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

共 149581 条记录，以下是301-310 订阅

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Multi Criteria Based Container Management in a Geo-distributed Cluster 10

Multi Criteria Based Container Management in a Geo-Distribut...

引用

10th ieee international conference on Electronics, computing and Communication Technologies (ieee CONECCT)

作者： Kumar, Naveen M. R. Annappa, B. Teja, Vishnu M. NITK Dept CS&E Mangalore India NIT Dept EC&E Tadepalligudem Andhra Pradesh India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939;9798350385922

According to Gartner, 95% of workloads will shift to containers by 2025 due to its lightweight feature. Docker is a commonly used container software for binding applications;the container orchestration system Kubernetes (K8s) manages resources seamlessly across Cloud, Fog, and Edge environments through containers. However, Nodes in the cluster introduces the risk of exceeding node capacity thresholds, leading to failures and potential application loss which degrades the Quality of Service (QoS). In this regard, Multi-Criteria Decision Making (MCDM) strategy for ranking the nodes in the cluster is proposed to achieve the migration decision in the Geo-distributed cluster for both stateful and stateless application servers using K8s. The proposed strategy has achieved a 15.94sec Average service restore time for the Nginx server and 48.99sec for the Zookeeper server. A proactive Deep Learning model BI-LSTM is proposed for resource utilization prediction of the cluster and achieved MAE of 0.01928 and 0.0206 for CPU and Memory utilization.

关键词： Kubernetes Electre III TOPSIS VIKOR Check-pointing Pod/Container Migration Deep Learning BI-LSTM Resource prediction Geo-distributed Cluster

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A Multi-staining Digital Pathology Image Registration Method Based on Global and Local computing 27

A Multi-staining Digital Pathology Image Registration Method...

引用

27th ieee/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and Parallel/distributed computing (SNPD)

作者： Ding, Yi Zhang, Xin Lu, Cheng Commun Univ China Sch Comp & Cyber Sci Beijing Peoples R China Guangdong Prov Peoples Hosp Dept Radiol Guangzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology Image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processing images with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.

关键词： digital pathological images global rigid registration local non-rigid registration feature point detection

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Holium: A Protocol for Data Transformation Pipelines 42

Holium: A Protocol for Data Transformation Pipelines

引用

42nd ieee international conference on distributed computing systems (ICDCS)

作者： Metais, Philippe Polyphene Paris France

The present file introduces a tutorial planned at the DINPS 22 workshop. The tutorial will cover Holium, a protocol based on IPFS, IPLD and WebAssembly, dedicated to data transformation pipelines. It will be provided ... 详细信息

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

关键词： Protocols conferences Pipelines distributed databases Tutorials

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Asynchronous Federated Split Learning 8

Asynchronous Federated Split Learning

引用

8th ieee international conference on Fog and Edge computing (ICFEC)

作者： Albuquerque, R. A. Dias, L. P. Ziazet, Junior Momo Vandikas, K. Ickin, S. Jaumard, B. Natalino, C. Wosinska, L. Monti, P. Wong, E. Concordia Univ Comp Sci & Software Engn Montreal PQ Canada Ericsson ER AI Kista Sweden Ericsson GAIA Montreal PQ Canada Chalmers Univ Technol Opt Networks Gothenburg Sweden Univ Melbourne Elect & Elect Engn Melbourne Vic Australia

ISBN: (纸本)9798350361360;9798350361353

We propose a first Asynchronous Federated Split Learning (AFSL), to add the flexibility of asynchronous computing to the combination of federated and split learning. This amounts to designing a harmonious combination of different paradigms in order to benefit from the advantages of each of them and to reduce the impacts of their shortcomings. This way, AFSL answers to the increasingly rising interest for distributed algorithms with the advent of edge computing in order to support new market segments, such as cloud gaming, immersive eXtended Reality (XR), indoor positioning, and mission critical IoT networks, with stringent requirements on latency and reliability. Computational experiments are conducted on IID and nonIID datasets to investigate the added value of the asynchronous feature. Results indicate that AFSL can accelerate model learning by up to 86% without sacrificing the model's convergence and accuracy. Indeed, not only average training times are reduced, but clients use fewer resources, a critical characteristic for devices with limited computing capabilities, e.g., in edge devices. Performance degradation can be mitigated by a careful selection of the aggregation principle. Other advantages are with AFSL training in dynamic scenarios as it provides robustness with a short recovery time by leveraging asynchronous client training. Index Terms-distributed machine learning, Federated learn-

关键词： distributed machine learning Federated learning Split learning Asynchronous Edge computing

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Hierarchical Heterogeneous Cluster systems for Scalable distributed Deep Learning 27

Hierarchical Heterogeneous Cluster Systems for Scalable Dist...

