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检索条件"主题词=Scheduling algorithms"

共 8602 条记录，以下是1661-1670 订阅

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Research on the Collaborative Mechanism Model of Robot Intelligent Identification and scheduling Algorithm in 5G Interconnection

Research on the Collaborative Mechanism Model of Robot Intel...

引用

IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Peng Liu Kai Qian Jiongting Jiang Zinan Li Zhenyu Bao State Grid Zhejiang Electric Power Co. Ltd. Ningbo Power Supply Company Ningbo China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper studies the collaborative mechanism model of robot intelligent identification and scheduling based on multi-agent system, focusing on how to improve the real-time perception, identification and scheduling capabilities of robots through the high bandwidth and low latency characteristics of 5G network. In terms of system design, a scheduling strategy based on distributed collaborative algorithm is adopted to achieve efficient scheduling of robot groups in complex environments through information sharing and autonomous decision-making in multi-agent system. Model simulation verifies the adaptability and stability of the collaborative algorithm in different scenarios, and demonstrates the advantages of the algorithm in recognition accuracy, scheduling efficiency and network delay through precise data analysis. The simulation results show that the robot scheduling response time is significantly shortened in the 5 G network environment, and the overall system efficiency is improved by more than $\mathbf{2 0 \%}$. This study provides theoretical support for robot intelligent scheduling and opens up a new path for the application of 5 G technology in industrial scenarios.

关键词： Job shop scheduling Accuracy 5G mobile communication Service robots scheduling algorithms Simulation Collaboration Stability analysis Real-time systems Multi-agent systems

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Graph Coding Aided Deep Reinforcement Learning for Fault-tolerant Workflow scheduling

Graph Coding Aided Deep Reinforcement Learning for Fault-tol...

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International Conference on Communication Technology (ICCT)

作者： Jiangming Li Sen Li Jian Tan Siyu Cheng Dong Jin Shuangwu Chen Jian Yang University of Science and Technology of China Hefei China Guangxi Key Laboratory of Digital Infrastructure Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Multi-access Edge Computing (MEC), as a novel computing paradigm, aims to execute workflows at the edge, thereby minimizing service latency. However, due to the high failure rate of edge servers, how to ensure reliable execution of workflows and improve service quality is a challenge. To address this challenge, this paper proposes a novel Deep Reinforcement Learning (DRL) based fault-tolerant workflow scheduling algorithm. The algorithm consists of an encoder based on a Graph Attention Neural Network (GAT) and a two-layer decoder based on a Long Short-Term Memory (LSTM) network. The encoder models the workflow as a Directed Acyclic Graph (DAG), capturing the dependencies between subtasks and learning the inherent correlation among their states. The decoder takes into account the influence of predecessor tasks on successors and generates scheduling actions for all subtasks concurrently through multi-step decision-making. Simulation results demonstrate that the proposed algorithm effectively reduces workflow makespan and resource consumption while fulfilling workflow reliability requirements.

关键词： Fault tolerance Multi-access edge computing scheduling algorithms Simulation Fault tolerant systems Neural networks Deep reinforcement learning Decoding Servers Long short term memory

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Implementation and Application of Wireless Node Device AD Hoc Network Technology Based on LoRa Communication 8

Implementation and Application of Wireless Node Device AD Ho...

引用

8th Asia Conference on Power and Electrical Engineering, ACPEE 2023

作者： Gao, Fang Dong, Jianwei Yin, Mengtao Tang, Xi Beijing Sifang Engineer Co. Ltd Monitoring R&D Department Beijing China

ISBN: (纸本)9798350345520

In order to ensure that wireless node devices access enough terminal sensors in the sensing layer and establish a wireless terminal network based on LoRa to provide continuous sensing data for the upper layer, a hardware architecture scheme of high-performance processor + cortex-M4 dual processor is proposed, accurate slot allocation and scheduling algorithm is designed to realize terminal access technology, and unordered tree data structure is used to construct wireless network routing algorithm to realize ad hoc network technology. The wireless node equipment can support 4000 sensor terminals to access, has accurate DRX scheduling, multi-hop can reach more than 10 levels, and can meet various application scenarios of power transmission and transformation. By using networking technology, wireless node equipment can realize the networking of terminal devices through walls and send sensing data, which brings convenience to site construction, reduces the maintenance cost after system operation, and promotes the more intelligent and networked development of the wireless sensing system. © 2023 IEEE.

