implementation of the model will be responsible for the management of communication between nodes and for providing an Application Programming Interface (API) for the development of parallel applications in the Drop computing paradigm.

关键词： Decentralized computing Parallel Computation Drop computing Application Programming Interface MPI

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Engineering distributed Collective Intelligence in Cyber-Physical Swarms 20

Engineering Distributed Collective Intelligence in Cyber-Phy...

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20th Annual international conference on distributed computing in Smart Sensor systems and the Internet of things (DCOSS-IoT)

作者： Aguzzi, Gianluca Savaglio, Claudio Univ Bologna Cesena Italy Univ Calabria Calabria Italy

ISBN: (纸本)9798350369458;9798350369441

Cyber-physical swarms represent a paradigm shift in distributed systems, mirroring characteristics akin to natural swarms, such as self-organization, scalability, and fault tolerance. this paper delves into these complex systems, characterized by vast networks of cyber-physical entities with limited environmental awareness, yet capable of exhibiting emergent collective behaviors. these systems encompass a diverse array of scenarios, ranging from swarm robotics to the interconnectivity in smart cities, as well as the collaboration among augmented humans. the engineering of such systems presents unique challenges, primarily due to their intricate complexity and the spontaneous nature of their collective behaviors. this paper aims to dissect these challenges, offering a clear delineation of potential approaches. We present a comprehensive analysis, shedding light on the intricacies of engineering cyberphysical swarms and discussing modern solutions in engineering collective applications for such systems.

关键词： distributed collective intelligence large-scale IoT collective computing aggregate computing

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Training Computer Scientists for the Challenges of Hybrid Quantum-Classical computing 24

Training Computer Scientists for the Challenges of Hybrid Qu...

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24th ieee/ACM international Symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： De Maio, Vincenzo Kanatbekova, Meerzhan Zile, Felix Friist, Nicolai Guggemost, Tobias Brandic, Ivona TU Wien HPC Lab Inst Informat Syst Engn Favoritenstr 9-11 A-1040 Vienna Austria TU Wien Atominst Stadionallee 2 A-1020 Vienna Austria Univ Vienna Fac Phys Vienna Ctr Quantum Sci & Technol VCQ Vienna Austria Univ Vienna Fac Phys Christian Doppler Lab Photon Quantum Comp Vienna Austria

ISBN: (纸本)9798350395679;9798350395662

As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. this fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.

关键词： computer science education post-Moore era high-performance computing quantum computing hybrid systems

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