引用

27th international Symposium on Real-Time distributed computing (ISORC)

作者： Wang, Yibo Geng, Tongsheng Silva, Ericson Gaudiot, Jean-Luc Univ Calif Irvine Elect Engn & Comp Sci Irvine CA 92717 USA Pontificia Univ Catolica Minas Gerais Elect Engn & Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9798350371291;9798350371284

distributed deep learning framework tools should aim at high efficiency of training and inference of distributed exascale deep learning algorithms. There are three major challenges in this endeavor: scalability, adaptivity and efficiency. Any future framework will need to be adaptively utilized for a variety of heterogeneous hardware and network environments and will thus be required to be capable of scaling from single compute node up to large clusters. Further, it should be efficiently integrated into popular frameworks such as TensorFlow, PyTorch, etc. This paper proposes a dynamically hybrid (hierarchy) distribution structure for distributed deep learning, taking advantage of flexible synchronization on both centralized and decentralized architectures, implementing multi-level fine-grain parallelism on distributed platforms. It is scalable as the number of compute nodes increases, and can also adapt to various compute abilities, memory structures and communication costs.

关键词： distributed Allreduce Framework Deep Learning Hierarchical Heterogeneous Cluster

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Analyzing the Impact of Cellular Network Delay on distributed Optimization of the Distribution Grid

Analyzing the Impact of Cellular Network Delay on Distribute...

引用

2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Inaolaji, Adedoyin Malandra, Filippo SUNY Buffalo Dept Elect Engn Buffalo NY USA

ISBN: (纸本)9798350318562;9798350318555

distributed optimization algorithms have revolutionized the decision-making process in distribution network management. Unlike their centralized counterparts, the effectiveness of distributed algorithms is significantly affected by the non-ideal states of communication networks used for data exchange. Hence, evaluating the resilience of distributed algorithms to communication imperfections is essential. Regarding this, this work examines the effectiveness of a distributed algorithm, named alternating direction method of multipliers (ADMM), in a three-phase unbalanced distribution system under the network delays of three popular cellular communication technologies. The performance assessment of the algorithm is done using the ieee 123-bus test system by optimally scheduling the inverters connected to the network to achieve voltage deviation minimization. This analysis offers valuable understanding about the efficacy of the ADMM algorithm in terms of the solution quality, number of iterations, convergence rate and update frequency.

关键词： distributed Optimization Communication Delay Optimal Power Flow Distribution Grids

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SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split computing 43

SplitBeam: Effective and Efficient Beamforming in Wi-Fi Netw...

引用

43rd ieee international conference on distributed computing systems (ICDCS)

作者： Bahadori, Niloofar Matsubara, Yoshitomo Levorato, Marco Restuccia, Francesco Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA 92697 USA Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

ISBN: (纸本)9798350339864

Modern ieee 802.11 (Wi-Fi) networks extensively rely on multiple-input multiple-output (MIMO) to significantly improve throughput. To correctly beamform MIMO transmissions, the access point needs to frequently acquire a beamforming matrix (BM) from each connected station. However, the size of the matrix grows with the number of antennas and subcarriers, resulting in an increasing amount of airtime overhead and computational load at the station. Conventional approaches come with either excessive computational load or loss of beamforming precision. For this reason, we propose SplitBeam, a new framework where we train a split deep neural network (DNN) to directly output the BM given the channel state information (CSI) matrix as input. The DNN is designed with an additional "bottleneck" layer to "split" the original DNN into a head model and a tail model, respectively executed by the station and the access point. The head model generates a compressed representation of the BM, which is then used by the AP to produce the BM using the tail model. We formulate and solve a bottleneck optimization problem (BOP) to keep computation, airtime overhead, and bit error rate (BER) below application requirements. We perform extensive experimental CSI collection with off-the-shelf Wi-Fi devices in two distinct environments and compare the performance of SplitBeam with the standard ieee 802.11 algorithm for BM feedback and the state-of-the-art DNN-based approach LB-SciFi. Our experimental results show that SplitBeam reduces the beamforming feedback size and computational complexity by respectively up to 81% and 84% while maintaining BER within about 10(-3) of existing approaches. We also implement the SplitBeam DNNs on FPGA hardware to estimate the end-to-end BM reporting delay, and show that the latter is less than 10 milliseconds in the most complex scenario, which is the target channel sounding frequency in realistic multiuser MIMO scenarios. To allow full reproducibility, we will r

