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Accelerated diffusion algorithms for dynamic load balancing

INFORMATION PROCESSING LETTERS 2002年第2期84卷 61-67页

作者： Karagiorgos, G Missirlis, NM Univ Athens Dept Informat & Telecommun Athens 15784 Greece

In this paper we consider the application of accelerated techniques in order to increase the rate of convergence of the diffusive iterative load balancing algorithms. In particular, we compare the application of Semi-Iterative, Second Degree and Variable Extrapolation techniques on the basic diffusion method for various types of network graphs. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： iterative load balancing diffusion algorithms synchronous distributed processor network accelerated techniques algorithms

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Bayesian-Learning-Based diffusion Least Mean Square algorithms Over Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 13217-13231页

作者： Huang, Fuyi Zhang, Sheng Zheng, Wei Xing Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Guangxi Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

To improve the learning performance of the conventional diffusion least mean square (DLMS) algorithms, this article proposes Bayesian-learning-based DLMS (BL-DLMS) algorithms. First, the proposed BL-DLMS algorithms are inferred from a Gaussian state-space model-based Bayesian learning perspective. By performing Bayesian inference in the given Gaussian state-space model, a variable step-size and an estimation of the uncertainty of information of interest at each node are obtained for the proposed BL-DLMS algorithms. Next, a control method at each node is designed to improve the tracking performance of the proposed BL-DLMS algorithms in the sudden change scenario. Then, a lower bound on the variable step-size of each node of the proposed BL-DLMS algorithms is derived to maintain the optimal steady-state performance in the nonstationary scenario (unknown parameter vector of interest is time-varying). Afterward, the mean stability and the transient and steady-state mean square performance of the proposed BL-DLMS algorithms are analyzed in the nonstationary scenario. In addition, two Bayesian-learning-based diffusion bias-compensated LMS algorithms are proposed to handle the noisy inputs. Finally, the superior learning performance of the proposed learning algorithms is verified by numerical simulations, and the simulated results are in good agreement with the theoretical results.

关键词： Bayes methods Inference algorithms Steady-state Estimation State-space methods Approximation algorithms Transient analysis Adaptive learning networks Bayesian learning diffusion algorithms distributed estimation machine learning

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Fast diffusion load balancing algorithms on torus graphs

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12th International Euro-Par Conference on Parallel Processing

作者： Karagiorgos, Gregory Missirlis, Nikolaos M. Tzaferis, Filippos Dept Informat & Telecommun Athens 15784 Greece

ISBN: (纸本)3540377832

In this paper, we consider the application of accelerated methods in order to increase the rate of convergence of the diffusive iterative load balancing algorithms. In particular, we compare the application of Semi-Iterative, Second Degree and Variable Extrapolation techniques on the basic diffusion method and the Extrapolated diffusion method for torus graphs. It is shown that our methods require approximately 30% less iterations to reach the balanced state compared to the existed ones.

关键词： iterative load balancing diffusion algorithms distributed processor network accelerated techniques

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Analysis of a diffusion LMS Algorithm With Communication Delays for Cyclostationary White Gaussian Inputs

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2022年 8卷 960-972页

作者： Eweda, Eweda Bermudez, Jose C. M. Bershad, Neil J. Future Univ Dept Elect Engn Cairo 11835 Egypt Univ Fed Santa Catarina Dept Elect Engn BR-88040900 Florianopolis SC Brazil Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92660 USA

Available analyses of the diffusion LMS (DLMS) algorithm assume that the nodes probe the unknown system with zero delay. This assumption is unrealistic, since the unknown system is usually distant from the nodes. The present paper studies the behavior of the algorithm without this assumption. The analysis is done for a network having a central combiner. This structure reduces the dimensionality of the resulting stochastic models while preserving important diffusion properties. Communication delays between the nodes and the central combiner are also considered in the analysis. The analysis is done for system identification for cyclostationary white Gaussian nodal inputs. Mean and mean-square behaviors of the algorithm are analyzed. It is found that delays in probing the unknown system yield a bias in the algorithm without increasing its convergence time. The communication delays between the nodes and the central combiner increase the convergence time without affecting the steady-state behavior. The stability of the algorithm is not affected by either type of delay. The analysis exactly matches the simulations.

关键词： Adaptive filters analysis diffusion algorithms distributed adaptation over networks EDICS ADEL-DAN (Distributed adaptation over networks) LMS algorithm

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Robust diffusion Huber-Based Normalized Least Mean Square Algorithm with Adjustable Thresholds

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第4期39卷 2065-2093页

作者： Yu, Yi Zhao, Haiquan Wang, Wenyuan Lu, Lu Southwest Univ Sci & Technol Sch Informat Engn Robot Technol Used Special Environm Key Lab Sichu Mianyang 621010 Sichuan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China Sichuan Univ Sch Elect & Informat Engn Chengdu Peoples R China

To improve the performance of the diffusion Huber-based normalized least mean square algorithm in the presence of impulsive noise, this paper proposes a distributed recursion scheme to adjust the thresholds. Because of the decreasing characteristic of the thresholds, the proposed algorithm can also be interpreted as a robust diffusion normalized least mean square algorithm with variable step sizes so that it has not only fast convergence but also small steady-state estimation error. Based on the contaminated Gaussian model, we analyze the mean square behavior of the algorithm in impulsive noise. Moreover, to ensure good tracking capability of the algorithm for the sudden change of parameters of interest, a control strategy is given that resets the thresholds with their initial values. Simulations in various noise scenarios show that the proposed algorithm performs better than many existing diffusion algorithms.

