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43rd Chinese Control Conference, CCC 2024

作者： Chen, Shengming Wang, Wending Zhou, Suolan Zhang, Lei College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Technology Co. Ltd Changzhou China

ISBN: (纸本)9789887581581

Time Sensitive Networks (TSN) is a set of revisions to the IEEE 802.1 standard enabling safety-critical and real-time transmission over Ethernet. It is crucial in TSN to use a reasonable scheduling algorithm to guarantee the deterministic delivery of streams. A heuristic algorithm is proposed in this paper for incremental time triggered traffic scheduling in TSN. It identifies available time slots with pruning operations. If the available time slot is insufficient to generate a scheduling plan, fallback operations are executed to extend the time slots. Experimental results show that the proposed algorithm can effectively improve the success rate of traffic scheduling in the application scenarios with different network sizes and stream scales. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： scheduling algorithms

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An Intelligent approach for the short-term generation scheduling of a hydrothermal power system employing the Osprey Optimization Algorithm

An Intelligent approach for the short-term generation schedu...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Pasupulati, Baburao Sushmi, Mavuluru Sudhakiran, P. Amosedinakaran, S. Sreedhar, R. Vinoth John Prakash, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electrical and Electronics Engineering Chennai India Srinivasa Ramanujan Institute of Technology Department of Electrical and Electronics Engineering Andhra Pradesh India New Horizon College of Engineering Department of Electronics and Communication Engineering Bengluru India

ISBN: (纸本)9798350381887

In an electric power system, the main goal of Hydrothermal Generation scheduling (HTGS) is to effectively manage hydrothermal unit power generation while abiding by system restrictions. The scheduling timeframe can range from a single day to multiple days. HTGS poses a challenging optimization problem due to its non-linear, non-convex, and non-smooth nature, making traditional techniques less effective. To address this complexity, a novel Osprey Optimization Algorithm (OAA) is introduced in this study. The algorithm is structured around three key stages: Initialization, Position Identification, and Fish Hunting and Transport to Optimal Locations. Notably, the OAA algorithm stands out for its unique feature of not requiring algorithmic tuning parameters, thereby expanding the search space for achieving optimal solutions. The effectiveness of the proposed OAA algorithm is demonstrated through its application to a test system comprising four hydro and three thermal units, with simulation results compared against recent experimental findings in the literature. © 2024 IEEE.

关键词： scheduling algorithms

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An Empirical Study on Various Workflow scheduling Techniques in Cloud Computing 1

An Empirical Study on Various Workflow Scheduling Techniques...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Madhumala, G. Suma, R. Sri Siddhartha Institute of Technology Department of Information Science and Engineering Karnataka Tumkur72103 India

ISBN: (纸本)9798350367720

Cloud computing (CC) is a modern technology which deploys networks of servers, positioned in extensive remote areas, for conducting operations on a larger quantity of data. In CC, a workflow module is employed for representing diverse scientific and web applications. One of the major problems is scheduling larger workflows of tasks on the heterogeneous CC environment. This article summarizes some important research works in the field of CC associated with workflow scheduling (WS). At first, this survey represents the common structure of WS in cloud and then, scheduling approaches are catalogued into four techniques like Machine Learning (ML), Deep Learning (DL), Hybrid and Algorithmic-based approaches are discussed in detail. Thereafter, the research gaps faced by the traditional approaches for WS will help the researchers in future for building an innovative model. This study considers 25 research articles focused on different methodologies employed for WS in cloud. Lastly, the evaluation is performed in the survey regarding year of publication, performance measures, research technique, and attainment of the research models. It is acknowledged that algorithmic based modules are usually employed approaches, while cloudsim is repeatedly applied tool for WS in CC. Moreover, makespan is the utilized analytic measure with superior value. © 2024 IEEE.

关键词： scheduling algorithms

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An Improved Linux Priority scheduling Method Based on XGBoost 20

An Improved Linux Priority Scheduling Method Based on XGBoos...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Yang, Ningke Shu, Xinfeng Liang, Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798350356328

In order to address the issue of excessive resource consumption due to frequent context switching when running a large number of compute-intensive tasks in high-load scenarios on Linux. The paper proposes a scheduling algorithm that utilizes eBPF programs to extract fine-grained data such as process memory usage, execution time, read/write rates, etc. This data is then used to construct a dataset, and an XGBoost algorithm model is employed to predict the future resource consumption of processes. Simultaneously, based on the characteristics of binary classification and multi-classification tasks in the XGBoost algorithm, tasks are assigned different scheduling priorities according to CPU utilization rates. Tasks of corresponding types are then executed based on CPU status before the priority scheduling algorithm is *** comparisons reveal that under similar conditions, reducing the overhead of context switching results in a 1.03% improvement in overall task execution time and enhances CPU running efficiency. Consequently, it is concluded that this improved Linux priority scheduling method based on XGBoost can enhance execution efficiency when running numerous compute-intensive tasks in high-load environments. © 2024 IEEE.