关键词： scheduling algorithms

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Memoization-based Task scheduling Algorithm for Smart Healthcare Systems

Memoization-based Task Scheduling Algorithm for Smart Health...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Gunjan Beniwala Anita Singhrova Maharaja Surajmal Institute of Technology (GGSIPU) Haryana India DCRUST Murthal Haryana India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The rapid increase in the number of sensing devices within the field of Internet of Things (IoT) has led to the generation of a vast amount of data on a daily basis. Cloud computing offers processing, storing capabilities, and other services like data analysis to handle such vast data volumes. However, real-time applications like online gaming, video surveillance, and the healthcare sector often require low latency and efficient bandwidth utilization, which can be challenging to achieve with traditional cloud computing. To address these challenges, Fog computing has been transpired as a solution. Fog computing extends the functionalities of the cloud computing paradigm by distributing computing resources near to the network’s edge. Since Fog devices are located at the network’s edge, they are often resource-constrained, making it crucial to make efficient decisions regarding job assignments and scheduling on these devices. A proficient job scheduling algorithm has the potential to decrease energy consumption, response time, and the volume of application requests. This work proposes a smart gateway framework with a proposed Dynamically Prioritized Memoization-based Best Fit Algorithm (DPMBFA) for efficient resource allocation in fog-cloud environments. We present a healthcare-based case study to schedule the task optimally to the available resources, hence increasing the Quality of Service (QoS) in IoT gateways. Latency, energy consumption, and throughput are considered for performance evaluation using YAFS, a simulation tool compared to existing approaches. The results proved that the proposed framework outperformed the existing algorithms.

关键词： Cloud computing Energy consumption scheduling algorithms Quality of service Medical services Logic gates Video surveillance

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Optimization of Monitoring and scheduling Strategy for Distributed Docker Cluster

Optimization of Monitoring and Scheduling Strategy for Distr...

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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Liangbin Zhang Shibin Liang Yuanming Wang Xinwei Liu College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China College of Culture and Communication Zhejiang Wanli University Ningbo China

ISBN: (数字)9798331531171

ISBN: (纸本)9798331531188

Swarm is an official native cluster management tool for Docker, which integrates multiple Docker nodes into a virtual single host and provides container distribution and scheduling management capabilities. Analyzing the overall architecture of Swarm, Spread as the default scheduling strategy encounters issues such as resource fragmentation and load balancing, a statically balanced improved container scheduling algorithm is proposed. A Swarm cluster monitoring system is constructed to monitor the performance of containers and node resources in real-time. Based on the resource request amount of containers to be scheduled, the weighted average of the resource utilization rate and load balance of candidate nodes is calculated as the scheduling weight. Experimental results show that compared with Spread strategy, the proposed method can reduce resource fragmentation on nodes, achieve better cluster balance, and enhance the overall performance of the cluster.

关键词： scheduling algorithms Containers Load management scheduling Real-time systems Resource management Monitoring Optimization Information systems

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Channel-Aware Rotating Priority Queue scheduling with Delay Deadline over Wireless Links

Channel-Aware Rotating Priority Queue Scheduling with Delay ...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yankai Zhang Wei Wang Xinglin Yang Yitu Wang Wenkai Zhang Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Electrical and Information Engineering North Minzu University Yinchuan China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Conventional Rotating Priority Queues (RPQ) scheduling adopted in Time-Sensitive Networks (TSN) fails to achieve desired performance over time-varying wireless channel. In this paper, we propose a channel-aware RPQ scheduling algorithm to meet explicit delay deadlines in wireless context. Specifically, we first construct an improved channel-aware RPQ framework. Different to the classic RPQ scheduler, we allocate the transmission opportunity to various devices before adopting RPQ scheduling for each device. For the scheduling of device transmission time, we establish a weighted queue for each device by learning their effects to the violation of delay deadlines, and propose a low-complexity scheduling algorithm by stabilizing the virtual outage queue via Lyapunov optimization. As the mapping to the weighted queue is non-smooth, we enhance the soft threshold function by employing momentum to accelerate the convergence rate. Simulation results verify the performance improvements of the proposed scheduling algorithm,

关键词： Performance evaluation Q-learning scheduling algorithms Heuristic algorithms Wireless networks Simulation Signal processing algorithms Signal processing Delays Resource management

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A System Utility Optimization-based Task Offloading and Computing scheduling Algorithm for Low Earth Orbit Satellite Networks

A System Utility Optimization-based Task Offloading and Comp...

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IEEE International Conference on Communications (ICC)

作者： Yin Wang Kang'An Gui Rong Chai School of Communications and Information Engineering Chongqing University of Posts and Telecommunications China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we investigate the joint task offloading and computing scheduling in low earth orbit (LEO) satellite networks, which consists of a number of ground terminals (GTs) and multiple LEO satellites. Considering the characteristics of tasks and the energy consumption of task execution, we define a system utility function, and formulate the problem of task offloading and computing scheduling as a constrained system utility function maximization problem. The formulated optimization problem is a mixed integer non-linear programming problem which cannot be solved conveniently. To solve the formulated optimization problem, we first propose local computing scheduling algorithm to determine the task local partition strategy. And then priority-based offloading and computing scheduling algorithm is proposed to determine the task offloading strategy. The effectiveness of the proposed algorithm is verified through simulations in satellite tool kit (STK) and MATLAB.

