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International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)

作者： Dipika S. Diwase Department of Computer Engineering SSGMCE Shegaon India

Cloud computing is a very important feature in which application resources are provided over the internet as a service to users that discovering its utilization all over. Mobile Cloud Computing (MCC) combines the features of mobile computing and cloud computing for improving performance. Mobile computing and cloud computing technologies has drastically changed the current perspective on distributed computing. In mobile cloud computing, programmer offloads the entire application from mobile devices to computational clouds where applications are partitioned at different levels of granularity for distributed application processing. Application partitioning is used to separate the intensive components of the mobile applications which operate independently in the distributed processing environment. In this paper, we will discuss computational offloading process of resources to cloud and different application partitioning algorithms for offloading in mobile cloud computing. Here, we give a review of application partitioning schemes in mobile cloud computing highlighting important features and key issues in this area. In this paper, we also discuss about challenges in partitioning of elastic application selecting appropriate research domains and exploring technique of distributed application processing in MCC that offering more data reliability.

关键词： Cloud computing partitioning algorithms Mobile communication Mobile handsets Algorithm design and analysis Computational modeling Mobile computing

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K-SpecPart: Supervised Embedding algorithms and Cut Overlay for Improved Hypergraph partitioning

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第4期43卷 1232-1245页

作者： Bustany, Ismail Kahng, Andrew B. Koutis, Ioannis Pramanik, Bodhisatta Wang, Zhiang Adv Micro Devices Inc Adapt & Embedded Comp Grp AECG Santa Clara CA 95054 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Univ Calif San Diego Dept Elect Engn La Jolla CA 92093 USA

State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on each level of the hierarchy. Multilevel partitioners are subject to two limitations: 1) hypergraph coarsening processes rely on local neighborhood structure without fully considering the global structure of the hypergraph and 2) refinement heuristics risk entrapment in local minima. In this article, we describe K-SpecPart, a supervised spectral framework for multiway partitioning that directly tackles these two limitations. K-SpecPart relies on the computation of generalized eigenvectors and supervised dimensionality reduction techniques to generate vertex embeddings. These are computational primitives that are not only fast, but embeddings also capture global structural properties of the hypergraph that are not explicitly considered by existing partitioners. K-SpecPart then converts the vertex embeddings into multiple partitioning solutions. Unlike multilevel partitioners that only consider the best solution, K-SpecPart introduces the idea of "ensembling" multiple solutions via a cut-overlay clustering technique that often enables the use of computationally demanding partitioning methods such as integer linear programming (ILP). Using the output of a standard partitioner as a supervision hint, K-SpecPart effectively combines the strengths of established multilevel partitioning techniques with the benefits of spectral graph theory and other combinatorial algorithms. K-SpecPart significantly extends ideas and algorithms that first appeared in our previous work on the bipartitioner SpecPart (Bustany et al., ICCAD 2022). Our experiments demonstrate the effectiveness of K-SpecPart. For bipartitioning, K-SpecPart produces solutions with up to similar to 15% cutsize improvement over SpecPart. For multiway partitioning, K-SpecPart produces solutions with up to similar to 20% cutsize improvement

关键词： Hypergraph partitioning partitioning algorithms physical design (EDA) spectral partitioning

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partitioning algorithms for improving efficiency of topic modeling parallelization

Partitioning algorithms for improving efficiency of topic mo...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Hung Nghiep Tran Atsuhiro Takasu VNU-HCMC University of Information Technology Vietnam National Institute of Informatics SOKENDAI (The Graduate University for Advanced Studies) Japan

ISBN: (纸本)9781467377898

Topic modeling is a very powerful technique in data analysis and data mining but it is generally slow. Many parallelization approaches have been proposed to speed up the learning process. However, they are usually not very efficient because of the many kinds of overhead, especially the load-balancing problem. We address this problem by proposing three partitioning algorithms, which either run more quickly or achieve better load balance than current partitioning algorithms. These algorithms can easily be extended to improve parallelization efficiency on other topic models similar to LDA, e.g., Bag of Timestamps, which is an extension of LDA with time information. We evaluate these algorithms on two popular datasets, NIPS and NYTimes. We also build a dataset containing over 1,000,000 scientific publications in the computer science domain from 1951 to 2010 to experiment with Bag of Timestamps parallelization, which we design to demonstrate the proposed algorithms' extensibility. The results strongly confirm the advantages of these algorithms.

