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检索条件"任意字段=23th IEEE International Conference on Distributed Computing Systems"

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distributed Quantum computing for Chemical Applications 5

Distributed Quantum Computing for Chemical Applications

引用

2024 international conference on Quantum computing and Engineering

作者： Jones, Grier M. Jacobsen, Hans-Arno Univ Toronto Dept Chem & Phys Sci Toronto ON Canada Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9798331541378

In recent years, interest in quantum computing has increased due to technological advances in quantum hardware and algorithms. Despite the promises of quantum advantage, the applicability of quantum devices has been limited to few qubits on hardware that experiences decoherence due to noise. One proposed method to get around this challenge is distributed quantum computing (DQC). Like classical distributed computing, DQC aims at increasing compute power by spreading the compute processes across many devices, with the goal to minimize the noise and circuit depth required by quantum devices. In this paper, we cover the fundamental concepts of DQC and provide insight into where the field of DQC stands with respect to the field of chemistry a field which can potentially be used to demonstrate quantum advantage on noisy-intermediate scale quantum devices.

关键词： distributed systems quantum distributed systems quantum computing chemistry

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Research on distributed Node Resource Optimization Mechanism for Multi-Agent systems Combined with Blockchain Technology 7

Research on Distributed Node Resource Optimization Mechanism...

引用

7th ieee international conference on Blockchain (ieee Blockchain)

作者： Yin, Zihang Bai, Bing Liu, Yang Cheng, Tongtong China Acad Informat & Commun Technol Beijing Peoples R China China Inst Commun Beijing Peoples R China

ISBN: (纸本)9798350351606;9798350351590

this paper explores integrating blockchain technology into multi-agent systems (MAS) to enhance distributed node resource optimization. Key challenges addressed include task decision-making, task allocation, and resource scheduling, with a focus on minimizing energy consumption and latency. Blockchain ensures secure, efficient coordination among nodes, mitigating issues like data privacy leaks and system failures. the study also leverages federated learning for secure decentralized machine learning model training. Simulation results demonstrate the enhanced performance, security, and scalability of MAS with blockchain, paving the way for more efficient distributed computing environments.

关键词： Blockchain Multi-Agent systems (MAS) distributed Resource Optimization Federated Learning Secure Coordination

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Architecting a SDS for Microservices-based distributed Edge computing systems 11

Architecting a SDS for Microservices-based Distributed Edge ...

引用

11th ieee international conference on Software Defined systems, SDS 2024

作者： Vora, Urjaswala Pennsylvania State University College of Information Sciences and Technology University Park United States

ISBN: (纸本)9798331518325

Software Defined systems (SDS) abstract the actual hardware at different layers with software components, layers such as Networking, Storage, Security, Servers, Data Centers, Clouds etc. this abstraction facilitates construction, integration / composition and management of complex systems effectively and sustainably. Microservices architecture is increasingly being used for designing and deploying large-scale application systems in both cloud-based and enterprise infrastructures where relatively small, loosely coupled microservices communicate with each other using lightweight communication protocols and abstract complex software application(s). We propose a sustainable SDS architecture to manage microservices of a distributed edge computing system which reduces dependency on any data centre / cloud infrastructure. © 2024 ieee.

关键词： Cloud computing architecture

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the Potentials of AI Planning on the Edge 7

The Potentials of AI Planning on the Edge

引用

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

作者： Georgievski, Ilche Aiello, Marco Univ Stuttgart IAAS Serv Comp Dept Stuttgart Germany

ISBN: (纸本)9798350304831

Edge computing brings computation closer to sources of data and knowledge by embedding computation in the physical space and close to the end users. Edge computing is becoming the ultimate platform where modern applications based on IoT and AI are deployed in a truly distributed manner. When edge applications require goal-oriented behaviour, AI planning comes into play as a powerful tool for achieving such behaviour. In turn, this necessitates AI planning systems that can be deployed and operate on the edge possibly on a multitude of dispersed nodes. Current approaches to distributed AI planning are mainly designed around the requirements and peculiarities of multi-agent systems, such as communication constraints and the self-interest of agents. In this work, we postulate that edge computing provides new perspectives for distributing AI planning. We propose the concept of edge AI planning where multiple AI planning components are distributed on edge nodes and communicate over a vast network. these components need to have clearly defined requirements of what can be distributed and how in order for the overall AI planning to work effectively, in turn enabling correct and consistent executions across the whole system.

关键词： AI Planning Edge AI Edge computing distributed AI Planning distributed AI

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Construction of a Load-testing Environment for distributed systems Including Idle Computers 25

Construction of a Load-testing Environment for Distributed S...

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： Nagashima, Yuya Higashino, Masayuki Kawamura, Takao Tottori University Tottori Japan

ISBN: (纸本)9798350330014

In recent years, many educational institutions have adopted information systems such as e-Learning platforms and groupware. However, these existing information systems often have significant installation, operation, and maintenance costs. Moreover, on-premise systems require expensive servers and cloud computing systems can accumulate high maintenance expenses. To address this issue, we have proposed that utilizes idle resources (idle computers) in an organization's information system. One of the characteristics of idle computers is that communication lines can be connected or disconnected at arbitrary times according to the operations of the original users. In general, the distributed system uses an idle computer as an unstable resource. However, our proposed system requires tolerating them as normal behaviors, and it requires a test environment for this problem. In this paper, we propose a load-testing environment to use idle computers having unstable behaviors in a distributed system. We also implemented and applied the proposed environment to a distributed e-Learning system. the result shows efficiency in showing behavior of unstable behaviors of idle computers to developers. © 2023 ieee.

