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检索条件"主题词=Partitioning Algorithms"

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Efficient Latency Optimization in Batch Processing Server Systems for Multi-User Co-Inference

Efficient Latency Optimization in Batch Processing Server Sy...

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Yiming Ma Ke Gao Chao Zhang Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist)

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

With the progression of internet of things (IoT) and artificial intelligence (AI), the demand for real-time, high-performance edge computing in multi-user collaborative inference scenarios has intensified. Batch-processing edge servers play a pivotal role in optimizing resource allocation, throughput, and overall system performance. This paper delves into the utilization of servers equipped with batch processing capabilities in edge computing for multi-user collaborative inference contexts. The inference tasks are partitioned into sub-tasks, and the minimization of user latency under energy consumption constraints is investigated. Initially, we assume a constant inference latency for the edge server and propose an iterative algorithm that explores partition points. Additionally, we introduce a traversal algorithm for the largest batch (TALB), taking into account the actual latency of the edge server. These algorithms demonstrate a significant reduction in task processing latencies of up to 79.5 % and enhance system performance across various conditions, particularly in scenarios with limited server bandwidth or significant capability differences among user devices.

关键词： Energy consumption System performance Collaboration Batch production systems Inference algorithms partitioning algorithms Servers Internet of Things Optimization Edge computing

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Efficient Zone Routing through K-Nearest Neighbours Optimization in Wireless Sensor Networks

Efficient Zone Routing through K-Nearest Neighbours Optimiza...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Rahul Mishra Sudhanshu Kumar Jha Department of Electronics and Communications University of Allahabad Prayagraj India

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wireless Sensor Networks faces significant challenges due to limited energy and computational resources, requires efficient routing protocols to optimize network lifetime and performance. Traditional Zone Routing Protocol (ZRP) employs mixed routing approach to minimize overhead by partitioning the network in various zones, and utilises the proactive routing approach within zones and follow the reactive routing among zones. However, inefficient zone formation increases routing overhead and reduce scalability, limiting the protocol's effectiveness in dynamic environments. This paper addresses these limitations by integrating the K-Nearest Neighbours (KNN) algorithm into ZRP, optimizing zone creation based on node proximity to enhance the routing efficiency and network organization. The proposed KNN-ZRP approach aims to create more cohesive and strategically formed zones, thus reduces the number of zones needed and improves the communication efficiency. Through extensive simulations, the paper presents a comparison among the performance of traditional ZRP with the KNN-enhanced ZRP across metrics such as zone count, routing overhead, and average distance to the nearest neighbour. The result shows that the KNN-ZRP approach achieves a significant reduction in routing overhead and prolongs network lifetime, with results showing up to a 30% reduction in zone numbers and improved resource conservation. This approach demonstrates substantial improvements in network scalability and efficiency, making it highly suitable for deployment in resource constrained WSN environments.

关键词： Wireless communication Measurement Wireless sensor networks Scalability Organizations Nearest neighbor methods Routing Routing protocols partitioning algorithms Optimization

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A Study of Submersible Positioning Based on Deep Reinforcement Learning and Automatic Control algorithms

A Study of Submersible Positioning Based on Deep Reinforceme...

引用

Robotics & Intelligent Manufacturing Technology (ISRIMT), International Symposium on

作者： Wenjie Liu Xunyi Ma Boyou Zheng Guoji Shen National University of Defense Technology Changsha China National University of Defense Technology Key Laboratory of Equipment State Sensing and Smart Support Changsha China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

In this paper, a position prediction model and a search model are developed to explore the application of Monte Carlo modeling and deep reinforcement learning and control algorithms to the localization of marine submersibles. A set of six-degree-of-freedom spatial equations of motion are developed using Newton's second law for analyzing the motion of a submersible in still water. Simulations confirm a high degree of similarity between the model and the actual motion. The Monte Carlo model is used to simulate the probability distribution, optimize the initial deployment position, and improve the efficiency of repositioning the faulty submersible. Among the compared search methods, reinforcement learning shows higher efficiency and resource savings with a minimum relative search time of 0.57 and a search completion rate of 0.95. An automatic control system combining deep reinforcement learning and control algorithms provides a hierarchical framework for real-time search path optimisation with improved adaptability and efficiency. For the multi-submarine task, an improved k-means clustering algorithm is used to construct a dynamic partitioning model to optimize the configuration of finite search devices. Simulation results show that the probability-based deployment scheme achieves a higher search success rate compared to the traditional approach.

