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检索条件"主题词=Partitioning Algorithms"

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SWISP: Distributed Convoy Mining via Sliding Window-based Indexing and Sub-track partitioning

SWISP: Distributed Convoy Mining via Sliding Window-based In...

引用

International Conference on Data Engineering

作者： Chenxu Wang Xin Yang Tianyi Li Jiaxing Wei Pinghui Wang Hongzhen Xiang Christain S. Jensen School of Software Engineering MOE Key Lab of INNS Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Department of Computer Science Aalborg University Aalborg Denmark MOE Key Lab of INNS Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the widespread deployment of location-aware mobile devices, a mass of trajectory data is being generated and collected. Mining co-movement patterns of people and vehicles from streaming and massive trajectory data has attracted much attention due to its wide applications in various fields. As a typical co-movement pattern, convoys describe objects moving together in consecutive timestamps. There are two challenges for efficient distributed convoy mining: object clustering and workload balancing. Clustering objects in each time snapshot is a time-consuming operation. In addition, on the basis of practical application scenarios, load balancing is an important consideration for distributed algorithms. To tackle the above challenges, we propose a novel method for distributed convoy mining via sliding window-based indexing and sub-track partitioning, abbreviated SWISP. We offer three major advancements. First, we develop a grid-based DBSCAN clustering algorithm named Grid-DBSCAN for distributed scenarios. It avoids the exhaustive calculation of pairwise distances for neighborhood search and thus improves computational efficiency in the clustering stage. Second, we propose a sliding window-based indexing scheme to filter out sub-tracks with less than $k$ consecutive time snapshots, significantly reducing the number of candidate sub-tracks for convoy mining. Third, we develop a distributed convoy mining algorithm based on sub-track partitioning. It exploits both temporal and spatial information of sub-tracks for data partitioning and solves the data skewness problem caused by uneven data distributions. We conduct extensive experiments on four real-world datasets. The experimental results show that our distributed algorithm can handle large-scale trajectory data and is more efficient than state-of-the-art approaches.

关键词： Clustering algorithms Filtering algorithms Load management Information filters Mobile handsets Trajectory partitioning algorithms

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PartFlow: A Visualization Tool for Application partitioning and Workload Offloading in Mobile Edge Computing

PartFlow: A Visualization Tool for Application Partitioning ...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Boda Li Minghao Li Jian Zhao Wei Cai University of Washington Tacoma WA United States Nanyang Technological University Singapore University of Waterloo Waterloo ON Canada

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

In mobile edge computing (MEC), one optimization strategy for mobile applications is to offload heavy computing tasks to cloud and edge servers. Constructing partitioning algorithms involves modeling individual methods through static code profilers, but exploiting dynamic user-driven execution patterns is also crucial. This paper introduces PartFlow, an interactive visualization system that supports comprehensive analysis of mobile application components and aids researchers in developing partitioning and offloading algorithms using real human behavioral data. PartFlow collects application component data remotely through binary instrumentation of mobile applications. Interactive diagrams are designed to evaluate component performance and illustrate transition patterns using the collected data. Additionally, PartFlow integrates a deep learning (DL)-based approach for multi-step forecasting of component states to improve accuracy and user experience in algorithm design. A case study and user feedback demonstrate PartFlow’s effectiveness in assisting researchers and engineers in creating offloading strategies.

关键词： Human computer interaction Multi-access edge computing Heuristic algorithms Data visualization Stochastic processes User experience partitioning algorithms Mobile applications Forecasting Usability

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A Load Balancing Network Based on Dynamic Traffic partitioning

A Load Balancing Network Based on Dynamic Traffic Partitioni...

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International Conference on Computer and Communications (ICCC)

作者： Jun Tao XinYu Wang Huihui Hang Wangfeng Yu School of Computer and Software Engineering Anhui Institute of Information Engineering WuHu China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Modern networks require high capacity, robustness, and error tolerance, but the currently popular IGP single home network cannot possess these features. an EBGP network with double exits are proposed, it can achieve load balancing of traffic and send network business data quickly and securely. At the same time, a bandwidth partitioning algorithm was developed, the algorithm divides traffic based on the MED and local preference attributes of the EBGP protocol to achieve load balancing of network traffic. Finally, a network system was constructed, and all operational parameters such as latency, jitter, and packet loss were normal, the results well met the needs of the network.

关键词： Packet loss Telecommunication traffic Jitter Load management Routing Routing protocols Robustness partitioning algorithms Network systems Load modeling

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A Proposal for Blockchain Account Network partitioning Optimization

A Proposal for Blockchain Account Network Partitioning Optim...

