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Load Balancing based Scheduling for Mixed Real Time Tasks on Multicore Systems

Load Balancing based Scheduling for Mixed Real Time Tasks on...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Shruti Jadon Pradyuman K.K. Urmil Kalaria K.R. Varsha Karthik Gupta Dept of Computer Science PES University Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Effective load balancing is essential for ensuring the efficient operation of multi-core systems, particularly in environments with diverse task priorities and deadlines. The primary objective is to intelligently distribute tasks among cores to optimize system responsiveness, prioritizing the timely completion of urgent tasks. Through rigorous computer simulations, this paper explores various load-balancing techniques tailored for multi-core systems operating in mixed real-time environments. Notably, the focus is on strategies applicable to such systems, excluding hardware-level optimizations and specific real-time scheduling algorithms. The paper introduces an algorithm aimed at enhancing both load balancing and response time. This involves leveraging the LBPSA (Load Balancing-based Partitioned Scheduling Algorithm) for load balancing and implementing the TBS (Total Bandwidth Server) migration method to further improve response time. The insights and findings presented offer valuable guidance for researchers, system designers, and practitioners involved in load balancing within mixed real-time multi-core systems across a broad spectrum of applications. The LBPSA and TBS methods are presented as contributions of the paper, enhancing the existing body of knowledge in load-balancing techniques for multi-core systems.

关键词： Scheduling algorithms Multicore processing Bandwidth Mean square error methods Load management Real-time systems partitioning algorithms Time factors Servers Optimization

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Dynamic Adaptive Weight Sparrow Search Algorithm for High-Strength Self-Consolidating Concrete Mix Design

Dynamic Adaptive Weight Sparrow Search Algorithm for High-St...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Megha Kulkarni Mohammed H. Fallah R. Gobinath Nijaguna G S Manohar M Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq Department of Civil Engineering SR University Warangal India Department of Information Science and Engineering S.E.A. College of Engineering & Technology Bangalore India Department of Computer Science and Engineering CHRIST (Deemed to be University) Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Concrete is the most used human-made material that has been utilized in the construction industry. Mix design optimization is difficult due to properties of concrete based on chemical and physical properties of the components as well as their relative proportions. Since number of parameters are comprised in a concrete mix design, identifying an optimal mixing design has always been a challenge for concrete engineers. Hence, this research proposes the Dynamic Adaptive Weighting Sparrow Search Algorithm (DAWSSA) approach for the High Strength Self-Consolidating Concrete mixt design (HSCC). This proposed methodology utilizes the data normalization and data partitioning techniques to normalize the data for enhancing the performance. Then, Best-Worst Method (BWM) approach is accomplished for pairwise comparisons of the quality criteria. The effectiveness of the proposed DAWSSA is estimated by utilizing various error metrices like R-Square (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) of values about 0.86, 1.57 and 2.29 respectively when compared to the previous works such as Multi-objective Salp Swarm Algorithm (MOSSA) and SSA-Extreme Gradient Boosting (SSA-XGB).

关键词： Manufacturing processes Communication systems Heuristic algorithms Concrete partitioning algorithms Root mean square Optimization Chemicals Design optimization Construction industry

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Robust and Efficient Outlier Detection Algorithm Based on Linear Neighborhood Sequence

Robust and Efficient Outlier Detection Algorithm Based on Li...

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International Conference on Big Data and Artificial Intelligence (BDAI)

作者： Ruiqiang Li Renmin Wang School of Information Engineering Guizhou University of Traditional Chinese Medicine Guiyang China

ISBN: (数字)9798350352009

ISBN: (纸本)9798350352016

Outlier detection aims to identify observations significantly deviating from the norm in a dataset. Existing detection methods face challenges in setting thresholds and tuning parameters. We introduce a novel Linear Neighborhood Sequence (LNS), insensitive to the parameter k, where objects share an outlier score. Moreover, we design an adaptive thresholding to partition anomaly sequences and capture outliers based on LNS properties. Experimental results on synthetic and real-world datasets demonstrate the robustness of our LNS outlier detector to parameter k and its effectiveness in outlier detection. Moreover, our method requires a significantly lower valid k value compared to similar kNN-based algorithms.

关键词： Detectors Big Data Robustness partitioning algorithms Artificial intelligence Anomaly detection Tuning Faces

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Data Sinks Deployment for Backbone-Assisted Real-Time PD-NOMA Networks based on Reinforcement Learning

Data Sinks Deployment for Backbone-Assisted Real-Time PD-NOM...

