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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Tao Chen The Business School Zhengzhou Technology and Business University China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

The complexity and uncertainty of enterprise operations make human resource emergency dispatch systems play an increasingly important role in ensuring that organizations can respond quickly to emergencies. An effective scheduling system can optimize employee allocation, improve the efficiency and effectiveness of emergency response. This study explored the application of collaborative filtering algorithms in human resource emergency scheduling systems, aiming to improve scheduling performance by intelligently matching employee skills with urgent task needs. This study constructed a dataset that includes employee skills, historical scheduling records, and task requirements. Through a series of comparative experiments, this study evaluated the performance of collaborative filtering algorithms, content-based recommendation algorithms, and rule-based scheduling algorithms in three key indicators: matching accuracy, scheduling response time, and employee satisfaction. The conclusion showed that the collaborative filtering algorithm had achieved significant results in the experiment. The accuracy of the collaborative filtering algorithm was as high as 97%, with a minimum response time of 99ms and a maximum response time of 148ms. Compared to the response times of content-based 234ms and rule-based 289ms, the collaborative filtering algorithm demonstrated faster processing speed. The results of the employee satisfaction survey also indicated that the satisfaction rating of employee scheduling under the collaborative filtering algorithm could reach up to 100, maintaining an overall high level, significantly higher than the other two algorithms. These data strongly demonstrate the superiority of collaborative filtering algorithms in human resource emergency scheduling systems.

关键词： Surveys Accuracy Uncertainty scheduling algorithms Collaborative filtering Organizations Filtering algorithms scheduling Time factors Resource management

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Delay and Overhead Efficient Transmission scheduling for Federated Learning in UAV Swarms

Delay and Overhead Efficient Transmission Scheduling for Fed...

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IEEE Conference on Wireless Communications and Networking

作者： Duc N. M. Hoang Vu Tuan Truong Hung Duy Le Long Bao Le INRS University of Québec Montréal QC Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper studies the wireless scheduling design to coordinate the transmissions of (local) model parameters of federated learning (FL) for a swarm of unmanned aerial vehicles (UAVs). The overall goal of the proposed design is to realize the FL training and aggregation processes with a central aggregator exploiting the sensory data collected by the UAVs but it considers the multi-hop wireless network formed by the UAVs. Such transmissions of model parameters over the UAV-based wireless network potentially cause large transmission delays and overhead. Our proposed framework smartly aggregates local model parameters trained by the UAVs while efficiently transmitting the underlying parameters to the central aggregator in each FL global round. We theoretically show that the proposed scheme achieves minimal delay and communication overhead. Extensive numerical experiments demonstrate the superiority of the proposed scheme compared to other baselines.

关键词： Training Wireless sensor networks Federated learning scheduling algorithms Wireless networks Simulation Spread spectrum communication

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Throughput Optimization in Cache-aided Networks: An Opportunistic Probing and scheduling Approach

arXiv

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arXiv 2024年

作者： Zhang, Zhou Atapattu, Saman Wang, Yizhu Di Renzo, Marco Tianjin China School of Engineering RMIT University MelbourneVIC Australia Université Paris-Saclay 3 Rue Joliot Curie Gif-sur-Yvette91190 France

This paper addresses the challenges of throughput optimization in wireless cache-aided cooperative networks. We propose an opportunistic cooperative probing and scheduling strategy for efficient content delivery. The strategy involves the base station probing the relaying channels and cache states of multiple cooperative nodes, thereby enabling opportunistic user scheduling for content delivery. Leveraging the theory of Sequentially Planned Decision (SPD) optimization, we dynamically formulate decisions on cooperative probing and stopping time. Our proposed Reward Expected Thresholds (RET)-based strategy optimizes opportunistic probing and scheduling. This approach significantly enhances system throughput by exploiting gains from local caching, cooperative transmission and time diversity. Simulations confirm the effectiveness and practicality of the proposed Media Access Control (MAC) strategy. © 2024, CC BY-NC-ND.

关键词： scheduling algorithms

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Optimizing Resource Management in Hadoop YARN for Efficient Allocation, Utilization, and scheduling

Optimizing Resource Management in Hadoop YARN for Efficient ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： B N Arunakumari J M Suhas Abha Nair BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Hadoop YARN(Yet Another Resource Negotiator) resource manager plays a critical role in the efficient functioning of Hadoop clusters by managing resources and scheduling tasks. This research paper explores various improvements to the Hadoop YARN resource manager to enhance resource allocation, utilization, and scheduling. The objective is to tackle the challenges posed by contemporary big data applications and workloads, enhancing overall system performance and responsiveness. This is achieved through the implementation of a capacity scheduling algorithm, emphasizing resource pre-emption. The aim is to distinguish and enhance efficiency of Capacity scheduler compared to existing algorithms such as FIFO and Fair scheduling using grep algorithm, backed by a thorough study of the same.