关键词： partitioning algorithms Algorithm design and analysis Load management Load modeling Approximation algorithms Parallel algorithms Heuristic algorithms

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NSshard: Low-Cross-Shard Sharding via Account partitioning for Blockchain-Based IoT

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IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 9019-9030页

作者： Yin, Bo Xie, Qianwen Xu, Rongwei Gu, Ke Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China

The Internet of Things (IoT) links the physical world to computing systems, and blockchain presents an opportunity to address the issues of weak interoperability and security flaws within IoT. However, blockchain faces the challenge of low throughput and scalability. Sharding is a promising solution, but it divides the blockchain into multiple committees, making the attack cost of malicious nodes lower. Sharding also leads to a large number of cross-committee transactions, which degrades the system's performance. In this article, we propose the NSshard sharding framework that provides secure and low-cross-committee scaling. NSshard consists of network sharding and state sharding. We first propose a reputation score-based network sharding, which assigns each node a reputation score to reward its honest verification of transactions and penalizes its malicious behavior. This network sharding uses a random but balanced distribution of reputation scores, thereby decreasing the risk of collusion. We also propose a graph-based account partitioning scheme for state partitioning. To reduce the amount of cross-committee transactions, the scheme uses an undirected weighted graph to depict accounts and transactions. We design two algorithms based on edge splitting and overlapping community discovery, respectively. We also propose a dynamic sharding method to handle new transactions. We conduct extensive experiments to evaluate the efficiency of the proposed framework based on Ethereum transaction data. The experimental results show that our proposed framework can reduce the number of cross-committee transactions by 34.8% at 128 committees compared to the Metis algorithm.

关键词： Sharding Blockchains Security Internet of Things Throughput Heuristic algorithms Resource management partitioning algorithms Delays Costs Blockchain efficient communications and networking network architecture sharding

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A Fundamental-Diagram-Informed Spatial partitioning Method for Heterogeneous Traffic Networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 12193-12205页

作者： Ding, Fan Zhao, Yu Tan, Huachun Liu, Zhao Pu, Ziyuan Southeast Univ Sch Transportat Nanjing 210000 Jiangsu Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China

Spatially partitioning heterogeneous traffic networks into multiple subnetworks is crucial for practical tasks, such as distributed signal control and model parallel processing. Existing partitioning methods that account for traffic characteristics over a certain period typically require calculating similarities between the time series of all sensors. Due to the quadratic increase in complexity with network size, these methods are inefficient for large-scale networks and extended time periods. Additionally, calculating similarities requires complete data, making such methods highly sensitive to missing data and lacking robustness. To address these issues, this article proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into subclusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each subcluster to extract fundamental diagram information. Then, this information is used to perform secondary clustering to form clusters. Finally, the cluster boundaries are fine-tuned to produce subnetworks with smooth boundaries. The proposed method calculates the parameter similarity between subclusters instead of the time series similarity between all sensors. This reduces computational costs and effectively handles data missing. A case study using real-world data verifies the effectiveness of the proposed method and its stability in the presence of missing data. Compared to spectral clustering, the total within-cluster variance and NcutSilhouette metric decrease by 5.7% and 17.8%, respectively. The proposed method enhances distributed or parallel tasks on traffic networks by providing stable and meaningful partitioning results. This method is beneficial for the analysis and effective management of complex heterogeneous traffic networks.