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Smart World Congress (SWC), IEEE

作者： Zhenjie Lv Chaonong Xu Jiachen Wei Beijing Key Lab of Petroleum Data Mining China University of Petroleum-Beijing Beijing China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Real-time performance is one of the most vital metrics in Backbone-assisted Power-Domain Non-Orthogonal Multiple Access Wireless Networks(BA-PDNOMAWNs) for Industrial Internet of Things applications. Since the relative geographic relationship between data sinks and wireless sensors affects real-time performance greatly, setting reasonable locations for data sinks is thus a feasible way to achieve high real-time performance for the scenarios where wireless sensors are not mobile. In this paper, we model the problem of finding the optimal locations for given sinks in BA-PDNOMAWNs so as to minimize uplink transmission latency. To find a low-complexity algorithm, we first formalize the problem as a Markov Decision Process, and then propose a reinforcement learning algorithm based on the classic Multi-Agent Deep Deterministic Policy Gradient, where its reward function is designed elaborately to accelerate convergences. Experimental results show that multiple data sinks in the BAPDNOMAWNs can reduce uplink transmission latency by $2.2 \times$ compared to that with a single data sink. Additionally, compared to the classic Simulated Annealing optimization algorithm and the other siting algorithms, such as Clustering Center and Grid partitioning in BA-PDNOMAWNs, our proposed algorithm can even reduce the uplink transmission delay by $2 \times$, thus achieving better real-time performance.

关键词： Wireless communication Wireless sensor networks NOMA Clustering algorithms Reinforcement learning Real-time systems partitioning algorithms Delays Sensors Uplink

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Dial-a-Ride Problem Considering Urban Traffic Dynamics in Multi-Reservoir Systems

Dial-a-Ride Problem Considering Urban Traffic Dynamics in Mu...

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International Conference on Intelligent Transportation

作者： Cong Quoc Tran Shang Jiang Mehdi Keyvan-Ekbatani Complex Transport Systems Lab Civil & Natural Resources Engineering University of Canterbury New Zealand

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Although on-demand mobility services can provide more efficient and sustainable door-to-door transport options, they typically face challenges due to the complexity of incorporating traffic conditions and stakeholders' involvement in the decision-making process, particularly under dynamic traffic settings. This study proposes a multi-reservoir frame-work to maximize the overall profit of the dynamic dial-a-ride (DAR) problem while considering the dynamic nature of traffic conditions, real-time passenger requests and their sharing preferences. A dynamic DAR algorithm is developed and evaluated numerically through a case study, considering specific traffic scenarios. Numerical results demonstrate the practical applicability and operational efficiency of the proposed system. Managerial insights and potential research directions are also provided.

关键词： Heuristic algorithms Systems operation Decision making Urban areas Real-time systems partitioning algorithms Stakeholders Optimization Resilience Meteorology

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Storage and Querying of Spatiotemporal Data in Blockchain

Storage and Querying of Spatiotemporal Data in Blockchain

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International Conference on

作者： Fanchang Amit Yadav Asif Khan Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

A storage structure C-L Matrix based on linked lists and matrices is proposed to address the issue of spatiotemporal data not fully utilizing its temporal and spatial attributes in blockchain, and efficient queries are implemented on this storage structure to meet the needs of fast on chain and large-scale transaction data queries in blockchain applications. It is also seamlessly integrated with the security features of blockchain, such as decentralization, tamper resistance, and traceability. After research analysis and comparison, C-L Matrix has significant advantages over F-L Tree in terms of spatial and temporal complexity; The C-L Matrix also solves cross domain, spatial data distribution, and temporal data chain problems, which can well support the application and management of spatiotemporal data in blockchain.

