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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Geng, Zengtao Ding, Xiangqian Li, Quanxi Yu, Shusong Ocean University of China Shandong Port Technology Group Qingdao Faculty of Information Science and Engineering Qingdao China Ocean University of China Faculty of Information Science and Engineering Qingdao China Shandong Port Technology Group Qingdao Qingdao China

ISBN: (纸本)9798350375107

To solve the loading and unloading problems of RMG (Rail Mounted Gantry), quay crane and AGV (Automated Guided Vehicle), this paper proposes a hierarchical cooperative scheduling algorithm. Firstly, the operation task is converted into 2 parts: the vertical equipment and the horizontal one, which can be regarded as the passenger and the taxi, i.e., named as the ride-hailing problem. We introduce the Hungarian algorithm to accomplish the matching of the task and the AGV. Secondly, the overall tasks of the equipment are transformed into planning paths, and then each equipment can execute it respectively. Here the global optimization should be considered. Finally, after the planning paths are truncated, we design a novel Markov random field model for the global optimization, aiming at the efficiency and safety of all equipment. In experiments, the effectiveness of our algorithm is verified by the simulation and real tests. The performance of safety and efficiency has strong promotion significance in new terminals and old reconstructed ones. © 2024 IEEE.

关键词： scheduling algorithms

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Cloud Computing Task scheduling based on Improved Particle Swarm Algorithm 24

Cloud Computing Task Scheduling based on Improved Particle S...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Liu, Xiaoyu Yu, Peng Liu, Xiaojie Inspur Cloud Inspur Group China Shandong Shenghan Finance and Trade Vocational College China

ISBN: (纸本)9798400718137

To further improve the efficiency and performance of cloud computing task scheduling and cope with the diversity of user tasks, the study unfolds the construction of cloud computing task scheduling model based on heuristic intelligent optimization algorithm based on artificial intelligence technology. The results show that the improved optimization algorithm designed by the study performs well on the hypervolume and anti-generation distance, obtaining a maximum value of 0.976 and a minimum value of 0.136, respectively, and has a good ability to find the optimal solution. The scheduling model has strong convergence performance, and the completion time is 17.64s, 39.71s, 61.69s and 86.23s when the number of tasks is 60, 120, 180 and 240, respectively, with high scheduling efficiency. Meanwhile, the highest value of resource utilization can reach 95.36%, and the load balancing performance is good. The design of the study can achieve reasonable resource allocation and task scheduling, which helps to improve the overall resource utilization of the data center. © 2024 Copyright held by the owner/author(s).

关键词： scheduling algorithms

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： scheduling algorithms

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An Improved Estimation of Distribution Algorithm for Earth Observation Satellite scheduling Problem

An Improved Estimation of Distribution Algorithm for Earth O...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Ma, Chunchun Xu, Rui Liu, Xiaoze Wu, Chu-Ge School of Aerospace Engineering Beijing Institute of Technology Beijing China School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350384185

Earth observation satellites play a pivotal role in advancing our understanding of the Earth and its environment. By strategically scheduling satellite orbits and observation, they perform a wide range of critical tasks. In this paper, we consider developing efficient solution schemes for the Earth observation satellites scheduling problems. We first formulate the scheduling problems as mixed-integer programming and then we propose an improved estimation of distribution algorithm, which is a swarm intelligence-type optimization algorithm. The proposed algorithm can efficiently compute a near-optimal solution of an scheduling problem. Numerical studies on a class of the Earth observation satellite scheduling problem suggest that the proposed algorithm generally display better performance and more stability than its classical counterpart. © 2024 IEEE.

关键词： scheduling algorithms

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Online scheduling Strategies for Renewable Energy Systems Based on Digital Twin Models 24

Online Scheduling Strategies for Renewable Energy Systems Ba...

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4th International Conference on Artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Zhu, Yongqing Mou, Xuepeng Wang, Wei Liu, Xingyan Hu, Jiang Power Grid Planning and Research Center Guizhou Power Grid Co. Ltd China

ISBN: (纸本)9798400710049

To address the randomness and volatility of renewable energy generation technologies, we propose an online scheduling strategy for renewable energy power systems based on a digital twin (DT) model. By constructing a digital twin edge network (DTEN) model of the power system, the strategy enables real-time monitoring, efficient optimization scheduling, and decision support for renewable energy power systems. Firstly, under the constraints of computational resource limitations and latency requirements, the total energy consumption of the entire network is minimized through optimized offloading decisions and computational resource allocation. Secondly, this non-convex optimization problem is reformulated as a Markov Decision Process (MDP), and optimization algorithms within deep reinforcement learning (DRL) are employed to achieve optimal system decisions. Lastly, an online and real-time capable on-policy DRL algorithm is introduced to solve the offloading strategies and computational resource allocation strategies. Simulation results demonstrate that the proposed algorithm trains rapidly and effectively reduce system energy consumption while ensuring offloading and computational latency requirements are met. © 2024 Copyright held by the owner/author(s).

关键词： scheduling algorithms

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Optimistic Joint Flow Control and Link scheduling with Unknown Utility Functions 24

Optimistic Joint Flow Control and Link Scheduling with Unkno...

