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An Efficient Release Time Assignment Method for Backup Tasks in Real-Time Standby-Sparing Driverless Vehicle Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 6143-6157页

作者： Liu, Yumeng Cai, Si Zhou, Quan Xu, Fanjiang Wang, Hongan Univ Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Informat Ctr POWERCHINA Hubei Elect Engn Co Ltd Wuhan 430040 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

Reliability is the primary goal of driverless vehicle design. Redundant system design is the key to ensure the safety and reliability of driverless vehicles, so some driverless vehicles adopt standby-sparing system. The standby-sparing system is a kind of computer systems that solves the fault tolerance problem by using two processors: a primary processor and a spare processor. The primary processor is responsible for executing main tasks. The spare processor is responsible for executing backup tasks. The release time assignment for backup tasks, as a part of task scheduling, is an important technology in real-time standby-sparing systems. The selective scheduling for tasks with (m, k)-constraint (MKSS) is the state-of-the-art scheduling method for real-time tasks in standby-sparing systems. However, this method is inefficient in release time assignment for backup tasks. In this work, we propose an efficient release time assignment method that can reduce the number of inspecting points and terminate the enumeration once there appears a special inspect point. Theoretical analysis results show that the new method can improve the execution efficiency of the release time assignment without sacrificing the energy-saving effect of MKSS. A set of experiments is conducted to evaluate the execution efficiency of the new method, and the results show that, compared with the MKSS method, the new method can reduce about 98.7% time overhead for assigning release time for tasks.

关键词： Task analysis Real-time systems Program processors Optimal scheduling Energy consumption scheduling algorithms Resource management Driverless vehicle standby-sparing (m, k)-constraint fault tolerance release time assignment

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Improved Online scheduling with Restarts on a Single Machine 22nd

Improved Online Scheduling with Restarts on a Single Mach...

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22nd International Workshop on Approximation and Online algorithms, WAOA 2024

作者： Amouzandeh, Aflatoun van Stee, Rob University of Siegen Siegen Germany

ISBN: (纸本)9783031813955

We consider the problem of minimizing the total completion time on a single online machine using restarts. Although restarts can potentially be very beneficial in the context of online scheduling, there has been relatively little research on this topic up until now. We present a very simple online algorithm which is better than 1.4568-competitive. The basic rule of the algorithm is to run jobs in order of increasing size. For possible restarts, the algorithm only considers whether the completion time of an incoming job is more than a factor of 1.4568 higher if we first let the running job finish compared to the case where we start the new job immediately. If this is the case, we interrupt the running job and start running the new job. All other existing or past jobs are ignored for this decision. The analysis of the algorithm has become significantly easier and shorter than for the previous best result which was 3/2. We hope that this result can lead to further research in this interesting topic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： scheduling algorithms

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The Reinforcement Learning—Remora Optimization Algorithm of Single-Objective Flexible Job Shop scheduling 14th

The Reinforcement Learning—Remora Optimization Algorithm of...

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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Liu, Yaoqi Li, Guiqin Lu, Lixin Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China

ISBN: (纸本)9789819626243

A single-objective Flexible Job-Shop scheduling Problem (FJSP) solving method based on the Reinforcement Learning-Remora Optimization Algorithm (RL-ROA) is proposed in this paper. The method integrates the characteristics of multi-variety small-batch production in enterprises and establishes a scheduling model of Multi-Variety Small-Batch Flexible Job-Shop scheduling Problem (MVSB-FJSP) for minimizing the maximum completion time. Based on the reinforcement learning (RL) module, intelligent adjustments of the key parameters in the Remora Optimization Algorithm (ROA) are achieved, the algorithm’s computational efficiency and solution quality are enhanced. The effectiveness of the proposed method is validated through the Brandimarte standard examples, and comparisons are conducted with other algorithms. The results demonstrate that the RL-ROA exhibits higher reliability and superiority in solving the MVSB-FJSP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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Optimizing Production Component scheduling in Multivariate Industrial Networks with Dynamic Changes in Production Costs 25th

