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检索条件"主题词=Scheduling algorithms"

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Intelligent scheduling of UAVs and Sensors for Information Age Minimization at Wireless Powered Internet of Things

Intelligent Scheduling of UAVs and Sensors for Information A...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Jiameng Li Xiaojie Wang Jun Wu Zhaolong Ning School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Age of Information (AoI) has received much attention from researchers as the latest metric to quantify the freshness of data. It is necessary to jointly schedule Unmanned Aerial Vehicles (UAVs) and sensors to reduce the system AoI in wireless powered Internet of things. However, constraints on UAV flight time, charging time, and data collection time, as well as constraints of half-duplex hardware for sensors make it difficult to efficiently jointly schedule UAVs and sensors by traditional methodes. Thus, we design a multi-agent Deep Reinforcement Learning (DRL)-based UAV cooperative scheduling algorithm that jointly optimizes sensor charging time, UAV trajectories and sensor update scheduling with AoI as the optimization objective. Initially, we define the AoI minimization problem, portraying it as a Markov decision process. Then, we design a multi-agent DRL algorithm founded on factorizing value functions to address this issue. Finally, experiments demonstrate that the MAPLE algorithm can effectively coordinate the scheduling of UAVs and sensors.

关键词： Wireless communication Wireless sensor networks Schedules scheduling algorithms Markov decision processes Autonomous aerial vehicles Minimization

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Research and Practice of Intelligent scheduling Algorithm Based on Machine Vision in Mechanical Equipment Manufacturing

Research and Practice of Intelligent Scheduling Algorithm Ba...

引用

Electrical Drives, Power Electronics & Engineering (EDPEE), International Conference on

作者： Yuying Li FUSHUN Vocational Technology Institute Fushun Liaoning China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

This research focuses on the intelligent scheduling algorithm based on machine vision in mechanical equipment manufacturing. First of all, through the detailed analysis of mechanical equipment manufacturing process and machine vision technology, a production monitoring system based on machine vision is constructed to realize realtime monitoring and data analysis of the production site. Secondly, the theoretical basis and optimization method of intelligent scheduling algorithm are studied, and an intelligent scheduling algorithm suitable for mechanical equipment manufacturing process is designed. Finally, a complete intelligent scheduling system is developed by combining the studied intelligent scheduling algorithm with machine vision technology, and verified by practical application. The results show that the production scheduling scheme proposed in this paper not only reduces the production cost, but also improves the production efficiency, and the system stability is high. Therefore, the effectiveness and optimization performance of the designed intelligent scheduling algorithm are verified. It embodies the practical application effect and value of the developed intelligent scheduling system.

关键词： Industries Job shop scheduling Manufacturing processes Costs scheduling algorithms Machine vision Production

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Optimizing Multi-path QUIC Protocol Performance in Cloud-Edge Collaboration through CPU Affinity and Kernel-Level Resource scheduling

Optimizing Multi-path QUIC Protocol Performance in Cloud-Edg...

引用

IEEE International Conference on Communications in China (ICCC)

作者： Xiaohan Xia Bin Chen Chao Cai Pei Gao Jiahui Qiu Yinglong Hou Intelligent Network Innovation Center China United Network Communication Co. Ltd. Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This study investigates how optimizing CPU affinity and kernel-level resource scheduling can enhance the performance of the Multi-path QUIC (MPQUIC) protocol in cloud-edge collaboration. It explores the significance of CPU affinity for improving MPQUIC protocol performance and leverages extended Berkeley Packet Filter (eBPF) technology to implement a heuristic planning algorithm for achieving high-performance implementations. By adjusting CPU affinity, it effectively enhances the maximum throughput achievable by MPQUIC on a single core and the relationship between acceleration bandwidth and CPU consumption. Furthermore, combining eBPF technology’s scheduling algorithm further optimizes the protocol performance in cloud-edge environments. This research provides a new perspective and approach for optimizing the performance of Cloud-Edge Collaboration using MPQUIC.

关键词： Protocols scheduling algorithms Scalability Collaboration Bandwidth Throughput Dynamic scheduling

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Active Disturbance Rejection Gain scheduling Control of Hypersonic Vehicle Based on Guardian Maps

Active Disturbance Rejection Gain Scheduling Control of Hype...

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Data Driven Control and Learning Systems (DDCLS)

作者： Tingyu Zhang Yongshuai Wang Mingwei Sun Mingrui Hao Zengqiang Chen College of Artificial Intelligence Nankai University Tianjin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper presented an adaptive gain scheduling algorithm for hypersonic vehicles with wide envelopes. First, the linear parameter variable parameter (LPV) model of hypersonic vehicles is established in the flight envelope. Then, the active disturbance rejection controller is considered to cope with disturbances and uncertainties, and the control gain is scheduled using the guardian maps method to adapt to the wide envelope of velocity and height. Finally, numerical simulation results show good control performance and meet the required performance indexes in all operating conditions, demonstrating the effectiveness and robustness of the proposed method.