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International Technical Conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Ikumi TANABE Koichi GYODA Graduate School of Engineering and Science Shibaura Institute of Technology

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Blockchain technology, originally developed to support cryptocurrencies, faces a barrier to wider adoption because of its limited processing throughput. Sharding is garnering attention as one of the solutions to address this problem. There are two primary issues associated with implementing sharding in blockchain. The first issue is the high ratio of cross-shard transactions (TXs) to all TXs, and the second issue is an imbalance in TX processing volumes across the shards. These issues are in a trade-off relationship that could significantly reduce blockchain throughput. In this paper, we propose an account network partitioning algorithm based on soft clustering using Stochastic Gradient Descent (SGD) to achieve more optimal sharding. The proposed algorithm is implemented, and the results of evaluation experiments using real transaction data are described.

关键词： Measurement Sharding Clustering algorithms Throughput partitioning algorithms Blockchains Cryptocurrency

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Dynamic Cache partitioning for Enhancing Parallel I/O Performance in NVMe SSDs

Dynamic Cache Partitioning for Enhancing Parallel I/O Perfor...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Zecheng Li Guancheng Li Songhui Cao Shu Yin Xiaojun Ruan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science California State University CA

ISBN: (数字)9798331520502

ISBN: (纸本)9798331520519

Solid State Drive cache, implemented as on-board shared DRAM memory, can significantly enhance 110 performance by caching frequently accessed data. Although SSD caching strategies for single 110 data flows have been extensively explored, studies on cache partitioning to optimize parallel 110 in an SSD are scarce. In this paper, we present a novel dynamic cache partitioning approach designed to improve overall performance of multi-parallel 110 data flows by minimizing per-formance degradation of cache pollution and resource contention. By dynamically adjusting cache partition sizes for each data flow by considering cache sensitivity on performance, our strategy seeks to determine the optimal cache partition sizes to maximize overall 110 throughput. We implemented the strategy in the SSD simulator MQSim and evaluated its performance using various synthetic and real-world workloads. Our experimental results indicate that our dynamic cache partitioning strategy achieves an overall throughput increase of up to 33.22 % compared to shared cache methods and outperforms static cache partitioning strategies by up to 21.19%.

关键词： Sensitivity Pollution Nonvolatile memory Solid state drives Heuristic algorithms Random access memory Throughput partitioning algorithms Resource management Optimization

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Intelligent Reflecting Array Phase Mapping Method with One-Dimensional Coverage partitioning

Intelligent Reflecting Array Phase Mapping Method with One-D...

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International Conference on Information Networking

作者： Jaehong Kim Jingon Joung School of Electrical and Electronics Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This study proposes the intelligent reflecting array (IRA) phase mapping method that can directly map the user equipment (UE) position to the IRA phase shift (PS) vector. To construct the mapping between the UE position and the IRA PS vector, we partition the coverage into multiple regions, each with an anchor. The IRA PS vector corresponding to each anchor position is stored offline. The designed mapping is used online to choose the IRA PS vector from the UE’s position feedback. Numerical results verified that the IRA phase mapping method with the generalized Lloyd algorithm (GLA)-based partitioning converges to the performance upper bound as the number of regions increases. Also, uniform angle-based partitioning can achieve comparable performance to GLA-based partitioning.

关键词： Phased arrays Upper bound Vectors partitioning algorithms Signal to noise ratio

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DACA: A Distributed Algorithm for Task partitioning and Offloading in Mobile Edge Computing Networks Supporting Transformer

DACA: A Distributed Algorithm for Task Partitioning and Offl...

引用

Smart World Congress (SWC), IEEE

作者： Yehan Deng Jingjing Li Na Tang School of Computer South China Normal University Guangzhou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Recent studies have explored collaborative Transformer-based inference in edge computing (EC), but they often overlook the mobility of users and edge devices, leading to potential reliability issues. This paper aims to minimize inference latency in mobile edge computing (MEC) by considering heterogeneity in mobility, computation, and communication. We propose a task partitioning model utilizing the GPipe scheme for Transformer-based inference. The task partitioning and offloading problem is then formulated with constraints on computation resources and mobility, decomposed into a bin-packing problem and an integer optimization problem. To solve these subproblems, we introduce the Distributed Aggregated Competition Algorithm (DACA). Extensive simulations and testbed experiments demonstrate the high performance of our proposed algorithm in minimizing inference latency across heterogeneous mobile edge devices and networks.

