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Chinese Automation Congress (CAC)

作者： Chenggen Pu Huawu Cai Ping Wang Jinfeng Liao Changjie Dong China-Korea Belt and Road Joint Laboratory on Industrial Internet of Things Chongqing University of Posts and Telecommunications Chongqng China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqng China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Satisfying the end-to-end delay requirements of various real-time data flows is a critical challenge in industrial wireless sensor networks (IWSNs). Since each data flow has different deadline constraints, effectively utilizing network resources while ensuring that data flows meet their deadlines becomes an important problem to be addressed. This paper proposes a joint time slot and channel allocation algorithm based on a multi-dimensional conflict graph, aiming to minimize the total end-to-end delay of multiple data flows with different deadline constraints. First, the concept of a minimum integer scheduling unit (MISU) is introduced to represent the minimum transmission period based on the deadlines, allowing for a unified scheduling structure. Next, a flow-link-slot-channel tuple is proposed to describe potential scheduling schemes, and a multi-dimensional conflict graph (MDCG) is constructed based on the conflicts among tuples. The joint scheduling problem is then transformed into finding the optimal maximum independent set (MIS) in the MDCG. To reduce computational complexity, the original problem is decomposed into a series of maximum weight independent set (MWIS) subproblems using a greedy-based hop-wise scheduling algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm in minimizing end-to-end delay compared to existing algorithms.

关键词： Wireless communication Wireless sensor networks Job shop scheduling scheduling algorithms Simulation Channel allocation Real-time systems Delays Optimization Periodic structures

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Traffic flow scheduling algorithm based on gate control and traffic shaping mechanism of TSN network in substation

Traffic flow scheduling algorithm based on gate control and ...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Bian Baoyin Zhang Hao He Xiaoyang Qiu Yong Li Lang Ma Tao Huang Xin State Grid Electric Power Research Institute Nanjing China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Substation protection and measurement and control services are very important for real-time monitoring of abnormal power grid operation and timely handling of faults. Among them, sample value packets and switch state packets belong to time-delay sensitive and periodic traffic types. The traditional best-effort network transmission mode cannot meet the transmission requirements of service flows in controllable delay and jitter. Based on TSN network gating and traffic shaping mechanism, the efficient transmission of substation protection and measurement and control sensitive service flows can be realized. Based on the dual-network redundant architecture of smart substation, a scheduling mechanism for multiple sensitive service flows was designed. When delay and jitter were introduced in TSN switches at different levels, and under the condition of considering the background traffic of various device configuration management messages, the transmission path with minimum cost was dynamically selected to ensure bounded delay and jitter for protection and measurement and control service flows. Numerical simulation results show that the performance of the proposed scheduling algorithm is close to the theoretical optimal value.

关键词： Performance evaluation scheduling algorithms Substation protection Configuration management Switches Optimal scheduling Jitter

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scheduling Model of Scenic Tour Task Based on Heuristic Intelligent Search Algorithm

Scheduling Model of Scenic Tour Task Based on Heuristic Inte...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Hongbo Wang Rong Fei Zilong Wang Aimin Li Yajun Liu Fuyu Zhu School of Computer Science and Engineering Xi'an University of Technology Xi’an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This paper constructs a scenic tour task scheduling model (hereinafter referred to as the model) based on heuristic intelligent search algorithm, puts forward the heuristic intelligent search solution scheme under single-task and multi-task scenarios, aims to unify the planning of tour routes according to the scenic spot flow situation, prevents the formation of attraction passenger flow peaks, and can effectively solve the problem of the traditional scheduling model's dynamic scheduling ability lack. In particular, the performance of different scheduling algorithms is compared and simulation tests are designed to verify that the proposed model and solution method are effective for solving the optimal path problem of scenic tour, which is of practical significance for realizing the intelligent management of scenic spots and promoting the intelligent development of tourism.

关键词： scheduling algorithms Heuristic algorithms Dynamic scheduling Search problems Multitasking Planning Artificial intelligence

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Research on Virtual Resource scheduling Algorithm Based on Multi-Objective Optimization

Research on Virtual Resource Scheduling Algorithm Based on M...

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Cyber Science and Technology Congress (CyberSciTech), IEEE

作者： Cheng Zhou Xiaojuan Guan Chen Wang Wen Ji Chenhan Lin State Grid Laboratory of Power cyberSecurity Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd Nanjing China State Grid Fujian Information & Telecommunication Company State Grid Fujian Electric Power Co.Ltd Fujian China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

In order to solve the resource management challenge of cloud service center when the user demand peaks periodically, reduce resource waste and energy consumption, improve resource utilization and system performance, a scheduling algorithm based on multi-objective optimization is proposed in this paper. Firstly, this paper analyzes the main problems of current cloud computing resource scheduling, constructs an adaptive virtual resource scheduling framework, establishes a multi-objective scheduling optimization model, and ensures the balance of resource allocation by quantifying task levels and establishing a “task-virtual node” allocation model. To solve the model, a load-balancing strategy based on improved particle swarm optimization (PSO) was proposed to improve resource utilization and reduce energy consumption. Other scheduling factors that need to be considered in the actual scheduling environment, including computing cost, load, task completion time, and convergence speed, are further studied and improved in the experiment. Based on the above scheduling factors, multi-objective optimization of cloud computing resource scheduling is carried out to achieve the optimal comprehensive performance of cloud computing task scheduling. Finally, through simulation experiments, it is verified that the algorithm has better performance in terms of cost, load, task completion time, convergence speed, and CPU consumption, especially when the task volume increases; it can balance the load and reduce energy consumption more effectively.