关键词： Uncertainty PI control scheduling algorithms Atmospheric modeling Numerical simulation scheduling Robustness

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A Dynamic Prioritization-based Collaborative Task scheduling Algorithm for Cloud-side-end of Distribution Networks

A Dynamic Prioritization-based Collaborative Task Scheduling...

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Smart Grid and Energy Internet (SGEI), International Conference on

作者： Zhengrong Wu Hua Li Yuxuan Yang Yunhua Liu Furong Yin CHINA SOUTHERN POWER GRID Guangzhou Guangdong China Digital Power Grid Technology (Guangdong) Co. Ltd China Southern Power Grid Guangzhou Guangdong China

ISBN: (数字)9798350368314

ISBN: (纸本)9798350368321

Affected by resource configuration and task request characteristics, the rationality of distribution network task scheduling is directly related to its operating status. For this reason, a research on cloud-edge collaborative task scheduling algorithm for distribution network based on dynamic priority is proposed. On the basis of using a unified semantic data model to solve the problem of information interaction obstacles, through information modeling, a consistent model framework is provided for core information such as terminal device identification and operating status; after taking into account the inherent static priority of the task (reflecting the basic importance of tasks), it incorporates the consideration of the task’s tolerance of deadlines, completes the dynamic priority division of tasks, and efficiently utilizes the limited computing resources of the object layer in the model while meeting the timeliness requirements of each task as much as possible. principles to perform task scheduling. In the test results, the load deviation is basically stable within 0.15, with the maximum value only 0.16; the ratio of cost to output is always lower than 0.84. Compared with the control group, it has significant advantages in stability and balance respectively.

关键词： Cloud computing scheduling algorithms Heuristic algorithms Collaboration Distribution networks Dynamic scheduling Data models Stability analysis Smart grids Security

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Leveraging CNN-CPU scheduling for Enhanced Resource Management in Storage Edge Network Frameworks

Leveraging CNN-CPU Scheduling for Enhanced Resource Manageme...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Mazen Alwahedi Chunjiang Duanmu College of Computer Science and Technology Zhejiang Normal University Jin Hua City China College of Physics and Electrical Information Engineering Zhejiang Normal University Jin Hua City China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Because the landscape of data processing created is likely to rise considerably in the next years due to the proliferation of devices that require data processing at edge computing, it is imperative for data processing issues at the network's edge storage frame. The goal of this study is to find out how deep learning can be used to speed up operation processing. Effective resource management is essential in the age of edge computing to maximize the performance of storage edge networked frameworks. The purpose of this work is to investigate how resource management in such systems could be improved by utilizing CNN-CPU scheduling approaches. Our goal is to optimize job allocation, prioritization, and scheduling in storage edge situations by fusing Convolutional Neural Networks (CNNs) with CPU-based scheduling methods. The suggested method aims to improve overall system performance, reduce latency, and maximize resource use. We illustrate the efficacy of CNN-CPU scheduling in improving resource management in storage edge networked frameworks with experimental validation and performance analysis. This research contributes to advancing the capabilities of edge computing systems by leveraging deep learning techniques for efficient resource allocation and management. The proposed approach seeks to overcome the limitations of traditional scheduling methods by leveraging the power of deep learning. CNNs are renowned for their ability to extract meaningful features from data, and by harnessing this capability, we can optimize resource utilization and minimize latency in storage edge networked frameworks. Through a combination of CNN-based feature extraction and CPU-based scheduling decisions, we strive to achieve efficient and intelligent resource management. This study investigates the potential of CNN-CPU scheduling techniques to address these challenges and enhance resource management in such frameworks. By integrating Convolutional Neural Networks (CNNs) with CPU-based sc

关键词： Deep learning scheduling algorithms System performance Feature extraction Data processing scheduling Resource management

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A Workflow scheduling Algorithm Based on Linear Evaluation for Dynamic Distributed Computing Environments

A Workflow Scheduling Algorithm Based on Linear Evaluation f...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xin Li Zhan Zeng Yuxin Chen Jinglong Deng Yue Hong Wei Zheng Department of Computer Science Xiamen University Xiamen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The development of computer architecture towards multi-core processors brings new opportunities and challenges for efficient task scheduling in scientific workflows. This paper investigates the key challenges of scheduling scientific workflows, typically represented as Directed Acyclic Graphs (DAGs), in multi-core and dynamic distributed computing environments. Traditional static and dynamic scheduling algorithms face limitations in handling complex DAG structures and complex computational environments. To address these issues, we propose an improved LinearPB algorithm that simplifies the node priority calculation through linear evaluation, thereby reducing computational overhead and enhancing scheduling performance. Our extensive experiments demonstrate that the LinearPB algorithm significantly reduces execution time, outperforms existing algorithms in most cases.

