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检索条件"主题词=Scheduling algorithms"

共 8561 条记录，以下是611-620 订阅

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University Timetable scheduling: A Heuristic and Machine Learning Approach 5th

University Timetable Scheduling: A Heuristic and Machine Lea...

引用

5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Priya, L. Kamatchi Bhat, Tushar Tsephel, Tenzin Rethwik, Voolla Sai Bansal, Tanya PES University Bangaluru India

ISBN: (纸本)9789819747108

The University Class scheduling Problem stands as a pivotal and intricate challenge within academia. A proficient university timetable holds the key to seamless academic operations, necessitating the delicate balance of multiple constraints like teacher availability and subject-hour allocations. This NP-Hard problem's complexity arises from its diverse limitations. Manual timetable creation presents a formidable task for educators and evaluators alike, underscoring the need for a system that fosters a balanced and equitable learning environment. This study introduces a novel university timetable scheduling algorithm adept at accommodating dynamic constraints, thus addressing the demand for adaptable and responsive scheduling solutions. By harnessing the potential of heuristic and machine learning algorithms, our Timetable scheduling System aims to efficiently generate schedules while adhering to specified constraints that should not be compromised, ensuring the production of high-quality timetables. Ultimately, this endeavor strives to streamline the timetable creation process, liberating educators to focus on delivering superior education and optimizing student outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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Adaptive scheduling Using the MQTT Protocol in the IoT Based on Environmental Sensing and Network Load

Adaptive Scheduling Using the MQTT Protocol in the IoT Based...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Chakravarty, Sujit Kumar Batra, Amul Singh, Neeti Kumar, Gajendra Ministry of Earth Science India Meteorological Department New Delhi India

ISBN: (纸本)9798331529833

Adaptive protocols that react to shifting network and ambient conditions have been developed in response to the Internet of Things' (IoT) increasing need for effective communication. The MQTT protocol is used in this study's adaptive scheduling strategy to maximize IoT communication by modifying transmission intervals in response to network congestion and environmental variables like pressure, temperature, and humidity. Using a mathematical model Adaptive scheduling enhances throughput, lowers latency, and improves energy economy in contrast to fixed scheduling which has trouble adapting. According to experimental data, adaptive scheduling performs better than fixed techniques, offering notable energy savings, decreased latency, and increased throughput. This opens the door for more effective and sustainable IoT communication. © 2025 IEEE.

关键词： scheduling algorithms

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A V2X-Based Distributed Virtual Traffic Light scheduling Algorithm 24

A V2X-Based Distributed Virtual Traffic Light Scheduling Alg...

引用

5th International Conference on Computer Science and Management Technology, ICCSMT 2024

作者： Li, Zhaohui Fu, Qi Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9798400709999

Urban traffic congestion not only increases fuel consumption and pollution but also causes inconvenience to residents. Although traditional physical traffic light systems provide a certain solution, their reliance on complex road infrastructure results in high maintenance and installation costs. In contrast, virtual traffic light systems, as a self-organizing traffic control system, do not rely on road infrastructure. A V2X-based distributed virtual traffic light scheduling algorithm has been designed and implemented, aiming to optimize vehicle traffic efficiency and reduce CO2 emissions. The research adopts hierarchical and clustering methods, combined with a priority scheduling strategy and Look-Ahead decision tree algorithm, to improve the overall efficiency of traffic flow. Simulation results show that this method performs excellently under different traffic densities. Under balanced traffic conditions, the scheduling algorithm increases the number of vehicles passing per minute by approximately 7.31% compared to the uncontrolled system and by approximately 8.28% compared to the traditional traffic light system;it also reduces the stop-start ratio per vehicle by approximately 9.90% and 20.50%, respectively. Under unbalanced traffic conditions, the number of vehicles passing increases by approximately 9.44% and 2.91%, and the stop-start ratio decreases by approximately 7.68% and 15.10%. By reducing the stop-start ratio per vehicle, fuel consumption can be effectively lowered, thereby reducing CO2 emissions. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： scheduling algorithms

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A Cache scheduling Method Based on Adaptive Expiration for Data Process System 7th

A Cache Scheduling Method Based on Adaptive Expiration for D...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Fang, Bin Zhong, Yibo Sun, Yizhen Tu, Jinjin Jiang, Guang Xiao, Yao Information and Communication Branch of State Grid Hunan Electric Power Company Limited Changsha410007 China Nanjing NARI Information and Communication Technology Nanjing City210000 China School of Software Xinjiang University Urumqi830091 China