关键词： Monte Carlo methods Heuristic algorithms Clustering algorithms Predictive models Prediction algorithms Deep reinforcement learning Mathematical models Real-time systems partitioning algorithms Underwater vehicles

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Blockchain Protocols for Reducing the Proportion of Cross-Shard Transactions

Blockchain Protocols for Reducing the Proportion of Cross-Sh...

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Computer Science and Blockchain (CCSB), International Conference on

作者： Huaizhou Yang Pei Huang Xintian Zhang Department of Computer Science Xi'an Shiyou University Xi’an Shaanxi China Department of Electronic Information Xi'an Shiyou University Xi'an Shaanxi China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

As an effective solution to the blockchain scalability problem, sharding algorithms are widely regarded as having great potential and application prospects. However, the increase in the number of shards naturally gives rise to a series of problems, such as the increase in the proportion of cross-shard transactions, longer transaction confirmation latency, and limited throughput. Therefore, we propose Brockerchain protocol to solve such problems. The protocol uses the convergent louvain algorithm (C-Iouvain) for node partitioning, which balances the load between shards, and then introduces the Agent Account mechanism, which reduces the proportion of cross-shard transactions. Experiments show that this protocol effectively reduces the proportion of inter-shard transactions, improves throughput, and reduces the transaction delay of inter-shard transactions.

关键词： Computer science Sharding Protocols Scalability Throughput Load management Blockchains partitioning algorithms Delays

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Research on Configuration Data Generation Algorithm for HyperParser

Research on Configuration Data Generation Algorithm for Hype...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Peijun Ma Gongzhi Zhao Jiangyi Shi Cong Yao Weitao Pan Xidian University Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The evolution of technologies such as Big Data, Cloud Computing, and Artificial Intelligence is inextricably linked to the transmission of network data. Programmable network devices that facilitate high-speed processing are becoming increasingly crucial. The significance of programmable parsers, as a constituent element of programmable network devices, is readily apparent. HyperParser has the advantages of low latency, high throughput, small area, low power consumption, and full programmability, but its configuration data generation algorithm is inefficient and has limited optimality. In this paper, the inefficiency in running speed is first optimized by subtree partitioning, and the preference for removing certain columns more conducive to approaching the global optimal solution is then analyzed from a probabilistic point of view. Using the big-union, edge network and enterprise network three kinds of parse graph test, the original algorithm in processing big-union in 10min failed to end the run, processing the rest of the two parse graphs consumed 0.1545s, 0.1513s, extracted the number of key bits 7 and 18; the algorithm proposed in this paper processing the three kinds of parse graphs consumed 3.679s, 0.019s and 0.019s, 3.679s, 0.019s and 0.027s, and the number of key bits extracted are 160, 7 and 17 respectively.

关键词： Cloud computing Power demand Computer applications Big Data Throughput Probabilistic logic Power electronics partitioning algorithms Low latency communication Optimization

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases Parallel processing Software partitioning algorithms

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A Multi-objective Evolutionary Algorithm Based on Q-learning for Adaptive Reference Vectors and Partition Deletion Strategy

A Multi-objective Evolutionary Algorithm Based on Q-learning...

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Chinese Control and Decision Conference, CCDC

作者： Weijie Yu Jianchang Liu The College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The performance of decomposition algorithms is highly sensitive to the shape of the Pareto front, as the predefined reference vectors may not always be adapted to various problem characteristics without prior knowledge. Moreover, as the number of optimization objectives increases, the objective space will be filled with a large number of non-dominated solutions, making it difficult to compare their quality. To address these issues, this paper proposes a decomposition algorithm based on an adaptive reference vector Q-learning with partition deletion strategy (RVQP) approach. The proposed method consists of three main operations: a reinforcement Q-learning (RQ) operation, a reference point sampling operation, and a partition deletion operation. The RQ operation considers the adaptive process of reference vectors as a reinforcement learning task, assisting the reference vectors in learning from the environment and selecting action plans based on optimal features. The reference point sampling operation generates new reference vectors using a distributed model. The partition deletion strategy is employed in environmental selection to eliminate inferior individuals, thereby increasing selection pressure. Experimental results comparing the proposed algorithm with typical problems and classical algorithms have validated its competitiveness and effectiveness.