关键词： Directed acyclic graph Runtime scheduling algorithms Multicore processing Heuristic algorithms Computer architecture Dynamic scheduling Distributed computing Faces Edge computing

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FODAS: A Novel Reinforcement Learning Approach for Efficient Task scheduling in Fog Computing Network

FODAS: A Novel Reinforcement Learning Approach for Efficient...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Ganesan Nagabushnam Yundo Choi Kyong Hoon Kim School of Computer Science and Engineering Kyngpook National University Daegu South Korea

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

In heterogeneous fog-cloud computing networks, efficiently scheduling aperiodic tasks is an NP-hard problem, particularly when aiming to minimize makespan, adhere to deadlines, and conserve energy. This paper introduces a novel scheduling algorithm, Fog-Optimized Deadline-Adaptive scheduling (FODAS), which combines Earliest Deadline First (EDF) principles with Deep Multi-Agent Reinforcement Learning, incorporating Proximal Policy Optimization (PPO) and Recurrent Neural Networks (RNN). FODAS is specifically designed to manage aperiodic tasks in heterogeneous fog-cloud environments, prioritizing deadline adherence and energy efficiency. The proposed algorithm begins by collecting tasks into a global scheduling queue, and sorting them by their deadlines. It incorporates three homogeneous schedulers within a heterogeneous framework, ensuring tasks meet their deadlines and achieve notable energy savings. Key performance metrics such as deadline meeting rate, makespan, and energy savings are evaluated, comparing FODAS against single-agent reinforcement learning algorithms such as PPO and Asynchronous Advantage Actor-Critic (A3C). Our findings reveal that FODAS significantly improves the rate of meeting deadlines by up to 18% compared to the conventional algorithms. Additionally, it delivers substantial energy savings, with improvements of up to 80% in certain setups, and markedly decreases makespan, achieving reductions of up to 57.3% compared to traditional algorithms. The proposed algorithm also demonstrates exceptional operational efficiency, reducing the time required for scheduling tasks, particularly in high-density node networks. These results underscore the effectiveness of FODAS in managing complex task scheduling within fog-cloud computing environments.

关键词： Measurement Recurrent neural networks Processor scheduling scheduling algorithms Scalability Energy conservation Reinforcement learning scheduling Resource management Sorting

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Data Blocks scheduling Schemes for Regional Distributed Storage Networks

Data Blocks Scheduling Schemes for Regional Distributed Stor...

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IEEE International Conference on Blockchain (Blockchain)

作者： Tongtong Cheng Yang Liu Cheng Chi Zihang Yin China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

In the Web3, the data distributed storage has become a crucial method for secure, reliable, and efficient data storage, and an efficient distributed scheduling and distribution strategy is essential for achieving rapid data response. This paper proposes a distributed data scheduling strategy that improves the firefly algorithm to achieve optimal scheduling path selection for regional data storage. By considering the storage nodes spatial status, it enables the rapid distribution and storage of data blocks. The storage path is implemented by smart contracts and stored on the blockchain with the data block identifier. Through simulation, our proposed scheduling algorithm demonstrates better performance and higher trustworthiness compared to traditional storage models.

关键词： scheduling algorithms Smart contracts Memory Optimal scheduling scheduling Stability analysis Blockchains

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Data scheduling Algorithm of Mobile Education Streaming Media Based on Improved Random Forest

Data Scheduling Algorithm of Mobile Education Streaming Medi...

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Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), Asia-Pacific Conference on

作者： Xiaoming Wu Wuxi Vocational Institute of Commerce Wuxi Jiangsu China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

With the rapid development of the Internet, the traditional media market has been integrated with the computer network, resulting in many new applications such as online video on demand, distance education, network television and so on. Streaming media is a technology that can play audio, video and other multimedia files on the Internet in real time without downloading and waiting. This paper builds a streaming media data scheduling model based on the improved random forest algorithm. First, it analyzes some indicators that affect the data flow, and then analyzes the accuracy of the improved random algorithm and its traditional algorithm under different decision tree numbers according to the improved forest algorithm. The gmean value of an improved random forest algorithm proposed in this paper is significantly higher than that of the traditional algorithm, and the fvalue is close to 0.92. It shows that the prediction accuracy of default and non-default samples is improved by this algorithm. It can improve the economic benefit and promote the establishment of an economical, smooth and efficient streaming media communication transmission system.

关键词： Economics Accuracy scheduling algorithms Education Software algorithms Streaming media Prediction algorithms

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