ISBN: (纸本)9789819624089

Cache data plays a crucial role in the operation of the power system. In practical applications, the demand for retrieval and queries has surged due to the vast amount of terminal data collected throughout the province. Placing data in the cache effectively improves query speed [1]. However, cache space is limited, and the amount of storable data is small [2]. Without proper control over the data placed in the cache, the hit rate may decrease [3]. Thus this paper proposes a new control method through adaptively setting the expiration time of cached data, which considers the historical traffic distribution and real-time query distribution. The simulation results show that the proposed method can significantly reduce the cache hit rate, which leads to the improvement of cache utilization. In addition, it avoids frequent cache replacements and reduces I/O overhead. © The Author(s) 2025.

关键词： scheduling algorithms

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Heuristics with Local Improvements for Two-Processor scheduling Problem with Energy Constraint and Parallelization 4th

Heuristics with Local Improvements for Two-Processor Sched...

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4th International Conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Zakharova, Yulia V. Sakhno, M.Yu. Sobolev Institute of Mathematics SB RAS Novosibirsk Russia

ISBN: (纸本)9783031812408

Modern computer systems offer some type of parallelism and use restricted resources. Parallel jobs require more than one processor at the same moment of time. Processors may operate at variable speeds. Running a job at a slower speed is more energy-efficient. However, it takes a longer time and affects the performance. We propose and experimentally evaluate heuristic methods based on greedy rules and list scheduling for the speed scaling scheduling problem with the total completion time criterion. Series of instances with various structure are constructed and tested. NP-hard and polynomially solvable cases are identified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： scheduling algorithms

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RFaaS: Function scheduling Across Heterogeneous Clusters 20th

RFaaS: Function Scheduling Across Heterogeneous Clusters

引用

20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Tang, Zhihang Li, Yiming Mao, Zezheng Zhao, Laiping Li, Keqiu College of Intelligence and Computing Tianjin University Tianjin300072 China Intelligent Computing Infrastructure Innovation Center Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9789819628292

The rapid development of cloud computing has attracted a diverse migration of applications. Serverless computing, with its abstract resource management, on-demand billing, and dynamic scaling, has become a popular cloud computing paradigm. X86 signifies traditional computility, while RISC-V symbolizes new potential. Making the most of existing traditional computing resources while exploring the potential of new computility will be a key challenge. The x86 and RISC-V hybrid computility supply will continue for a long time. Task management under diverse instruction set architectures is a critical issue that needs addressing in this evolving landscape. Current research predominantly concentrates on homogeneous instruction set clusters. In this paper, we propose RFaaS, a function job scheduling methodology tailored for RISC-V + X86 heterogeneous instruction set clusters, leveraging the OpenFaaS serverless computing platform. We delve into the affinity traits of function jobs in RISC-V + X86 amalgamated instruction set clusters and devise an affinity classifier alongside an architecture-aware scheduling algorithm. Our methodology dissects scheduling decisions into resource fulfillment and affinity alignment, underpinned by a meticulously crafted update algorithm to uphold and refine job affinities. Experimental results show that RFaaS can provide at least 3x performance improvement and 2.4x throughput increase compared to existing solutions. © IFIP International Federation for Information Processing 2025.

关键词： scheduling algorithms

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Deadline-Aware Task scheduling for Cloud-Fog Systems 28

Deadline-Aware Task Scheduling for Cloud-Fog Systems

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28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Blanco, Gabriela Azucena Ayala Marcelli, Emiliano Nazareno Rojas Duarte, Alex Rubén Stockel Huang, Hsiang-Ting Li, Keng-Ying Chin, Tai-Lin Dept. CSIE National Taiwan University of Science and Technology Taiwan Universidad Politécnica Taiwán-Paraguay Paraguay

ISBN: (纸本)9798331542399

The integration of fog computing with cloud service reduces transmission times to ensure that Internet of Things (IoT) devices satisfy deadline-aware tasks. Existing researches focus on the effective reduction of task transmission delays in hierarchical fog-cloud networks. However, an improper task execution order increases the waiting time and the risk of missing deadlines. To address this problem, the concept of Deadline-Aware Task Offloading and scheduling (DTOS) is introduced. To balance the load of all fog nodes, this research proposes an offloading strategy using a Genetic Algorithm that encodes multiple chromosomes with different offloading decisions for each task. Each evolution round refines the offloading positions, generating a strategy that approaches the optimal solution. Subsequently, the scheduling algorithm finds an effective execution sequence for each fog node based on the generated offloading decision. The simulation results demonstrate that the proposed strategy is more efficient than the baseline algorithms compared in this research. © 2025 IEEE.