关键词： Split computing MIMO ieee 802.11 Wi-Fi Beamforming Experiments

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Distributing Compilation to Enable High Throughput Scalable Quantum Workloads 5

Distributing Compilation to Enable High Throughput Scalable ...

引用

2024 international conference on Quantum computing and Engineering

作者： Waring, Harry J. Oxford Quantum Circuits Reading Berks England

ISBN: (纸本)9798331541378

The increasing quality and availability of Quantum Processing Units (QPUs) is fueling a growing interest in quantum computing across many technological areas. The resulting increase in demand for QPU resources necessitates Quantum computing as a Service (QCaaS) providers to support a high throughput of quantum workloads. A major runtime bottleneck in current QCaaS software stacks is the computationally-intensive compilation step which requires significant compute. To address this, Oxford Quantum Circuits has introduced distributed compilation whereby quantum programs are compiled in parallel and stored until the QPU is available. This has replaced our previous serial compilation approach where each program was compiled immediately prior to execution. From experiments using our production compilers and a simulated backend representing the QPU, we show that distributed compilation has resulted in a 78% reduction in processing time as compared to serial compilation. This demonstrates that there are sizeable performance gains to program throughput attainable through the introduction of distributed compilation into a QCaaS architecture. We posit that the usefulness of this feature will only grow given the increasing complexity of quantum programs and the growing popularity of quantum -classical hybrid algorithms.

关键词： distributed Compilation Quantum computing Quantum computing as a Service Quantum systems Software

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset 14

Intrusion Detection at the Edge Computing: A Deep Learning A...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Kumar, Vipin Kumar, Vivek Bhadauria, Abhay Pratap Singh Dixit, Jay Kumar, Amit Haridwar India Department of Computer Engineering & Applications GLA University UP Mathura India

ISBN: (纸本)9798331531935

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments. © 2025 ieee.

关键词： Edge computing

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Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources 27

Energy-Aware AI-based Optimal Cloud Infra Allocation for Pro...

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27th ieee/ACIS international conference on Software Engineering, Artificial Intelligence, Networking and Parallel/distributed computing (SNPD)

作者： Agomuo, Okechukwu Clement Jnr, Osei Wusu Brempong Muzamal, Junaid Hussain Univ Utah Dept Operat & Informat Syst Salt Lake City UT 84132 USA Univ Utah Khalert Sch Comp Salt Lake City UT USA Fast NUCES Fast Sch Comp Lahore Pakistan

ISBN: (纸本)9798350391961;9798350391954

This paper presents a comprehensive study on optimizing resource allocation in cloud computing environments using an ensemble of machine learning techniques and optimization algorithms. We developed a multifaceted approach, integrating Long Short-Term Memory (LSTM) networks for forecasting resource demands, Particle Swarm Optimization (PSO) for initial resource allocation, Q-learning for dynamic resource adjustment, and Linear Regression (LR) for predicting energy consumption. Our LSTM model demonstrated high accuracy in demand forecasting, with detailed performance metrics indicating its effectiveness in diverse scenarios. The PSO algorithm significantly enhanced the efficiency of resource distribution, evidenced by a reduction in the number of utilized units. Q-learning contributed to the system's adaptability, optimizing resource allocation based on changing demands in real-time. The LR model accurately predicted energy consumption, aligning closely with observed data and highlighting the potential for energy-efficient cloud management.

关键词： Cloud computing Resource Allocation Machine Learning LSTM Particle Swarm Optimization Q-learning Linear Regression Energy Efficiency Optimization Algorithms Cloud Resource Management

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