关键词： Cloud computing Energy consumption Costs scheduling algorithms Computational modeling Resource management Particle swarm optimization Optimization Convergence Load modeling

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Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness

arXiv

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arXiv 2024年

作者： Bouška, Michal Šůcha, Přemysl Novák, Antonín Hanzálek, Zdeněk Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Prague Czech Republic Czech Technical University in Prague Faculty of Electrical Engineering Department of Control Engineering Karlovo náměstí 13 Prague Czech Republic

In this paper, we investigate the use of the deep learning method for solving a well-known NP-hard single machine scheduling problem with the objective of minimizing the total tardiness. We propose a deep neural network that acts as a polynomial-time estimator of the criterion value used in a single-pass scheduling algorithm based on Lawler's decomposition and symmetric decomposition proposed by Della Croce et al. Essentially, the neural network guides the algorithm by estimating the best splitting of the problem into subproblems. The paper also describes a new method for generating the training data set, which speeds up the training dataset generation and reduces the average optimality gap of solutions. The experimental results show that our machine learning-driven approach can efficiently generalize information from the training phase to significantly larger instances. Even though the instances used in the training phase have from 75 to 100 jobs, the average optimality gap on instances with up to 800 jobs is 0.26%, which is almost five times less than the gap of the state-of-the-art heuristic. © 2024, CC BY-NC-ND.

关键词： scheduling algorithms

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Research on Energy scheduling Strategy of DC Microgrid with Multiple Power Routers

Research on Energy Scheduling Strategy of DC Microgrid with ...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Shujian Tang Meng Zhang Junjie Zhu Zhenjiang Power Supply Company State Grid Zhenjiang Electric Power Co. Ltd Zhenjiang China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

The DC microgrid clusters have become one of the main networking forms for residential and industrial parks in the future due to its simple control method and high DC load and distributed energy consumption efficiency. This paper takes the low-voltage DC microgrid clusters as the research object. First, it gave the matching and scheduling method of the top-level control, and then designed a DC microgrid clusters experimental device to complete the communication and dispatch experiment. In the first stage, a packetized power dispatch protocol was proposed and the matching and power transmission dispatch are expressed as two problems respectively. In the matching problem, a mixed-integer nonlinear programming algorithm is better than a one-to-one matching algorithm in terms of matching efficiency, and it has obvious advantages in dealing with power line transmission loss. However, the greater time complexity ( $o$ (2 S × D+D )) of the mixed-integer nonlinear programming algorithm than that (o(SD)) of the latter leads to longer matching time. In the scheduling problem, the heuristic algorithm performs better than the knapsack algorithm under the number of multi-power channels. In the second stage, a dispatch platform was designed for experimental verification. The four-cycle continuous scheduling experiment verified the correctness of the scheduling protocol.

关键词： Job shop scheduling scheduling algorithms Heuristic algorithms Clustering algorithms Power transmission Microgrids Programming

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A Virtual Cryptographic Card Resource Sharing Method Based on Lottery scheduling Optimization Algorithm

A Virtual Cryptographic Card Resource Sharing Method Based o...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Yi Gao Daoyu Wang Jing Xia Xixi Fu Information and Electronics Department Wuhan Digital Engineering Institution Wuhan China Innovation Center for GPU and Intelligent Computing System Technology Wuhan Digital Engineering Institution Wuhan China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Given the pressing demands for high concurrency and throughput in secure business operations within cloud computing virtual environments, traditional I/O virtualization methods often lack the necessary latency guarantees for user cryptographic card computation tasks. Therefore, this paper explores a virtual cryptographic card resource sharing approach based on a lottery scheduling optimization algorithm, enabling multi-machine sharing and flexible allocation of crypto graphic cards. The study employs randomized scheduling based on the volume and waiting time of cryptographic computation tasks. By proportionally dividing cryptographic card resources, it enhances the timely responsiveness of virtual cryptographic computation tasks and reduces the uncertainty in resource allocation caused by the randomness of traditional lottery scheduling algorithms, thus balancing the computational requirements of different tasks for cryptographic computations.