关键词： scheduling algorithms

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Application of Artificial Intelligence in Optimal scheduling of Light Storage and Charging Microgrid Parks

Application of Artificial Intelligence in Optimal Scheduling...

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2024 International Conference on New Power System and Power Electronics, NPSPE 2024

作者： Hu, Weifeng Gu, Yunchang Zhang, Yuchen Yao, Jianqi State Grid Zhejiang Electric Power Co. Ltd Cixi Power Supply Company Ningbo China

ISBN: (纸本)9798350389869

This study explores the application of artificial intelligence technology in the optimization and scheduling of photovoltaic storage and charging microgrid parks. By analyzing the characteristics and requirements of microgrids and combining intelligent algorithms, a scheduling strategy based on Deep Q-Network (DQN) algorithm is proposed. During the experimental phase, a detailed evaluation was conducted through a series of experiments. In the comparative experiment of system operating costs, the total 30 day operating cost based on DQN algorithm was ¥ 15000, significantly lower than that of genetic algorithm (GA) and linear programming (LP). Secondly, in the experiment of renewable energy utilization rate, the average utilization rate of DQN algorithm reached 98.5%. In the supply-demand balance experiment, the average balance index of DQN algorithm was 0.968. Finally, in the convergence speed experiment of the DQN algorithm, the average number of convergence iterations was only 50. From experimental data, DQN algorithm has significant advantages in optimizing scheduling, which can more efficiently reduce costs, improve renewable energy utilization, enhance supply-demand matching, and have a faster convergence speed. © 2024 IEEE.

关键词： scheduling algorithms

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Research on Intelligent Algorithm Design and Performance Evaluation Model for New Energy scheduling

Research on Intelligent Algorithm Design and Performance Eva...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Yang, Yueping Tan, Zhi Fei, Wei Chen, Yu Zhou, Ningyi Liu, Huajun Ningbo Power Supply Company Zhejiang Electric Power Co. Ltd. Ningbo China Smart Energy Co. Ltd Ningbo China

ISBN: (纸本)9798331527624

In this paper, a new energy scheduling system based on improved grey model algorithm is proposed, and its performance is evaluated comprehensively. Firstly, aiming at the uncertainty and volatility problems in new energy scheduling, this paper designs an improved grey model algorithm that integrates historical data and real-time information to improve prediction accuracy and scheduling efficiency. Then this paper introduces the application process of this algorithm in new energy scheduling system in detail, and verifies its effectiveness through model simulation experiment. The experimental results show that the proposed algorithm has significant advantages in improving energy efficiency, reducing operation cost, and enhancing system stability. In addition, people also set up a complete performance evaluation system, and carried out quantitative analysis and evaluation of each index of the algorithm. This study not only provides a new theoretical support and technical means for the field of new energy dispatching, but also provides a useful reference for the future development of related fields. © 2024 IEEE.

关键词： scheduling algorithms

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Resilient scheduling of Moldable Parallel Jobs to Cope With Silent Errors

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IEEE TRANSACTIONS ON COMPUTERS 2022年第7期71卷 1696-1710页

作者： Benoit, Anne Le Fevre, Valentin Perotin, Lucas Raghavan, Padma Robert, Yves Sun, Hongyang Ecole Normale Super Lyon LIP Lab F-69342 Lyon France Barcelona Supercomp Ctr Barcelona 08034 Spain Vanderbilt Univ Nashville TN 37235 USA Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA

We study the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time or the makespan, when jobs can fail due to silent errors and hence may need to be re-executed after each failure until successful completion. Our work generalizes the classical scheduling framework for failure-free jobs. To cope with silent errors, we introduce two resilient scheduling algorithms, Lpa-List and Batch-List, both of which use the List strategy to schedule the jobs. Without knowing a priori how many times each job will fail, Lpa-List relies on a local strategy to allocate processors to the jobs, while Batch-List schedules the jobs in batches and allows only a restricted number of failures per job in each batch. We prove approximation ratios for the two algorithms under several prominent speedup models (e.g., roofline, communication, Amdahl, power, monotonic, and a mix model). An extensive set of simulations is conducted to evaluate different variants of the two algorithms, and the results show that they consistently outperform some baseline heuristics. Overall, our best algorithm is within a factor of 1.6 of a lower bound on average over the entire set of experiments, and within a factor of 4.2 in the worst case.