关键词： scheduling algorithms

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Intelligent scheduling task adjustment method based on graph neural network and adaptive weights 4

Intelligent scheduling task adjustment method based on graph...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Liu, Lei Yu, Xuexia Hu, Zhiyuan Li, Xiaojie Zhao, Guowen Su, Jun Beijing China-Power Information &. Technology Co. Ltd. State Grid Information & Telcommunication Group Beijing100000 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In order to make the scheduling of tasks to be executed by intelligent agents more flexible and reasonable, a method for adjusting intelligent scheduling tasks based on graph neural networks and adaptive weights is proposed. Based on the short-term memory ability of graph neural networks, search for temporary computing tasks received by intelligent agents in the database as the basic data for subsequent arrangement and adjustment. Extracting thematic content features of intelligent tasks based on recurrent neural networks;Identify priority task targets in the scheduled tasks through graph neural network models. Based on adaptive weights and combined with online learning listwise algorithm, intelligent task scheduling adjustment is achieved. The experimental results show that the conflict rate of task scheduling adjustment using this method remains stable within 5%, and the scheduling adjustment effect is more reasonable;The utilization rate of computing resources is higher after task scheduling adjustment, and task scheduling is more flexible;The longest response time for task scheduling adjustment is 29ms, which is more effective. © 2025 SPIE.

关键词： scheduling algorithms

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Joint Optimization of Intelligent Parking and Automated EV Charging with Advanced Orderly Charging scheduling Algorithm

Joint Optimization of Intelligent Parking and Automated EV C...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Shen, Zhao Wang, Qianqian Lu, Wenchao Peng, Weilun CSG Guangdong Guangzhou Power Supply Bureau Guangzhou China

ISBN: (纸本)9789819639762

Traditional charging stations often suffer from inefficiencies when fully charged vehicles occupy charging infrastructure, reducing resource utilization. In response, we introduce an intelligent, unmanned parking and charging system based on a fully automated robotic arm capable of autonomously managing plug-in and plug-out operations. This enables a single charging unit to serve multiple parking spaces while supporting fully automated charging scheduling. Furthermore, we design an optimized, orderly charging model and algorithm to enhance EV parking slot matching, charging times, frequency, sequence, and power allocation. This approach reduces grid load, increases charging pile utilization, and improves the user experience. Experimental results show that the proposed system achieves a 98.69% charging fulfillment rate compared to 61.6% in conventional systems, along with increased charging profitability. This flexible scheduling and power management solution maximizes charging station efficiency and improves user satisfaction. © Beijing Paike Culture Commu. Co., Ltd. 2025.

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A New Multiple Agricultural Machinery scheduling via a Genetic Algorithm Improved by Large Neighborhood Search Method 5

A New Multiple Agricultural Machinery Scheduling via a Genet...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Feng, Xin Chen, Xingyu Huang, Lvwen Pu, Pan Northwest A&F University Shaanxi Yangling China

ISBN: (纸本)9798331533694

Aiming at the problem that the commonly used scheduling algorithm in the process of intelligent scheduling of agricultural machinery are easy to fall into local optimum, a multiple agricultural machinery scheduling operation method with a genetic algorithm improved by large neighborhood search method is proposed. To begin with, focusing on the shortcomings of high cost and inefficient of agricultural machinery scheduling, an agricultural machinery scheduling model with a time window is constructed, with the optimization goal of least total operating cost. Subsequently, a genetic algorithm improved by large neighborhood search algorithm is designed. Through selection, crossover, mutation and local search, better solutions are continuously sought. By adding adaptability to the crossover and mutation processes and simultaneously improving the large neighborhood search operator, a more efficient and flexible search for the optimal solution is achieved. Finally, comparative experiments with six commonly used algorithms are carried out based on real data sets. The experimental results show that even if the number of farmlands increases, the scheduling cost of the proposed algorithm still maintains a significant advantage. When the number of farmlands is 17, 27 and 37, the total cost is reduced by an average of 171.40, 305.28 and 460.39 CNY respectively. Moreover, the advantages of this algorithm in terms of convergence results and speed are more prominent. © 2025 IEEE.

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