关键词： scheduling algorithms File systems System performance Big Data applications scheduling Resource management Yarn

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Psched: An Efficient Task scheduling Approach Considering Atomic Tasks in Self-Powered Systems

Psched: An Efficient Task Scheduling Approach Considering At...

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IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Xuejin Li Keni Qiu College of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350388558

ISBN: (纸本)9798350388565

Energy harvesting technology is a promising approach to overcome the limitations of traditional battery-powered IoT devices, enabling the devices of longer lifetime and easier maintenance. However, the unstable nature of harvested energy can compromise system performance and energy efficiency, particularly for systems tasked with scheduling large atomic tasks that are more vulnerable to energy fluctuations. In this paper, we propose a heuristic scheduling algorithm, Psched, which is based on dual-period power prediction and takes the characteristics of atomic tasks into account. The prediction model achieves an 87% accuracy rate in predicting TV-RF signals' power. Additionally, we develop an optimal scheduling algorithm (OPT) to establish a performance baseline. Experimental evaluations show that proposed Psched reaches 93% of the OPT's performance in terms of the weighted number of successful tasks, which is 12%–23% higher than that of the other algorithms. Moreover, the proposed Psched also performs better than other heuristic algorithms in terms of task success rate and energy efficiency.

关键词： Schedules scheduling algorithms Nonvolatile memory System performance Heuristic algorithms Optimal scheduling Predictive models Prediction algorithms Energy efficiency Energy harvesting

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A Large-Scale Multi-Objective Flexible Job-Shop scheduling Algorithm Based on Differential Evolution and Angle-Based Weight Vector Assignment

A Large-Scale Multi-Objective Flexible Job-Shop Scheduling A...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Li Xu Guo-Sheng Hao Xia Wang Shi-Jin Ren Yi Zhu school of computer science and technology jiangsu normal university Xuzhou China jiangsu wisdom-driven research institute co. ltd Xuzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Traditional large-scale multi-objective evolutionary algorithms perform poorly on the large-scale Flexible Job-Shop scheduling Problems (FJSP), which are characterized by discrete decision variables, large-scale decision space, and sparse non-dominated solutions. In order to improve the sparsity of non-dominated solutions and enhance the diversity as well as convergence of the population, a Large-Scale Multi-Objective Flexible Job-Shop scheduling Algorithm Based on Differential Evolution and Angle-Based Weight Vector Assignment (MOEA/D-ADE) is proposed. The optimization objectives of this scheduling problem are minimizing the maximum completion time, the total machine energy consumption, and the mean squared error of the machine load. Firstly, an initial population is formed by screening out high-quality individuals from the decision space based on their individual process equilibrium coefficients using a knowledge-based solution filtering approach. Secondly, to obtain non-dominated solutions, the proposed algorithm employs Differential Evolution (DE) to enhance its global search capabilities. Then it calculates the angle between the offspring and the corresponding weight vector of the parent to achieve the reallocation of the offspring. Finally, the algorithm realizes adaptive adjustment of the mutation probability based on the diversity of individuals in the neighborhood. The proposed method is applied to 10 large-scale FJSP and compared with 3 multi-objective evolutionary algorithms, and the effectiveness of the algorithm is finally verified.

关键词： Energy consumption scheduling algorithms Filtering Knowledge based systems Evolutionary computation Vectors scheduling Complex systems Optimization Convergence

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Polling Resource scheduling for Internet of Vehicles Based on U-Net Semantic Segmentation

Polling Resource Scheduling for Internet of Vehicles Based o...

