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partitioning Stateful Data Stream Applications in Dynamic Edge Cloud Environments

IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第4期15卷 2368-2381页

作者： Ding, Shaoshuai Yang, Lei Cao, Jiannong Cai, Wei Tan, Mingkui Wang, Zhenyu South China Univ Technol Sch Software Engn Guangzhou 510641 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518000 Guangdong Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

Computation partitioning is an important technique to improve the application performance by selectively offloading some computations from the mobile devices to the nearby edge cloud. In a dynamic environment in which the network bandwidth to the edge cloud may change frequently, the partitioning of the computation needs to be updated accordingly. The frequent updating of partitioning leads to high state migration cost between the mobile side and edge cloud. However, existing works don't take the state migration overhead into consideration. Consequently, the partitioning decisions may cause significant network congestion and increase overall completion time tremendously. In this article, with considering the state migration overhead, we propose a set of novel algorithms to update the partitioning based on the changing network bandwidth. To the best of our knowledge, this is the first work on computation partitioning for stateful data stream applications in dynamic environments. The algorithms aim to alleviate the network congestion and minimize the make-span through selectively migrating state in dynamic edge cloud environments. Extensive simulations show our solution not only could selectively migrate state but also outperforms other classical benchmark algorithms in terms of make-span. The proposed model and algorithms will enrich the scheduling theory for stateful tasks, which has not been explored before.

关键词： Cloud computing Mobile handsets partitioning algorithms Heuristic algorithms Object tracking Data models Servers Edge cloud computation partitioning stateful data stream applications

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A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space partitioning

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 51-64页

作者： Huang, Ting Gong, Yue-Jiao Chen, Wei-Neng Wang, Hua Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Victoria ISILC Melbourne Vic 8001 Australia

Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.

关键词： Sociology Statistics partitioning algorithms Topology Vegetation Probabilistic logic Optimization Binary space partition (BSP) evolutionary algorithm (EA) multimodal optimization probabilistic niching computation

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PDD: partitioning DAG-Topology DNNs for Streaming Tasks

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 9258-9268页

作者： Wu, Liantao Gao, Guoliang Yu, Jing Zhou, Fangtong Yang, Yang Wang, Tengfei East China Normal Univ Software Engn Inst Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Hong Kong Univ Sci & Technol Guangzhou IoT Thrust Guangzhou 510000 Peoples R China Wuhan Univ Technol State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China

To enable the inference of high-precision deep neural networks (DNNs) on resource-constrained devices, DNN offloading has been widely explored in recent years. Some works have also integrated the chain-topology DNN (CDNN) offloading with pipeline processing to further reduce inference delay when processing streaming tasks. To improve the accuracy of the inference results, the topology of DNN tends to evolve from chain topology to directed acyclic graph (DAG) topology. However, most of the existing works do not study partitioning and offloading DAG-topology DNNs (DDNNs) for streaming tasks. Moreover, when partitioning computationally expensive DNN models, multipartitioning probably outperforms the bi-partitioning method, and most of the works do not study multipartitioning DAG-topology DNNs. In this article, we propose a more general multipartitioning and offloading method for large-scale DDNNs to process streaming tasks, which can adaptively partition DDNNs into multiple parts considering the computing power and bandwidth of all available computing units. Specifically, we first present a transforming method based on topological sorting that can losslessly transform DAG-topology DNNs into CDNNs. Then, based on greedy and dichotomy ideas, a multipartitioning algorithm is designed to partition and offload CDNNs. In this way, we can solve DDNNs' multipartitioning problem based on the proposed transforming and partitioning algorithms. Experimentshttps://***/sreasearcher/PDD-Code show that the method proposed in this article significantly outperforms bi-partitioning and nonpartitioning methods when offloading computationally expensive DNN models.

关键词： Computational modeling Task analysis Artificial neural networks Delays Copper partitioning algorithms Topology Directed acyclic graph (DAG) topology deep neural network (DNN) partitioning streaming tasks

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EFFICIENT HEURISTIC partitioning ALGORITHM FOR PARALLEL-PROCESSING OF LARGE POWER-SYSTEMS NETWORK EQUATIONS

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1995年第6期142卷 625-630页

作者： CHAN, KW DUNN, RW DANIELS, AR Sch. of Electron. & Electr. Eng. Bath Univ. UK

An efficient heuristic algorithm for automatic partitioning of a power system network is presented. The algorithm was designed to be used in conjunction with a diakoptics-based coarse grain parallel method for solving large, sparse linear systems arising from network analysis. It exploits the techniques of factorisation path graph partitioning and equivalent post-ordering. Test results for systems up to 811 busbars and 1476 transmission lines are included for comparison purposes. This indicates that the method is able to divide a power system network model into a number of equal sized subnetworks in order to optimise the use of parallel computer systems for network analysis while the resultant number of fill-in elements is kept to a minimum.

关键词： HIGH POWER Heuristics power systems partitioning algorithms PARALLEL PROCESSING (COMPUTERS) system network Transmission Lines Clinical Test Result Network analysis

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The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph partitioning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第10期43卷 3724-3738页

作者： Wolf, Steffen Bailoni, Alberto Pape, Constantin Rahaman, Nasim Kreshuk, Anna Kothe, Ullrich Hamprecht, Fred A. Heidelberg Univ HCI IWR D-69120 Heidelberg Germany EMBL D-69117 Heidelberg Germany

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment;or a threshold;or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.

