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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Chandramowleeswaran, G. Sindhu, M. Alabdeli, Haider Mishra, Anshu Vinod, Priya Kaur, Harmeet Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai600062 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Christ University School Of Business And Management India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India

ISBN: (纸本)9798350390759

This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. © 2024 IEEE.

关键词： scheduling algorithms

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Fine Time Granularity Allocation Optimization of Multiple Networks Industrial Chains in Task Processing Systems 24th

Fine Time Granularity Allocation Optimization of Multiple Ne...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Li, Pan Di, Kai Bai, Xinlei Jiang, Yuanshuang Chen, Fulin School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211189 China School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China

ISBN: (纸本)9789819982103

As the industrial division of labor becomes increasingly specialized, various collaborative relationships between industrial chains develop, forming complex multi-networks. In the task processing system of multiple network industrial chains, there are dynamic online tasks. The arrival and deadline of these tasks cannot be accurately predicted. Therefore, it is necessary to divide the scheduling into finer time granularity to improve the response speed, efficiency, and timeliness, while ensuring the task completion rate and minimizing the task cost. In this paper, we study the characteristics of online tasks in multiple networks industrial chains and design a corresponding online scheduling framework. We analyze the arrival of online tasks in real-world scenarios and propose a passive scheduling algorithm based on the characteristics of different scenarios. The algorithm is tested on several sets of simulated data. Compared with previous heuristic algorithms, our algorithm can achieve better results in terms of task completion time, energy cost, and task completion rate in scenarios with fine time granularity. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： scheduling algorithms

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Deep Learning-Based Resource Allocation algorithms for 6G Networks

Deep Learning-Based Resource Allocation Algorithms for 6G Ne...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Raja, R. Arshath Sharma, Vikrant Yuvaraj, N. Shukla, Rishi Prakash Kumar, Divya Manwal, Manika B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Electronics and Communication Engineering Chennai India Graphic Era Hill University Computer Science and Engineering Dehradun India Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Ait Management Chandigarh University Punjab Mohali India Chandigarh University Mohali India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350309140

Deep learning-primarily based aid allocation algorithms for 6G networks enable enhanced community overall performance via close to most useful schedulers. those algorithms leverage deep neural network architectures to analyze the physical layer aid allocation undertaking which solves the network utility maximization (NUM) problem. The neural community is educated on the use of various reinforcement learning (RL) techniques, which include Q-learning, to perform correct selections as a scheduler in a given deadline. The community structure consists of two fundamental architectures namely the actor-critic structure and the multi-enter-multi-output (MIMO) structure. The actor-critic architecture makes use of a coverage gradient-primarily based technique and a deep Q-community (DQN) to optimize the utility functions. The MIMO structure uses superior deep-gaining knowledge of techniques, along with convolutional neural networks and lengthy short-time period memory (LSTM) recurrent neural networks for stepped-forward selection scoring and quicker convergence. The deep mastering-based algorithms are shown to enhance the scheduling accuracy and convergence time in evaluation with the present scheduling algorithms in 6G networks and bring a promising performance improvement as compared to the conventional scheduling algorithms. © 2024 IEEE.

关键词： scheduling algorithms

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FEDHYPER: A UNIVERSAL AND ROBUST LEARNING RATE SCHEDULER FOR FEDERATED LEARNING WITH HYPERGRADIENT DESCENT 12

FEDHYPER: A UNIVERSAL AND ROBUST LEARNING RATE SCHEDULER FOR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Ziyao Wang, Jianyu Li, Ang University of Maryland College Park United States Apple United States