关键词： Roads Time series analysis Sensor phenomena and characterization Clustering algorithms Robustness Internet of Things Training partitioning algorithms Computational complexity Transportation Fundamental diagram (FD) K-medoids clustering spatial partitioning traffic network

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DNN partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 1945-1962页

作者： Liu, Zhang Du, Hongyang Lin, Junzhe Gao, Zhibin Huang, Lianfen Hosseinalipour, Seyyedali Niyato, Dusit Xiamen Univ Dept Informat & Commun Engn Xiamen 361102 Fujian Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Jimei Univ Nav Inst Xiamen 361021 Peoples R China SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial computation resources, which are beyond the capability of a single vehicle. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering computing services for DNN-based tasks through resource pooling via Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. To this end, we first leverage a Lyapunov optimization technique to decouple the original long-term optimization with stability constraints into a per-slot deterministic problem. Afterwards, we propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models to determine the optimal DNN partitioning and task offloading decisions. Furthermore, we integrate convex optimization techniques into MAD2RL as a subroutine to allocate computation resources, enhancing the learning efficiency. Through simulations under real-world movement traces of vehicles, we demonstrate the superior performance of our proposed algorithm compared to existing benchmark solutions.

关键词： Resource management Vehicle dynamics Optimization Computational modeling Diffusion models Heuristic algorithms Artificial neural networks partitioning algorithms Dynamic scheduling Deep reinforcement learning DNN partitioning deep reinforcement learning diffusion model Lyapunov optimization resource allocation task offloading vehicular networks

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Two-Dimensional Balanced partitioning and Efficient Caching for Distributed Graph Analysis

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第2期36卷 133-149页

作者： Lin, Shuai Wang, Rui Li, Yongkun Xu, Yinlong Lui, John C. S. Univ Sci & Technol China Hefei 230026 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Univ Sci & Technol China AnHui Prov Key Lab High Performance Comp Hefei 230026 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Distributed graph analysis usually partitions a large graph into multiple small-sized subgraphs and distributes them into a cluster of machines for computing. Therefore, graph partitioning plays a crucial role in distributed graph analysis. However, the widely used existing graph partitioning schemes balance only in one dimension (number of edges or vertices) or incur a large number of edge cuts, so they degrade the performance of distributed graph analysis. In this article, we propose a novel graph partition scheme BPart and two enhanced algorithms BPart-C and BPart-S to achieve a balanced partition for both vertices and edges, and also reduce the number of edge cuts. Besides, we also propose a neighbor-aware caching scheme to further reduce the number of edge cuts so as to improve the efficiency of distributed graph analysis. Our experimental results show that BPart-C and BPart-S can achieve a better balance in both dimensions (the number of vertices and edges), and meanwhile reducing the number of edge cuts, compared to multiple existing graph partitioning algorithms, i.e., Chunk-V, Chunk-E, Fennel, and Hash. We also integrate these partitioning algorithms into two popular distributed graph systems, KnightKing and Gemini, to validate their impact on graph analysis efficiency. Results show that both BPart-C and BPart-S can significantly reduce the total running time of various graph applications by up to 60% and 70%, respectively. In addition, the neighbor-aware caching scheme can further improve the performance by up to 24%.

关键词： partitioning algorithms Computational modeling Synchronization Costs Analytical models Social networking (online) Reviews Load modeling Clustering algorithms Blogs Distributed graph systems graph partition graph algorithms graph processing

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ParSGCN: Bridging the Gap Between Emulation partitioning and Scheduling

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第3期44卷 1180-1192页

作者： Wang, Ziyi Zhao, Wenqian Pu, Yuan Chen, Lei Thong, Wilson Wang Kit Sheng, Weihua Ho, Tsung-Yi Yu, Bei Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Huawei Technol Noahs Ark Lab Hong Kong Peoples R China Huawei HiSilicon Design Automat Lab Hong Kong Peoples R China