关键词： Performance evaluation Multi-access edge computing Computational modeling Simulation Transformers Inference algorithms partitioning algorithms Reliability Distributed algorithms Optimization

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Microprocessor Design Space Exploration via Space partitioning and Bayesian Optimization

Microprocessor Design Space Exploration via Space Partitioni...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Zijun Jiang Yangdi Lyu Microelectronics Thrust The Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Design space exploration (DSE) has long been a very important topic in electronic design automation (EDA), but the growing diversity of applications and the complexity of integrated circuits make conventional DSE frameworks less effective and efficient. Therefore, an exploration algorithm that can find the optimal designs with fewer samples is demanded. This paper proposes a DSE framework for microprocessors that integrates a novel optimization algorithm with EDA flows. The proposed optimization algorithm utilizes space partitioning and Bayesian optimization to explore diverse and high-dimensional design spaces in microprocessors efficiently. Using the framework, we explore the design space of VexRiscv CPUs for TinyML workloads, where our proposed optimization algorithm obtains more Pareto-optimal designs and higher hypervolume with fewer samples.

关键词： Design automation Microprocessors Space exploration partitioning algorithms Bayes methods Complexity theory Optimization

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Optimal Power Flow Based on Area partitioning Method for Power Grids

Optimal Power Flow Based on Area Partitioning Method for Pow...

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International Conference on Information Science and Technology (ICIST)

作者： Zhiqiang Ma Fei Liang Qi Yang Bing Chen Mei Zhou Yu Na Metrology Centre Marketing Service Center State Grid Ningxia Electric Power Co. Yinchuan China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

The power system is gradually orienting to large-scale distributed computing, and the trend of area interconnection is becoming more and more obvious. It is still a classic prob-lem to calculate power flow by using distributed optimization. Compared with traditional centralized power flow calculation, it has obvious advantages with fast computing speed and less information between areas. In this paper, we define the boundary voltage coupling equation with auxiliary variables, to solve the non-convex subproblem in the iterative process of ADMM, and match the optimal area partitioning for ADMM by proposing the optimal spectral clustering (OSC) method. Specifically, the OSC scheme takes the Jacobian matrix, admittance matrix, and voltage pivot vector as the the joint adjacency matrix to measure the similarity of spectral clustering algorithm into consideration, which is obviously better than the traditional spectral clustering method considering admittance matrix only. Combining with ADMM algorithm, IEEE-118 bus system is used as a test example to perform the distributed calculation after area partitioning, the experimental results verify the good performance of proposed spectral clustering method in distributed power grid.

关键词： Jacobian matrices Voltage measurement Clustering methods Clustering algorithms Power grids Vectors Mathematical models partitioning algorithms Admittance Load flow

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2FWL-SIRGN: A Scalable Structural 2-dimensional Folklore Weisfeiler Lehman Graph Representation Learning Approach Via Structural Graph partitioning

2FWL-SIRGN: A Scalable Structural 2-dimensional Folklore Wei...

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IEEE International Conference on Big Data

作者： Justin Carpenter Edoardo Serra Computer Science Dept. Boise State University Boise Idaho United States

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Graph representation learning has numerous applications, ranging from social networks to bioinformatics, with a major focus on Graph Neural Networks (GNNs). However, many GNN models face challenges in capturing intricate graph structures, such as cycles, and are prone to overfitting and high computational costs, limiting their scalability on medium to big *** this paper, we propose 2FWL-SIRGN, a novel approach that integrates higher-order Weisfeiler-Lehman (WL) test algorithm while mitigating its computational challenges. Our method combines the Structural Iterative Representation Learning for Graph Nodes (SIRGN) framework with the 2-dimensional Folklore Weisfeiler-Lehman (2FWL) isomorphism test. The unsupervised training of the SIRGN component improves the model’s resistance to overfitting, while the 2FWL component enhances its expressive power, enabling it to capture complex patterns, such as cycle structures. However, the inclusion of 2FWL increases computational overhead. To address this, we introduce a Structural Graph partitioning algorithm, which allows 2FWL-SIRGN to scale efficiently to big *** experiments demonstrate that 2FWL-SIRGN outperforms state-of-the-art methods by addressing key challenges in graph representation learning. Our model captures richer structural information while maintaining computational efficiency, surpassing other higher-order WL approaches. Additionally, our partitioning strategy enables 2FWL-SIRGN to effectively handle large-scale graphs, and its inherent resistance to overfitting addresses a common limitation of GNNs. These advancements position 2FWL-SIRGN as a robust solution for real-world applications where both scalability and accuracy are critical.

关键词： Representation learning Social networking (online) Scalability Computational modeling Biological system modeling partitioning algorithms Computational efficiency Bioinformatics Overfitting Immune system

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