关键词： Adjustable thresholds diffusion algorithms Impulsive noise Nonstationary control method

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AN ADAPTIVE diffusion QUATERNION LMS ALGORITHM FOR DISTRIBUTED NETWORKS OF 3D AND 4D VECTOR SENSORS

AN ADAPTIVE DIFFUSION QUATERNION LMS ALGORITHM FOR DISTRIBUT...

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21st European Signal Processing Conference (EUSIPCO)

作者： Jahanchahi, Cyrus Mandic, Danilo P. Univ London Imperial Coll Sci Technol & Med Commun & Signal Proc Res Grp London SW7 2AZ England

ISBN: (纸本)9781479936878

A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion division algebra and the widely linear model allow for a unified processing of 3D and 4D data, which can exhibit both circular and noncircular distributions. The analysis shows that the D-WLIQLMS provides a solution that is robust to link and node failures in sensor networks. Simulations on benchmark 4D signals illustrate the advantages offered by the D-WLIQLMS.

关键词： Quaternions widely linear models distributed networks diffusion algorithms

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Exploiting Heterogeneity in P2P Video Streaming

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IEEE TRANSACTIONS ON COMPUTERS 2011年第5期60卷 667-679页

作者： Couto da Silva, Ana Paula Leonardi, Emilio Mellia, Marco Meo, Michela Univ Fed Juiz de Fora Dept Comp Sci ICE BR-36036330 Juiz De Fora MG Brazil Politecn Torino Dipartimento Elettron I-10129 Turin Italy

In this paper, we investigate the impact of peer bandwidth heterogeneity on the performance of a mesh-based P2P system for live streaming. We show that bandwidth heterogeneity constitutes an important resource for P2P live streaming systems. Indeed, by effectively exploiting it, the overall performance of the system is significantly improved. This requires the adoption of smart schemes for both the overlay topology construction and chunk scheduling mechanisms that discriminate among peers based on their bandwidth.

关键词： P2P streaming systems diffusion algorithms bandwidth aware scheduling fluid models

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Chunk Distribution in Mesh-Based Large-Scale P2P Streaming Systems: A Fluid Approach

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2011年第3期22卷 451-463页

作者： Couto da Silva, Ana Paula Leonardi, Emilio Mellia, Marco Meo, Michela Univ Fed Juiz de Fora ICE Dept Comp Sci BR-36036330 Juiz De Fora MG Brazil Politecn Torino Dipartimento Elettron I-10129 Turin Italy

We consider large-scale mesh-based P2P systems for the distribution of real-time video content. Our goal is to study the impact that different design choices adopted while building the overlay topology may have on the system performance. In particular, we show that the adoption of different strategies leads to overlay topologies with different macroscopic properties. Representing the possible overlay topologies with different families of random graphs, we develop simple, yet accurate, fluid models that capture the dominant dynamics of the chunk distribution process over several families of random graphs. Our fluid models allow us to compare the performance of different strategies providing a guidance for the design of new and more efficient systems. In particular, we show that system performance can be significantly improved when possibly available information about peers location and/or peer access bandwidth is carefully exploited in the overlay topology formation process.

关键词： P2P streaming systems diffusion algorithms overlay fluid models

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Nonlinear distributed state estimation on the Stiefel manifold using diffusion particle filters

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DIGITAL SIGNAL PROCESSING 2022年 122卷 103354-103354页

作者： de Figueredo, Caio G. Bordin Jr, Claudio J. Bruno, Marcelo G. S. Inst Tecnol Aeronaut Pca Mal Eduardo Gomes 50 BR-12228900 Sao Jose Dos Campos SP Brazil Univ Fed ABC Av Estados 5001 BR-09210580 Santo Andre SP Brazil

Many relevant problems in engineering demand the estimation of dynamic system states that are constrained to non-Euclidean spaces. Furthermore, it is generally advantageous to implement estimation methods in a network of cooperating nodes instead of relying on a single data fusion center. With this in mind, we propose in this paper two new distributed particle filtering methods to track the states of a dynamic system that evolves on the Stiefel manifold, which arises naturally when the states are subject to nonlinear orthogonal constraints. The proposed algorithms are based on the Random Exchange and Adapt-then-Combine diffusion techniques, and perform parametric approximations using the matrix von Mises-Fisher distribution to compress information exchanged between network nodes. Estimates of the state are then determined via an empirical averaging method that approximates the centroid (Karcher mean) of the particle set. As we verify via numerical simulations, the proposed methods show improved performance compared to previous Particle and Extended Kalman filters designed for Euclidean state variables, and compared to a non-cooperative particle filtering algorithm. (C)& nbsp;2021 Elsevier Inc. All rights reserved.& nbsp;& nbsp

关键词： Particle filter Stiefel manifold State estimation Distributed estimation diffusion algorithms

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An Optimal Relationship-Based Partitioning of Large Datasets

An Optimal Relationship-Based Partitioning of Large Datasets

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14th East-European Conference on Advances in Databases and Information Systems

作者： Capko, Darko Erdeljan, Aleksandar Popovic, Miroslav Svenda, Goran Fac Tech Sci Novi Sad 21000 Serbia

ISBN: (纸本)9783642155758

Modern adaptive applications utilize multiprocessor systems for efficient processing of large datasets where initial and dynamic partitioning of large datasets is necessary to obtain an optimal load balancing among processors. We applied evolutionary algorithms (Genetic Algorithm and Particle Swarm Optimization) for initial partitioning, and diffusion (DR) and cut-and-paste (CP) algorithms for dynamic partitioning. Modified versions of DR and CP algorithms are developed to improve dynamic partitioning running in NUMA multiprocessor systems. The proposed algorithms were applied on datasets describing large electricity power distribution systems and experimental results prove reductions of processor load imbalance and performance improvements.

关键词： Graph partitioning diffusion algorithms NUMA

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