关键词： Information systems

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Coded distributed computing with Wireless Shared Heterogeneous Workers 4

Coded Distributed Computing with Wireless Shared Heterogeneo...

引用

4th ieee international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Lei, Yu Li, Mingming Zhao, Guangquan Peng, Xiyuan Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Norinco Group Air Ammunition Research Institute Co. LTD Test Technology Department Harbin China

ISBN: (纸本)9798350361643

distributed computing can parallelize large-scale computing tasks. However, due to the straggler effect, the performance of distributed systems may be diminished. Coded distributed computing mitigates the straggler effect by introducing redundant loads. In this research, we investigate a wireless network comprising multiple masters and heterogeneous workers, with each master allocated a distinct task. We divide computing tasks into two types: long-term computing tasks and short-term computing tasks. For long-term computing tasks, we propose the Static Worker Assignment algorithm (SWA), which balances the assignment of workers and minimizes the single iteration time of all tasks. Comparing SWA with two reference benchmarks reveals significant speed improvements of up to 54.03% and 51.14% over the respective benchmarks. For short-term tasks, we propose the Dynamic Worker Assignment algorithm (DWA), which reduces task reloading time without significantly increasing single iteration time. Compared to reloading by SWA, the DWA method may result in a 0.89% increase in single iteration time, while the task reloading delay decreases by 52.70%. © 2024 ieee.

关键词： distributed computer systems

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Algorithm and Hardware Co-Design for Efficient PMCMC Acceleration on FPGA

Algorithm and Hardware Co-Design for Efficient PMCMC Acceler...

引用

9th international conference on Signal and Image Processing (ICSIP)

作者： Wang, Fulong Peng, Shiyu Gong, Yuanhao Li, Minghan Liu, Shuanglong Hunan Normal Univ Coll Hunan Prov Key Lab Low Dimens Quantum Struct & Quantum Contr Key Lab Phys & Devices Postmoore Era Changsha Peoples R China

ISBN: (纸本)9798350350920

the particle Markov-chain Monte Carlo (PMCMC) method is a stochastic algorithm that combines Particle Filters (PFs) and Markov-chain Monte Carlo (MCMC) techniques. this approach is widely used in Bayesian inference for high-dimensional state spaces and nonlinear, non-Gaussian dynamic systems. However, current PMCMC accelerators face significant challenges due to their intensive computational complexity and the intricate particle routing, limiting their application in real-time scenarios. To address these challenges, we propose a novel distributed PMCMC method that leverages parallel computing to enhance hardware execution speed. Additionally, our method introduces a particle exchange scheme that not only resolves the accuracy issues caused by particle routing in distributed PMCMC but also achieves faster computing speed. Our design is implemented on a Xilinx Kintex-7 xc7k480t FPGA device. Experimental results demonstrate that our accelerator is nearly 65x faster than CPU performance, and provides speedups up to 5x compared to existing FPGA-based accelerators.

关键词： Particle Markov-chain Monte Carlo (PMCMC) Particle Filter (PF) Field Programmable Gate Arrays (FPGAs) distributed PMCMC Hardware Acceleration

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FLEDGY: Federated Learning for Edge Devices Accommodating GPU Diversity 48

FLEDGY: Federated Learning for Edge Devices Accommodating GP...

引用

48th Annual ieee international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Prabono, Aria Ghora Goh, Yang Fan Kobayashi, Mei EAGLYS Inc Tokyo Japan

ISBN: (纸本)9798350376975;9798350376968

We propose a federated learning framework designed to effectively utilize diverse GPU architectures in edge devices. It offers wider compatibility with various GPU platforms than conventional approaches which tend to focus on CUDA-based systems for enhancing the feasibility and cost-efficiency of machine learning in edge computing scenarios. Our framework is structured for straightforward deployment, addressing the complexities of initial federated learning project setups. We also built tensor manipulation library from scratch as the core of this framework. this contribution extends the reach of federated learning, enabling more flexible and inclusive approaches in distributed machine learning environments.

关键词： federated learning machine learning edge computing

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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 Study on the Performance of distributed Storage systems in Edge computing Environments 15

A Study on the Performance of Distributed Storage Systems in...

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15th ieee international conference on Joint Cloud computing (JCC)

作者： Makris, Antonios Kontopoulos, Ioannis Xyalis, Stylianos Nektarios Psomakelis, Evangelos theodoropoulos, theodoros Varvarigos, Andreas Tserpes, Konstantinos Harokopio Univ Athens Dept Informat & Telemat Athens Greece Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

ISBN: (纸本)9798350387339

Edge computing presents a promising paradigm for the management and processing of the vast volumes of data generated by Internet of things (IoT) devices. By merging cloud services with decentralized processing at the edge of the network, edge computing optimizes resource utilization while mitigating communication overhead and data transfer delays. Despite advancements, there are issues regarding cloud/edge-based application requirements. A distributed edge storage solution is crucial, ensuring data proximity, minimizing network congestion, and adapting to changing demands. Nevertheless, implementing or selecting an efficient edge-enabled storage system presents numerous challenges due to the distributed and heterogeneous nature of the edge, as well as its limited resource capabilities. Hence, it is essential for the research community to actively contribute towards clarifying the objectives and delineating the strengths and weaknesses of different storage solutions. this work presents an overview and performance analysis of three storage solutions in the edge computing context, namely MinIO, IPFS, and BigchainDB. the evaluation considers a set of Quality of Service (QoS) and resource utilization metrics. the systems are deployed on a cluster of four Raspberry Pis, which function as a network of edge devices. the results demonstrate the superiority of IPFS and provide insights into the performance of the evaluated storage systems for edge deployments.

关键词： edge storage solutions performance evaluation minio ipfs bigchaindb distributed databases

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