The theoretical landscape of federated learning (FL) undergoes rapid evolution, but its practical application encounters a series of intricate challenges, and hyperparameter optimization is one of these critical challenges. Amongst the diverse adjustments in hyperparameters, the adaptation of the learning rate emerges as a crucial component, holding the promise of significantly enhancing the efficacy of FL systems. In response to this critical need, this paper presents FEDHYPER, a novel hypergradient-based learning rate scheduling algorithm for FL. FEDHYPER serves as a universal learning rate scheduler that can adapt both global and local learning rates as the training progresses. In addition, FEDHYPER not only showcases unparalleled robustness to a spectrum of initial learning rate configurations but also significantly alleviates the necessity for laborious empirical learning rate adjustments. We provide a comprehensive theoretical analysis of FEDHYPER's convergence rate and conduct extensive experiments on vision and language benchmark datasets. The results demonstrate that FEDHYPER consistently converges 1.1-3× faster than FEDAVG and the competing baselines while achieving superior final accuracy. Moreover, FEDHYPER catalyzes a remarkable surge in accuracy, augmenting it by up to 15% compared to FEDAVG under suboptimal initial learning rate settings. Our source code is released at https://***/ATP-1010/FedHyper. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： scheduling algorithms

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Implications of Non-Uniform Deadline Scaling to Quality of Service Under Single Errors

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IEEE ACCESS 2022年 10卷 14586-14599页

作者： Schmidt, Robert Garcia-Ortiz, Alberto Univ Bremen Inst Electrodynam & Microelect D-28359 Bremen Germany

Fault-tolerant real-time systems for emerging critical applications like wearable electronic healthcare monitors, consumer-grade unmanned aerial vehicles, or environmental monitoring have to tolerate errors during operation. If they fail, the consequences are dire. But often their budgets for error mitigation capabilities are low, which requires to limit mitigation capabilities to the most critical functions of a system. Still, less critical functions of a system should operate as long as possible, but current state-of-the-art scheduling approaches either ignore them or suffer from low acceptance rates. Our fully static and verification friendly mixed-criticality approach guarantees mentioned systems that the most critical system functions are always available, and maximizes the time where less critical system functions are operational: We prove that our approach is feasible, and extends the system operation time while providing full service by a factor of 1.93 with a probability of 0.92. With 1.56 higher acceptance rates compared to similar state-of-the-art approaches, the integration of functionalities of different criticality in one fault-tolerant system succeeds more and more often, which especially benefits emerging critical applications with limited budgets for error mitigation.

关键词： Task analysis Quality of service Schedules Fault tolerant systems Fault tolerance Systems operation Real-time systems Real-time systems scheduling algorithms system recovery

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Optimization of Agent Patrol Algorithm for Patrolling Set of Points with Different Frequency 24

Optimization of Agent Patrol Algorithm for Patrolling Set of...

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8th International Conference on Computer Science and Application Engineering, CSAE 2024

作者： Chen, Zhifei Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9798400718090

The design of agent patrol system with different task targets needs to develop reasonable patrol algorithms and strategies. This work proposes an efficient algorithm to optimize robotic patrol system based on the pinwheel scheduling algorithm and the greedy algorithm with optimal characteristics. It can perform patrolling tasks according to the checkpoints number and the visit frequency provided by the user. One-dimensional and two-dimensional scheduling algorithms are designed for patrolling checkpoints in linear and planar distributions respectively. To assess the effectiveness of the optimized algorithms, a comparison test with traditional pinwheel algorithm is conducted, which shows that the time complexity of the combined algorithm is reduced from O(n2) to O(log n), significantly improving the patrolling efficiency. In real-world test, the combined algorithm reduces the running time by about 25%, while the peak memory usage is reduced by nearly 17%, proving that the algorithm has better stability and scalability in resource-constrained systems. © 2024 Copyright held by the owner/author(s).

关键词： scheduling algorithms

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Exploration of an Enhanced scheduling Approach with Feasibility Analysis on a Single CPU System 15

Exploration of an Enhanced Scheduling Approach with Feasibil...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Saravanan, Vijayalakshmi Wan, Gang Pillai, Anju S University of South Dakota Department of Computer Science VermillionSD United States Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781665464994

Developing a new scheduling algorithm and conducting the performance analysis to recognize its effect in practice can be a laborious task. CPU scheduling is crucial in achieving the operating system's (OS) design goals. There exists a variety of scheduling algorithms in the field and in this paper, a performance comparison of different existing scheduling algorithms by simulating the same bundle of tasks is carried out. A variety of algorithms under batch OS and time-sharing OS are considered. Upon the analysis, a novel task scheduling algorithm incorporating the merits of existing algorithms is proposed for a single CPU system. The performance of various algorithms is compared with the proposed algorithm for parameters viz., throughput, CPU utilization, average turnaround time, waiting time, and response time. Extensive simulation analysis for the various bundle of tasks is conducted and the proposed algorithm is found to outperform the other algorithms in terms of guaranteed reduced average response time. Thus, an efficient CPU scheduler is proposed to accommodate varying workloads at run-time making the best use of the CPU in a particular execution scenario. © 2022 IEEE.