关键词： Satellites scheduling algorithms Simulation Low earth orbit satellites Programming scheduling Partitioning algorithms

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An Improved Genetic Optimization Algorithm for scheduling Serverless Application Jobs in Public Cloud Environments

An Improved Genetic Optimization Algorithm for Scheduling Se...

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International Conference on Computer Supported Cooperative Work in Design

作者： Lingjie Pei Li Pan Shijun Liu School of Software Shandong University Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Due to the advantages of ease of management, high elasticity, and low prices, serverless computing has gained more and more momentum in recent years. Serverless computing allows developing and deploying applications in the form of Function-as-a-Service (FaaS). Application service providers can benefit greatly from deploying and running their applications in public serverless FaaS cloud platforms. Currently, public FaaS cloud platforms generally offer multiple computation resource configurations for application service providers to select for deploying and executing their functions with a pay-per-use billing. Application service providers can select a function instance with a higher processing capability to achieve a shorter job completion time, but it also incurs a higher cost, while service providers often have budget constraints for running their jobs. Thus, when scheduling serverless application jobs in public FaaS cloud platforms, application service providers need to trade off between the job completion time and the cost, which is generally an NP-hard problem. To address these issues, in this paper we propose an improved genetic optimization algorithm for scheduling serverless application jobs in public FaaS clouds, which can achieve the minimization of the job completion time within the given budgets. Specially, during the evolution process of our genetic scheduling algorithm, we use a greedy strategy to search for optimal schedules. Through experiments running on both synthetic and real-world data, we validate the efficiency and effectiveness of our proposed genetic optimization based scheduling algorithm in minimizing the job completion time within budget constraints.

关键词： Costs scheduling algorithms NP-hard problem Serverless computing Optimal scheduling Genetics Minimization

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Optimization of Multi-Level Nurse scheduling: A Biorhythm-Based Fatigue Model Integrated with PFMEA for Enhancing Service Reliability

Optimization of Multi-Level Nurse Scheduling: A Biorhythm-Ba...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Li Huang Economics and Management school Panzhihua University Panzhihua China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

This paper focuses on the optimization of multi-level nurse scheduling, addressing the issue that traditional methods often overlook nurse fatigue risks and their impact on service system reliability. An innovative solution is proposed, which involves the introduction of PFMEA (Process Failure Modes and Effects Analysis) as a risk management tool, combined with biorhythm theory, to construct a biorhythm-based fatigue model for nurse scheduling. This model is integrated into the scheduling optimization process to achieve rational allocation of multi-level nurse resources. To assess risks and solve the complex scheduling problem, this study adopts the entropy-weighted PFMEA method for risk quantification and designs a hybrid improved algorithm that combines the advantages of NSGAII (Non-dominated Sorting Genetic Algorithm II) and MOGWO (Multi-Objective Grey Wolf Optimizer). An empirical study was conducted in a county-level hospital to validate the effectiveness of the model. The results demonstrate that the model not only significantly optimizes the scheduling plan but also alleviates nurse fatigue, reduces risks, and ultimately enhances the reliability of the medical service system. This research brings new perspectives and methodologies to the field of multi-level nurse scheduling in medical services. Future studies can further explore diversified scheduling modes, more refined fatigue assessment methods, and intelligent scheduling algorithms to continuously improve the quality and efficiency of medical services.

关键词： Analytical models scheduling algorithms Biological system modeling Medical services Fatigue Reliability engineering scheduling Risk management Optimization Sorting

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F4: Fast, Flexible and stable-Fortified User-Space Thread scheduling Framework

F4: Fast, Flexible and stable-Fortified User-Space Thread Sc...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Xiaobo Zheng Zihao Duan Shiyi Li Haojun Hu Wen Xia Harbin Institute of Technology Shenzhen

ISBN: (数字)9798331520502

ISBN: (纸本)9798331520519

Thread scheduling is a core functionality of operating systems, optimizing CPU resource utilization through dedicated algorithms. However, modifying kernel-level scheduling in Linux is complex and error-prone. User-space scheduling offers a promising alternative, yet existing implementations struggle with speed, flexibility, and robustness. This paper introduces F4, a user-space thread scheduling framework that shifts scheduling algorithm deployment from the kernel to user space. By allowing the kernel to delegate scheduling decisions through interfaces like system calls and eBPF hooks, F4 enables user space to develop and implement its own scheduling strategies. This separation of scheduling mechanisms and policy definitions enhances efficiency and adaptability. F4 improves communication through message queues and the shoot system, accelerates execution with inter-processor interrupts (IPI), and increases interference resistance through dynamic priority adjustments. These innovations allow F4 to achieve fast, flexible, and stable-fortified thread scheduling in user space, addressing the limitations of current user-space scheduling solutions.

关键词： Resistance Technological innovation scheduling algorithms Linux Production scheduling Robustness Resource management Kernel Message systems

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