关键词： Approximation algorithms Resource management Schedules scheduling algorithms Computational modeling Biological system modeling Heuristic algorithms Resilient scheduling parallel jobs moldable jobs speedup model failure scenario transient errors silent errors list schedule batch schedule approximation ratios

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scheduling algorithm for fidelity of quantum gate circuits 3

Scheduling algorithm for fidelity of quantum gate circuits

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3rd International Conference on Electronic Information Engineering and Data Processing, EIEDP 2024

作者： Liu, Xudong Xu, Jinchen Lian, Hang Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9781510680531

As quantum computing technology matures, the availability and performance of quantum devices are steadily improving. However, in the NISQ (Noisy Intermediate-Scale Quantum) era, the quantum bit error rate caused by quantum noise remains a significant constraint on computational accuracy and reliability. In quantum operating systems, quantum task scheduling is a core technology crucial for fully leveraging the performance potential of quantum processors. Currently, most multi-processor scheduling algorithms focus on task load balancing and do not directly consider the characteristics of quantum circuits and the differentiated error rates of quantum computing backends. In quantum computing, the impact of noise errors in single-qubit gates, two-qubit gates, and measurement gates on the fidelity of quantum gate circuits is crucial. Therefore, intelligently matching quantum tasks with the characteristics of backend processors to optimize computational accuracy is an urgent problem to be addressed. This paper proposes a new quantum task scheduling algorithm, which matches tasks with backends based on the weight of single-qubit gates, two-qubit gates, and measurement gates in quantum circuits, as well as the error rate characteristics of each quantum computing backend. Experimental results show that the proposed scheduling algorithm for improving the fidelity of quantum gate circuits outperforms static load balancing scheduling algorithms, resulting in a 4.39% increase in the fidelity of quantum tasks. © 2024 SPIE.

关键词： scheduling algorithms

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An Efficient Multipriority Data Packet Traffic scheduling Approach for Fog of Things

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IEEE INTERNET OF THINGS JOURNAL 2022年第1期9卷 525-534页

作者： Serdaroglu, Kemal Cagri Baydere, Sebnem Yeditepe Univ Dept Comp Engn TR-34755 Istanbul Turkey

With the proliferation of heterogeneous services in scalable Internet-of-Things (IoT) architectures, the Cloud of Things begins to face latency-based network challenges. As a solution to these problems, the Fog-of-Things (FoT) paradigm has emerged. This paradigm promises numerous benefits for latency-sensitive IoT service design, by reducing IoT data traffic toward the cloud. Since FoT utilizes heterogeneous services, the decision outputs of these services are transmitted by multipriority data packet traffic patterns. For the full utilization of fog computing benefits, we need to model this traffic and apply an efficient data traffic scheduling approach. In this study, we address the starvation challenge of multipriority FoT data traffic scheduling in a scalable fog architecture and propose a complex event processing-based efficient scheduling policy for its prevention. We first give the arrival-service model for FoT data traffic using finite-size multilevel waiting lines. Then, we compare the proposed policy with the first-in-first-out and multipriority-discipline queue policies through a comprehensive analysis of waiting times and wait-time gap characteristics. We have also conducted extensive simulation tests to explore the performance of these policies in our testbed for up to 800 clients communicating with the fog system. The results reveal that the empirical values obtained from the tests verify the theoretical model and the proposed approach can successfully relieve the waiting-time gaps observed in priority levels.

关键词： Internet of Things Queueing analysis Data models scheduling Cloud computing scheduling algorithms Computational modeling Cloud data traffic scheduling fog Internet of Things (IoT) queuing theory

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Design of a Demonstration System for Vehicular Edge Computing scheduling 14

Design of a Demonstration System for Vehicular Edge Computin...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Sun, Ye Wu, Shuangnan Shen, Li Yan, Hao Sheng, Feng Vocational University Hunan Automotive Engineering Zhuzhou China

ISBN: (纸本)9798350356670

With the widespread adoption of intelligent in-vehicle devices, demands for server computing performance and network transmission speed have significantly increased, while resources in vehicle-edge computing remain limited. This paper presents a demonstration and learning platform for vehicular edge computing scheduling, employing Web3D technology to create a three-tier model of the architecture, vividly illustrating the resource allocation process. Addressing the dynamic complexity of in-vehicle network environments, this study abandons conventional scheduling methods, opting instead for deep learning algorithms to determine data offloading strategies that can adapt to changing conditions. Furthermore, the design incorporates a virtual academy where users can learn about edge computing, deep learning, and related subjects to gain a comprehensive understanding of the demo system. © 2024 IEEE.

关键词： scheduling algorithms

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