关键词： scheduling algorithms

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A High Concurrency and Low-Delay Data scheduling Method for Massive Power Devices

A High Concurrency and Low-Delay Data Scheduling Method for ...

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2022 International Conference on Power System and Energy Technology, ICPSET 2022

作者： Ouyang, Benkai Xian, Zhao Yin, Zhenchao Chen, Yao Chen, Xiangyuan Shaoguan Power Supply Bureau Guangdong Power Grid Co. Ltd Guangdong China Guangdong Electric Power Communication Technology Co. Ltd Guangdong China

With the massive processing devices connected to the power system, the demand for data processing increases gradually. However, in the high concurrency scenario, the simultaneous processing of massive data will lead to the depletion of computing resources of the power system and the suspension of power services. To address this problem, this paper first proposes the classification method of power system data based on a neural network. Secondly, this paper proposes the data priority model based on the entropy method to determine the processing order of massive data of power devices. Then, a problem of high concurrency and low-delay data scheduling is constructed. To address this problem, we proposed an intelligent data scheduling algorithm based on upper confidence bound (UCB). Finally, the feasibility and effectiveness of the method are proved by simulation. © Published under licence by IOP Publishing Ltd.

关键词： scheduling algorithms

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Research on UAV scheduling Optimization in the Forest Fire

Research on UAV Scheduling Optimization in the Forest Fire

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作者： Yu, Qihang He, Huan Li, Mingyang Hou, Dayou Zhang, Jie Wang, Xianchao Fuyang Normal University Anhui Fuyang236037 China

Wildfire is a kind of fire that occurs in the natural ecosystem, which will affect a series of processes such as ecosystem succession, carbon cycle and natural climate change. In recent years, wildfires continue to occur around the world, and the losses caused by wildfires are increasing. In this paper, we will take New South Wales and East Victoria in Australia as the research object, establish the corresponding model, and give the best UAV combination method to solve the wildfire extinguishing action. We use the UAV scheduling algorithm to establish a mathematical model about UAV combination, and then consider the threat factors such as mountain terrain synthetically, and further simplify the model. Also, we use data visualization to show the relevant information more concretely. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： scheduling algorithms

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Minimum Delay Optimization for Message scheduling in In-Vehicle Applications Based on Pheromone Resetting Strategy 19

Minimum Delay Optimization for Message Scheduling in In-Vehi...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jiang, Junqiang Xie, Lunxin Zhou, Duqun Fan, Bo Hunan Institute of Science and Technology Yueyang China

ISBN: (纸本)9798350346558

In-vehicle applications generally have latency requirements;serious safety accidents will likely be caused due to the applications failing to take the correct actions within a specified time frame. In this study, a Pheromone Resetting Ant Colony Optimization (PRACO) algorithm is proposed to address the calculation of the minimum response time of an application whose messages are transmitted by using the Controller Area Network with flexible data rates (CAN FD) bus. A Random Popup (RP) algorithm is equipped in PRACO to quickly obtain the valid message sequence, followed by resetting the pheromones on all paths if ants find a new optimal valid message sequence path. The minimum response delay of an in-vehicle application can be further got through a continuous iterative search and pheromone update. A Directed Acyclic Graph (DAG) workflow scheduling example and an Adaptive Cruise Control (ACC) application are used to conduct the simulation experiment. The results show that our PRACO algorithm significantly outperforms other static scheduling algorithms in obtaining the lowest response latency. © 2022 IEEE.

关键词： scheduling algorithms

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