关键词： Adaptation models Q-learning Shape Sociology Evolutionary computation Vectors partitioning algorithms

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Research on Heuristic IPv6 Address Scan Target Generation Technology Based on Address Structure

Research on Heuristic IPv6 Address Scan Target Generation Te...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Peiming Zhang Zhongmiao Kang Jiajia Fu Power Dispatching Control Center Guangdong China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

IP address scanning is a universal network measurement method used to study network topology and detect network devices. It involves obtaining relevant information about the target network by calling the port service and recording the target's response. This information can be utilized in various fields such as network security analysis and management, network service measurement, vulnerability analysis, network topology analysis, and address analysis. Currently, IPv4 address scanning technology has been well developed. However, compared to the IPv4 address space, IPv6 offers a significantly larger address space and currently has a lower density of active addresses in the address space, making the traversal method used in IPv4 address scanning unsuitable for IPv6 networks. Meanwhile, the scanning efficiency of existing IPv6 scanning target generation methods is relatively low. To address this, we propose a heuristic IPv6 address scanning target generation algorithm based on address structure. This algorithm mainly includes three parts: pre-scan mechanism, IPv6 active address direct push algorithm, and IPv6 active address extension algorithm. Testing experiments using address sets from publicly available data sources as inputs demonstrate that the scanning target address generation algorithm has improved accuracy by over 20% compared to existing methods.

关键词： Network topology Statistical analysis Heuristic algorithms Soft sensors Current measurement Network security partitioning algorithms

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Multiscale Numerical Simulation for Assessing Effective Electrical Conductivity Estimations of Geological Medium

Multiscale Numerical Simulation for Assessing Effective Elec...

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Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Anastasiia Yu. Kutishcheva Sergey I. Markov Ekaterina I. Shtanko Laboratory of mathematical modeling of multiphase processes in native and artificial multiscale heterogeneous media Trofimuk Institute of Petroleum Geology and Geophysics SB RAS Novosibirsk Russia Department of Geophysical Systems Novosibirsk State Technical University Novosibirsk Russia Department of Computer Technologies Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The paper considers methods for estimating the effective electrical conductivity of a geologic medium with an internal structure represented as a set of layer-by-layer tomographic images. Based on this type of input data, a discrete hierarchical mesh model of the sample is constructed. It is used to describe the internal singularities of the investigated medium with significant accuracy. To obtain the effective electrical conductivity of the geologic medium, a direct problem of electric field potential distribution in the sample volume is solved numerically. Since the computational domain is defined as a hierarchy of mesh partitions, a multiscale modification of the scalar finite element method is applied. The averaging procedure of the properties is based on Ohm's law. The possibility of step-by-step averaging of the electrical properties of a rock sample is also investigated. To obtain the electrical effective characteristic of the entire sample, the effective properties of individual macroelements in the mesh partitioning are determined and further averaged using the algorithms proposed in the paper. The variation of the effective electrical conductivity over certain sub-domains of the sample is given.

关键词： Solid modeling Accuracy Conductivity Media Rocks Numerical simulation partitioning algorithms Numerical models Finite element analysis Informatics

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Allocation Strategies for Disaggregated Memory in HPC Systems

Allocation Strategies for Disaggregated Memory in HPC System...

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International Conference on High Performance Computing

作者： Robin Boëzennec Danilo Carastan-Santos Fanny Dufossé Guillaume Pallez University of Rennes Inria CNRS IRISA Rennes France Univ. Grenoble Alpes Grenoble INP Inria CNRS LIG Grenoble France INRIA Rennes France

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

In this work we consider scheduling strategies to deal with disaggregated memory for HPC systems. Disaggregated memory is an implementation of storage management that provides flexibility by giving the option to allocate storage based on system-defined parameters. In this case, we consider a memory hierarchy that allows to partition the memory resources arbitrarily amongst several nodes depending on the need. This memory can be dynamically reconfigured at a cost. We provide algorithms that pre-allocate or reconfigure dynamically the disaggregated memory based on estimated needs. We provide theoretical performance results for these algorithms. An important contribution of our work is that it shows that the system can design allocation algorithms even if user memory estimates are not accurate, and for dynamic memory patterns. These algorithms rely on statistical behavior of applications. We observe the impact on the performance of parameters of interest such as the reconfiguration cost.

关键词： Costs Runtime Heuristic algorithms High performance computing Storage management Memory management Stochastic processes Dynamic scheduling partitioning algorithms Resource management

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