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International Conference on Computer and Communications (ICCC)

作者： Junjie Wang Lei Lei Xiaoqin Song Nanjing University of Aeronautics and Astronautics Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics National Mobile Communications Research Laboratory Southeast University

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the continuous development of Internet of vehicles (IoV) and intelligent transportation system (ITS), infor-mation interaction and resource sharing between vehicles have become increasingly important. However, the resource scheduling problem in IoV has been the main bottleneck restricting its fur-ther development. This paper proposes a semantic segmentation method based on U-Net deep learning architecture and applies it to the polling scheduling problem in the IoV environment to achieve the optimal allocation of resources. The simulation results show that compared with the traditional polling scheduling method, the semantic segmentation method combined with U-Net can significantly save bandwidth resources and reduce image transmission delay. Specifically, our algorithm achieves a 97 % bandwidth savings and a 90% reduction in image transmission delay. In the corresponding semantic segmentation algorithm, the U - Net algorithm also outperforms the SegNet algorithm model in real-time transmission of Internet of Vehicles image resources, which verifies its potential in resource scheduling optimization.

关键词： scheduling algorithms Semantic segmentation Image communication Simulation Bandwidth scheduling Real-time systems Delays Resource management Internet of Vehicles

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Research on Cloud-Fog Computing Task scheduling Based on Classification Mining

Research on Cloud-Fog Computing Task Scheduling Based on Cla...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Lindong Liu Xiaoning Zeng Yuehai Qin School of Computer Science Guangdong University of Education Guangzhou China School of Mathematics Guangdong University of Education Guangzhou China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In the Fog Computing environment, it is necessary to study the task scheduling problem of Cloud-fog in order to meet the resource requirements of task scheduling, reduce data transmission during task scheduling, and improve the throughput of Fog Computing systems. By improving and optimizing the classification mining algorithm, the classification mining algorithm is integrated into the Cloud and Fog task scheduling model, integrating the characteristics of Fog and Cloud. A Cloud-Fog fusion task scheduling model based on classification mining and CFCM task scheduling algorithm are proposed in the paper. The algorithm first schedules the corresponding tasks directly to the Fog layer or Cloud layer based on the generated classification rules, and then transfers the tasks to specific computing nodes of Cloud layers or Fog layer. Simulation experiments showed that the CFCM algorithm has better performance in reducing task execution time and average waiting time compared to similar algorithms.

关键词： Schedules Processor scheduling scheduling algorithms Computational modeling Throughput scheduling Classification algorithms Data mining Information technology Edge computing

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Task scheduling Algorithm for Heterogeneous Computing Power Network

Task Scheduling Algorithm for Heterogeneous Computing Power ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Guodong He Xiaohui Li Siting Lv Yuanyuan Zhou ZhiGang Ni Xingbo Chen Xidian University China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

To address the issue of inefficient processing of computational task requests in heterogeneous Computing Power Network, a novel optimal pathfinding algorithm based on adap-tive task division is proposed. By modeling computing network resources and computational tasks as a Directed Acyclic Graph (DAG), a latency prediction model is established. This model, with latency as weights, facilitates adaptive task division within the DAG, and employs a reinforcement learning algorithm for optimal pathfinding. This approach matches computing nodes on the optimal path, enabling edge-to-edge computing and collabo-rative transmission, effectively reducing task processing latency. Simulation experiments demonstrate a significant reduction in the average system latency, with a maximum acceleration ratio of 5.8, showcasing improved performance in complex environments.

关键词： Adaptation models Vehicular and wireless technologies scheduling algorithms Computational modeling Simulation Predictive models Prediction algorithms

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Joint Power Control and Device scheduling in Federated Learning with Energy Harvesting

Joint Power Control and Device Scheduling in Federated Learn...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Boyuan Zhang Ning Lu Department of Electrical and Computer Engineering Queen's University Kingston ON Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Federated Learning (FL) is emerging as a promising approach for enabling collaborative machine learning without centralizing data, particularly within Internet of Things (IoT) networks. Despite its potential, the challenge of reducing communication delay and energy consumption in client-server transmissions is considered a critical challenge in wireless network implementations of FL. Moreover, the capability of many IoT devices to harvest energy introduces a novel dimension to optimizing FL efficiency, which remains underexplored. In this paper, we present POLISH, a novel POwer controL, and devIce scheduling algorithm derived from an optimization problem designed to navigate a tripartite trade-off between minimizing communication delay (specifically, the transmission time for model updates), reducing total energy consumption, and maximizing device participation. Through simulations, POLISH has shown superior performance in reducing communication delay and energy consumption when compared to existing benchmarks, highlighting its effectiveness in enhancing FL performance in energy-constrained IoT networks.

关键词： Performance evaluation Energy consumption scheduling algorithms Federated learning Power control Signal processing algorithms Delays Internet of Things Energy harvesting Optimization

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