关键词： Clustering algorithms partitioning algorithms Image segmentation Image edge detection Merging Correlation Vegetation Image segmentation partitioning algorithms greedy algorithms optimization integer linear programming machine learning convolutional neural networks

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Geometric multiscale community detection: Markov stability and vector partitioning

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JOURNAL OF COMPLEX NETWORKS 2018年第2期6卷 157-172页

作者： Liu, Zijing Barahona, Mauricio Imperial Coll London Dept Math South Kensington Campus London SW7 2AZ England Imperial Coll London Dept Chem South Kensington Campus London SW7 2AZ England

Multiscale community detection can be viewed from a dynamical perspective within the Markov stability framework, which uses the diffusion of a Markov process on the graph to uncover intrinsic network substructures across all scales. Here we reformulate multiscale community detection as a max-sum length vector partitioning problem with respect to the set of time-dependent node vectors expressed in terms of eigenvectors of the transition matrix. This formulation provides a geometric interpretation of Markov stability in terms of a time-dependent spectral embedding, where the Markov time acts as an inhomogeneous geometric resolution factor that zooms the components of the node vectors at different rates. Our geometric formulation encompasses both modularity and the multi-resolution Potts model, which are shown to correspond to vector partitioning in a pseudo-Euclidean space, and is also linked to spectral partitioning methods, where the number of eigenvectors used corresponds to the dimensionality of the underlying embedding vector space. Inspired by the Louvain optimization for community detection, we then propose an algorithm based on a graph-theoretical heuristic for the vector partitioning problem. We apply the algorithm to the spectral optimization of modularity and Markov stability community detection. The spectral embedding based on the transition matrix eigenvectors leads to improved partitions with higher information content and higher modularity than the eigen-decomposition of the modularity matrix. We illustrate the results with random network benchmarks.

关键词： multiscale community detection spectral methods partitioning algorithms modularity Markov stability

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Hypergraph partitioning With Embeddings

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第6期34卷 2771-2782页

作者： Sybrandt, Justin Shaydulin, Ruslan Safro, Ilya Clemson Univ Sch Comp Clemson SC 29634 USA

Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein "hyperedges" may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. State-of-the-art algorithms that solve this problem follow the multilevel paradigm, which begins by iteratively "coarsening" the input hypergraph to smaller problem instances that share key structural features. Once identifying an approximate problem that is small enough to be solved directly, that solution can be interpolated and refined to the original problem. While this strategy represents an excellent trade off between quality and running time, it is sensitive to coarsening strategy. In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. Our approach prioritizes coarsening within self-similar regions within the input graph, and leads to significantly improved solution quality across a range of considered hypergraphs. Reproducibility: All source code, plots and experimental data are available at https://***/2019/partition.

关键词： partitioning algorithms Bipartite graph Approximation algorithms Machine learning Message systems Interpolation Contracts

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A NEURAL NETWORK DESIGN FOR CIRCUIT partitioning

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1990年第12期9卷 1265-1271页

作者： YIH, JS MAZUMDER, P Dept. of Electr. Eng. & Comput. Sci. Michigan Univ. Ann Arbor MI USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

A neural network model is proposed for circuit bipartitioning. The massive parallelism of neural nets has been successfully exploited to balance the partitions of circuit and to reduce the external wiring between the partitions. The experimental results obtained by neural nets are found to be comparable with those achieved by the C.M. Fiduccia and R.M. Mattheyses (1982) algorithm. The proposed approach can be implemented in hardware to accelerate time-consuming partitioning procedures.< >

关键词： Neural networks partitioning algorithms Wires Artificial neural networks Wiring Parallel processing Hardware Acceleration Integrated circuit interconnections Minimization

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MARKOV MODEL-BASED PHONEME CLASS partitioning FOR IMPROVED CONSTRAINED ITERATIVE SPEECH ENHANCEMENT

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 1995年第1期3卷 98-104页

作者： HANSEN, JHL ARSLAN, LM Robust Speech Processing Laboratory Department of Electrical Engineering Duke University Durham NC USA

Research has shown that degrading acoustic background noise influences speech quality across phoneme classes in a nonuniform manner, This results in variable quality performance of many speech enhancement algorithms in noisy environments. A phoneme classification procedure is proposed which directs single-channel constrained speech enhancement. The procedure performs broad phoneme class partitioning of noisy speech frames using a continuous mixture hidden Markov model recognizer in conjunction with a perceptually motivated cost-based decision process. Once noisy speech frames are identified, iterative speech enhancement based on all-pole parameter estimation with inter- and intra-frame spectral constraints is employed. The phoneme class-directed enhancement algorithm is evaluated using TIMIT speech data and shown to result in substantial improvement in objective speech quality over a range of signal-to-noise ratios and individual phoneme classes.

关键词： Speech enhancement Working environment noise partitioning algorithms Iterative algorithms Acoustic noise Speech analysis Degradation Background noise Speech processing Hidden Markov models

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partitioning of MPEG coded video bitstreams for wireless transmission

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IEEE SIGNAL PROCESSING LETTERS 1997年第6期4卷 153-155页

作者： Gharavi, H Richards, CI The Department of Electronic and Electrical Engineering Loughborough University Loughborough Leicestershire UK

This paper introduces an efficient technique for dividing a preceded Moving Pictures Expert Group-2 (MPEG-2) bitstream into an arbitrary number of partitions in decreasing order of visual importance, For a fixed partition size ratio, this has been achieved by variable-length code (VLC) interleaving within a macroblock or group of macroblocks. The algorithm is capable of recreating a syntactically correct bitstream, regardless of the error rate on secondary partitions, This renders it applicable to many efficient transmission schemes, particularly over wireless channels.

关键词： Quadrature amplitude modulation Interleaved codes Frequency synchronization Error correction Discrete cosine transforms Switches partitioning algorithms Error analysis Error probability Modulation coding

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