关键词： Resistance Loading Spatial databases Blockchains Spatiotemporal phenomena partitioning algorithms Complexity theory

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A Hybrid Particle Swarm Optimization K-Means Algorithm with Quantum Behavior

A Hybrid Particle Swarm Optimization K-Means Algorithm with ...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Man Liu Haojie Yun Qiyao Zhu Hua Chun College of Computer Science and Technology Inner Mongolia Minzu University Tongliao China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

The K-means algorithm is a partition-based clustering approach with two notable drawbacks, sensitivity to the initial center and a tendency to converge to suboptimal solutions. To address these limitations, a hybrid particle swarm optimization k-means algorithm with quantum be-havior (HQPSO-K) is proposed. The improved algorithm uses a nonlinear contraction-expansion coefficient to balance local optimization and global search. Moreover, Levy flight strategy is incorporated to further improve the particles' capability to evade local optima. Finally, the improved QPSO algorithm combined with K-means explores the search space more efficiently. The algorithm is tested with UCI data set. Experimental results demonstrate that HQPSO-K achieves superior clustering outcomes and is less sensitive to initial conditions.

关键词： Sensitivity Heuristic algorithms Clustering algorithms Market research partitioning algorithms Space exploration Particle swarm optimization Optimization Computational intelligence

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Modular Validation within Digital Twins: A Case Study in Reliability Analysis of Manufacturing Systems

Modular Validation within Digital Twins: A Case Study in Rel...

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Simulation Winter Conference

作者： Ashkan Zare Sanja Lazarova-Molnar Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense DENMARK Institute AIFB Karlsruhe Institute of Technology Karlsruhe GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

As manufacturing rapidly evolves, optimizing processes is essential. Digital Twins, which act as near real-time virtual replicas of the corresponding real-world systems, can support this optimization by providing insights and supporting decision-making. Digital Twins can only be fully effective if their underlying models continuously and accurately reflect the corresponding physical systems. However, not all model components change at the same pace, and relevant data updates also vary in frequency. Thus, Digital Twins require robust validation mechanisms that can identify what parts of models need to be re-extracted, what parts need to be recalibrated, and what parts need to remain same. This is a complex task that necessitates precise partitioning of models with respect to the above noted considerations. Here, we propose a novel approach to modular validation, aimed at supporting Digital Twins. To illustrate our approach, we provide a case study in reliability analysis of manufacturing systems.

关键词： Decision making Optimization methods Data models Real-time systems Digital twins partitioning algorithms Reliability Standards Manufacturing systems Testing

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On Distributed Simulation Modeling of Large Scale Networks

On Distributed Simulation Modeling of Large Scale Networks

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International Science and Technology Conference (Modern Networking Technologies) (MoNeTeC)

作者： M. V. Tyushev R. L. Smelyanskii Faculty of Computational Mathematics and Cybernetics Moscow State University Moscow Russia

ISBN: (数字)9798350369007

ISBN: (纸本)9798350369014

This paper addresses the challenges in distributed simulation modeling of large-scale networks (LSIM – consist of hundreds of thousands of nodes). The focus is on the efficient mapping of LSIM onto a distributed simulation environment (DSE). The main contribution is the descriptions of the solutions of the two problems: (1) clustering the LSIM graph, where each node represents a data processing point, into a clustered-graph where each node is a cluster of LSIM nodes; and (2) mapping this clustered-graph onto a DSE graph, where each vertex represents a computational server. The research operates under the assumptions that all LSIM nodes generate identical computational workloads, all DSE servers are identical, and DSE network channels have heterogeneous bandwidths.

关键词： Computational modeling Clustering algorithms Bandwidth Data processing Computer networks partitioning algorithms Servers

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Optimal Configuration of Reconfigurable Intelligent Surfaces With Non-uniform Phase Quantization

Optimal Configuration of Reconfigurable Intelligent Surfaces...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jialong Lu Rujing Xiong Tiebin Mi Ke Yin Robert CaimingQiu School of Electronic Information and Communications Huazhong University of Science and Technology Center for Mathematical Sciences School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The existing methods for Reconfigurable Intelligent Surface (RIS) beamforming in wireless communication are typically limited to uniform phase quantization. However, in real world applications, the phase and bit resolution of RIS units are often non-uniform due to practical requirements and engineering challenges. To fill this research gap, we formulate an optimization problem for discrete non-uniform phase configuration in RIS assisted multiple-input single-output (MISO) communications. Subsequently, a partition-and-traversal (PAT) algorithm is proposed to solve that, achieving the global optimal solution. The efficacy and superiority of the PAT algorithm are validated through numerical simulations, and the impact of non-uniform phase quantization on system performance is analyzed.

关键词： Quantization (signal) Array signal processing System performance Memory management Reconfigurable intelligent surfaces Numerical simulation partitioning algorithms MISO Global communication Optimization

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