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Liu, Xin Wei, Honghao Ying, Lei ShanghaiTech University Shanghai China Washington State University Pullman United States University of Michigan Ann Arbor Ann Arbor United States

ISBN: (纸本)9798400705212

This paper proposes new joint flow control and link scheduling (JFCLS) algorithms for the classical network utility maximization (NUM) problem with unknown utility functions. Our algorithm leverages the idea of optimism, i.e., being optimistic in using the historical information to predict the future impact of flow rate and link scheduling decisions, to reduce the oscillation in the flow rate and link scheduling decision. The optimistic design leads to a gradient-type update for flow rate control. We prove that optimistic JFCLS with the gradient information of utility functions establishes a zero optimal utility gap with O(1/T) convergence rate while guaranteeing a constant queue length at each node for any time slot. When only the values of utility functions are observed, we propose zero-order optimistic JFCLS and prove it establishes a trade-off with utility gap O(1/T1/4 *) and O(T3/4) queue length. Our experiments demonstrate the proposed optimistic algorithm achieves a fast convergence rate and is very adaptive to network dynamics, such as flow dynamics or link failure. © 2024 Copyright held by the owner/author(s).

关键词： scheduling algorithms

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An Improved Heuristic Schedule Approach of Task scheduling for Big Data processing System 9

An Improved Heuristic Schedule Approach of Task Scheduling f...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sharma, Banisha Goel, Paurav Rana, Ishani Chandigarh University Deptt. of Cse Mohali India

ISBN: (纸本)9798350394474

The fact that various jobs have different quality standards is a major problem with scheduling algorithms. Only a small number of algorithms are created to satisfy these various demands. For multivariate situations, none of the scheduling algorithms in use have been able to discover the best solution. algorithms that take into account the trade-off between various quality standards for job scheduling are scarce. As a result, we suggest a new hybrid work scheduling algorithm in our research study that satisfies several quality standards for big data processing systems. Additionally, we put the suggested algorithms into practise by utilising MATLAB to generate and validate the findings. © 2024 IEEE.

关键词： scheduling algorithms

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Fairness in CPU scheduling: A Probabilistic Algorithm 7

Fairness in CPU Scheduling: A Probabilistic Algorithm

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Prasanna, Sachin Singh Gulati, Abhayjit Anagha, H.C. National Institute of Technology Karnataka Information Technology Karnataka Surathkal India

ISBN: (纸本)9798350372816

This paper introduces a novel CPU scheduling algorithm for uniprocessor systems that employs a probabilistic function to enhance fair resource allocation. Unlike traditional algorithms, our approach specifically tackles the challenge of equitable resource distribution by integrating a probabilistic methodology whilst also keeping the priority of each process in mind. We detail the implementation and evaluate its performance against established algorithms, assessing metrics such as average turnaround time, average waiting time and the gini index. All the related code, data used for testing and a working webpage to try out the algorithm first hand can be found at GitHub. © 2024 IEEE.

关键词： scheduling algorithms

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Multigranularity Interleaved Reconfigurable Edge Data Center Network Architecture for Accelerated GAI Jobs

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 13222-13232页

作者： Teng, Yun Yang, Hui Yao, Qiuyan Cheng, Wenlong Hao, Miao Zhang, Jie Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

The network has become a bottleneck for generative artificial intelligence (GAI) jobs. Accelerating GAI jobs in edge data centers using hybrid electrical/optical switch is considered a promising solution. This architecture optimizes bandwidth utilization by enabling demand-aware topology reconfiguration through flexible configuration of optical circuit switche optical circuit switches (OCS). However, frequent topology reconfiguration may increase latency. Therefore, there is a balanced relationship between latency and bandwidth utilization. In this article, we propose a multigranularity adaptive interleaved algorithm for service scheduling in edge data centers. First, different degrees of time slot shifts are introduced based on the latency sensitivity of jobs, where large bandwidth GAI jobs are transmitted in a single hop by configuring a demand-aware topology. Additionally, when the reconfiguration threshold is met, low-priority ports are prioritized for reconfiguration to ensure latency requirements are met. This approach effectively resolves the tradeoff between bandwidth utilization and latency by decoupling them from each other. Simulation results show that this approach can effectively reduce the latency and improve the network throughput.

关键词： Optical switches Data centers Bandwidth Computer architecture scheduling algorithms Topology Cloud computing Switching circuits Low latency communication Optical fiber networks Demand-aware topology reconfiguration edge data centers hybrid electrical/optical switch multigranularity adaptive interleaving algorithm

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Design of a Gain scheduling Algorithm for a Three Conical Tank Interacting System 10

Design of a Gain Scheduling Algorithm for a Three Conical Ta...

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10th International Conference on Advances in Machinery, Material Science and Engineering Application, MMSE 2024

作者： Venkataraman, Aravind Pitchai Veerapathran, Veeramani Nokerov, Suleyman Malikmyradovich Venkataraman, Subramanian Pitchai Suleiman, Faiza Bait Ali Mattu University Mettu Oromia Ethiopia University of Technology and Applied Science Salalah Oman Oguz Han Engineering and Technology University of Turkmenistan 'Oguz Han' Scientific and Technology Center Ashgabat Turkmenistan M.A.M School of Engineering Tamil Nadu Thiruchirappalli India

ISBN: (纸本)9781643685465

Controlling fluid levels in cone-shaped discharge tanks can be challenging, especially in the chemical industry. This paper presents a novel approach for achieving precise level control in such tanks using a proportional-integral (PI) control algorithm. Our method relies on a mathematical model of three interacting conical tanks arranged in a single-input, single-output (SISO) system. By implementing this algorithm, operators can effortlessly maintain the desired fluid level in all three tanks, as specified by the set point. This translates to direct and practical application of the derived controller parameters in real-time process control. The key to our approach lies in a gain scheduling algorithm, developed by analyzing the mass balance equation in the form of differential equations. We provide a detailed step-by-step derivation to guide researchers in their own controller design endeavors. The obtained PI controller values are tested in MATLAB and results are presented. © 2024 The Authors.

关键词： scheduling algorithms

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