Efficient functional verification is crucial in the very-large-scale integration (VLSI) design flow. Existing processor-based emulation systems suffer from low efficiency due to the gap between partitioning and scheduling during compilation. To address the above concern, we propose ParSGCN, a scheduling-friendly emulation compilation flow that considers the objective of scheduling during partitioning. To incorporate the hard-to-perceive look-ahead information about scheduling, we embed it into a net cut probability distribution, which is easier to utilize. We estimate this probability distribution using a tailored variant of graph convolutional network (GCN) that is trained through a customized loss function and a large dataset of real-world compilation solutions. Additionally, we have developed a set of novel techniques to guide the emulation partitioning process using the estimated probability distribution. The proposed method is integrated into an industrial emulator and evaluated on large-scale designs with up to over 100 million cells. Comprehensive experimental results demonstrate the effectiveness of ParSGCN, showcasing an average improvement of 16.38%, 26.04%, and 19.52% in the best, worst, and median solution quality, respectively, based on 50 runs.

关键词： Emulation Integrated circuits Program processors Processor scheduling Job shop scheduling Design automation Hardware Deep learning design automation emulation graph neural networks partitioning algorithms

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Multirobot Stochastic Patrolling via Graph partitioning

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 300-312页

作者： Wang, Weizhen Duan, Xiaoming He, Jianping Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

In this article, we study a multirobot stochastic patrolling problem by employing graph partitioning techniques, where each robot adopts a Markov-chain-based strategy over its assigned subgraph, so that the overall patrolling performance is optimized. To quantify the patrolling performance of the robot team, we first introduce a novel performance measure based on the mean first hitting time. We then formulate optimization problems for unweighted complete graphs and transcribe it to the well-known maximum $k$-cut problem. To reduce the computational complexity, we identify a special solution structure of the optimization problem, and we develop an efficient heuristic descent-based algorithm by taking advantage of this special property of the optimal solution. We show that our algorithm converges in a finite number of steps and finds a suboptimal solution that preserves the special solution structure and satisfies a suboptimality bound. We validate our findings through numerical experiments and show the clear advantages of our partition-based strategy.

关键词： Robots Stochastic processes partitioning algorithms Surveillance Optimization Heuristic algorithms Vectors Graph partitioning Kemeny's constant Markov chains maximum k -cut problem robotic surveillance

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ClusPar: A Game-Theoretic Approach for Efficient and Scalable Streaming Edge partitioning

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IEEE TRANSACTIONS ON COMPUTERS 2025年第1期74卷 116-130页

作者： Ding, Zezhong Kong, Deyu Zhang, Zhuoxu Xie, Xike Xu, Jianliang Univ Sci & Technol China USTC Sch Artificial Intelligence & Data Sci Hefei 230026 Peoples R China USTC Suzhou Inst Adv Res Data Darkness Lab Suzhou 215123 Peoples R China USTC Sch Software Engn Suzhou 215123 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511453 Peoples R China USTC Suzhou Inst Adv Res Suzhou 215123 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong 999077 Peoples R China

Streaming edge partitioning plays a crucial role in the distributed processing of large-scale web graphs, such as pagerank. The quality of partitioning is of utmost importance and directly affects the runtime cost of distributed graph processing. However, streaming graph clustering, a key component of mainstream streaming edge partitioning, is vertex-centric. This incurs a mismatch with the edge-centric partitioning strategy, necessitating additional post-processing and several graph traversals to transition from vertex-centric clusters to edge-centric partitions. This transition not only adds extra runtime overhead but also risks a decline in partitioning quality. In this paper, we propose a novel algorithm, called ClusPar, to address the problem of streaming edge partitioning. The ClusPar framework consists of two steps, streaming edge clustering and edge cluster partitioning. Different from prior studies, the first step traverses the input graph in a single pass to generate edge-centric clusters, while the second step applies game theory over these edge-centric clusters to produce partitions. Extensive experiments show that ClusPar outperforms the state-of-the-art streaming edge partitioning methods in terms of the partitioning quality, efficiency, and scalability.

关键词： partitioning algorithms Clustering algorithms Runtime Computers Costs Memory management Computational efficiency Games Transforms Synchronization Distributed graph system streaming edge partitioning parallelization

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