关键词： Uncertainty scheduling algorithms Pressing Dynamic scheduling Throughput Cryptography Resource management Time factors Virtualization Optimization

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Robust purification defense for transfer attacks based on probabilistic scheduling algorithm of pre-trained models: A model difference perspective 23

Robust purification defense for transfer attacks based on pr...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Liu, Xinlei Xie, Jichao Hu, Tao Ma, Hailong Li, Baolin Yi, Peng Zhang, Zhen Information Engineering University Zheng Zhou450001 China Ministry of Education Key Laboratory of Cyberspace Security Zheng Zhou450001 China

Neural networks are vulnerable to meticulously crafted adversarial examples, resulting in high-confidence misclassifications in image classification tasks. Due to their stealthiness and difficulty in detection, black-box transfer attacks have become a significant focus of defense. In this article, we propose a purification defense based on probabilistic scheduling algorithm of pre-trained models (ProbSched-PTM) to counter diverse transfer attacks. We first quantify the differences among various models based on their output scores and verify the linear negative correlation between adversarial transferability and model difference. Subsequently, guided by the model difference probability, we integrate the negative momentum probability as a regularization factor to construct ProbSched-PTM. It selects the most appropriate substitute model from multiple pre-trained models to generate strong-transferability adversarial examples for training the purification model, which enables the purification model to effectively eliminate diverse adversarial perturbations. The ProbSched-PTM-based purification defense provides robust defense against unseen adversarial attacks from different substitute models. In a black-box attack scenario, utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 94.8% on CIFAR-10 and over 71.2% on Mini-ImageNet, demonstrating state-of-the-art performance. © 2024 IEEE.

关键词： scheduling algorithms

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Markov Chain-based Data Privacy Prioritizing for Task scheduling in Digital Twin Edge Networks

Markov Chain-based Data Privacy Prioritizing for Task Schedu...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Elif Bozkaya-Aras Müge Erel-Özçevik Department of Computer Engineering National Defence University Turkish Naval Academy Istanbul Turkey Department of Software Engineering Manisa Celal Bayar University Manisa Turkey

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Edge computing plays a significant role in shaping the future of Internet of Things (IoT) networks by processing high volumes of data at the network edge and meeting the desired latency demand. Although edge computing is regarded as a promising technology for these features as well as its decentralization and supportive functionalities, there is a need to develop systems to protect data security and privacy since users can face the risk of data leakage when they offload data to the edge/cloud server. In order to solve this problem, in this paper, we propose a three-layer digital twin edge network architecture for task scheduling based on edge-cloud computing. The digital twin has been used to map the IoT network to the virtual space, monitor physical assets in real-time, and perform comprehensive network analysis. On this basis, a task scheduling model that prioritizes data privacy has been introduced to prevent users’ privacy-sensitive information from being collected by attackers. The proposed model can both take full advantage of edge and cloud computing and protect data privacy. We classify computation tasks based on data privacy sensitivity and model them using the M/M/1 Markov model in local, edge server, and cloud computations. Then, we propose a data privacy prioritizing task scheduling algorithm combining digital twin and edge-cloud collaboration. This genetic optimization based algorithm significantly increases data privacy by 40% compared to the greedy approach, while keeping the time under an acceptable level. In this way, each task is distributed between the local, edge server, and cloud server. Thanks to the intelligence behavior and repetitive growth of the proposed algorithm, the optimal task scheduling is performed in a digital twin system. Our analysis reveals that data leakage attacks can be significantly decreased with the proposed task scheduling algorithm by minimizing task processing time.

关键词： Data privacy Cloud computing Sensitivity scheduling algorithms Computational modeling Collaboration Network architecture Data models Digital twins Servers

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Robust User scheduling and Power Allocation in Cell-Free Massive MIMO Networks

Robust User Scheduling and Power Allocation in Cell-Free Mas...

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Saeed Mashdour André R. Flores Shirin Salehi Rodrigo C. De Lamare Anke Schmeink João P. S. H. Lima Paulo Branco Da Silva Centre for Telecommunications Studies Pontifical Catholic University of Rio de Janeiro Brazil Chair of Information Theory and Data Analytics RWTH Aachen University Aachen Germany Department of Electronic Engineering University of York United Kingdom CPQD Campinas Brazil

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

This paper introduces a robust resource allocation framework for the downlink of cell-free massive multi-input multi-output (CF-mMIMO) networks to address the effects caused by imperfect channel state information (CSI). In particular, the proposed robust resource allocation framework includes a robust user scheduling algorithm to optimize the network’s sum-rate and a robust power allocation technique aimed at minimizing the mean square error (MSE) for a network with a linear precoder. Unlike non-robust resource allocation techniques, the proposed robust strategies effectively counteract the effects of imperfect CSI, enhancing network efficiency and reliability. Simulation results show a significant improvement in network performance obtained by the proposed approaches, highlighting the impact of robust resource allocation in wireless networks.

关键词： scheduling algorithms Wireless networks Simulation Mean square error methods Massive MIMO Downlink Robustness

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