Optimizing Production Component Scheduling in Multivariate I...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Xing, Xiangxiang Chen, Fulin Zuo, Tianyu Li, Pan Di, Kai Wang, Xin Chen, Lifeng Jiang, Yichuan Chen, Dan School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211189 China School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China No.8511 Research Institute of CASIC Jiangsu Nanjing210007 China Sanjiang Aerospace Xianfeng Electronic Information Co. Ltd. Wuhan Xiaogan China College of Electronics and Information Engineering Beibu Gulf University Guangxi Qinzhou535000 China

ISBN: (纸本)9789819642069

With the development of industrial intelligence and information technology, the industrial network presents a new characteristic of multivariate network. In this paper, a production element scheduling optimisation algorithm for dynamic change of production cost in multivariate industrial network is designed in order to reduce the scheduling element falling into local optimum. First of all, the multi -time granularity prediction method based on stability selection predicts the production cost of components in multivariate industrial networks. Subsequently, multi -dimensional training is based on multi-dimensional training to learn scheduling optimization algorithm to solve the scheduling optimization of components. Finally, the experiment shows that the algorithm has better performance for optimization of task scheduling in multivariate industrial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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An Adaptive Time Quantum-Based Efficient Round-Robin Algorithm for CPU scheduling 5th

An Adaptive Time Quantum-Based Efficient Round-Robin Algorit...

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

作者： Arthur, Menaka Pushpa Kareem, Rizwan Mohamed Yogeshwar, G. Vellore Institute of Technology Chennai Campus Chennai600127 India

ISBN: (纸本)9789819745395

The efficiency of the Round-Robin (RR) algorithm for CPU scheduling depends on its Time Quantum (TQ) value. The optimal time quantum value can reduce the average waiting time and average turnover time of the processes in the ready queue. Finding an adaptive time quantum for the processes in the ready queue is a challenging task to get an efficient performance from the round-robin algorithm. Too lengthy or too short of time quantum induces starvation or computation overhead in the process execution. TQ value is adaptive to various features of waiting processes in the ready queue such as the number of processes in the ready queue, average burst time, arrival time, and priority of the waiting processes. Hence, we have modified the round-robin algorithm by considering its performance-influencing factors to set the optimal time quantum value. Also, this proposed RR sets a dynamic time quantum for every iteration that aligns with the changing nature of the ready status properties. The performance improvement of the proposed round-robin algorithm is analyzed using implementation results. With the support of performance metrics such as average waiting time (AWT) and average turnaround time (ATT), the proposed modified round-robin algorithm is compared with other CPU scheduling algorithms and existing modified RR. The comparison results show the better performance of the proposed RR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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A Whittle Index Policy for Dynamic Task scheduling of Multi-Platform Multi-Sensor Systems 2

A Whittle Index Policy for Dynamic Task Scheduling of Multi-...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Min Wang, Zengfu Wang, Xiaoxu Yang, Bige Northwestern Polytechnical University School of Automation Xi'an China

ISBN: (纸本)9798331515669

Task scheduling is key to multi-platform multi-sensor systems, which faces challenges of complex sensor combination and high computational complexity. To deal with these challenges, this research introduces the concept of a sensor resource pool to characterize available sensors, and formulate the optimization problem as a restless multi-armed bandit (RMAB) process, considering both task execution and the limitations of the sensor resource pool. To tackle the formulated RMAB problem, we apply Whittle relaxation. Accordingly, the Whittle index policy is introduced for the dynamic task scheduling of multi-platform multi-sensor systems. Numerical simulations demonstrate the effectiveness of the proposed policy. © 2024 IEEE.

关键词： scheduling algorithms

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Heterogeneous Workflow scheduling Algorithm 10

Heterogeneous Workflow Scheduling Algorithm

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Wu, Hsuan-Zong Lai, Kuan-Chou National Taichung University of Education Taichung Roc Dept. of Computer Science No.140 Minsheng Rd. West Dist. Taichung City ROC Taichung Taiwan

ISBN: (纸本)9798350394924

As cloud-native applications is more and more popular, job scheduling becomes an important issue. In a cloud-native environment, different jobs have different resource requirements and execution times, causing the efficiency of job scheduling to affect system performance. Therefore, job scheduling algorithms have to face several issues: 1. resource contention resulting from insufficient resource allocation to task groups in decentralized approaches, such as Apache Spark. 2. heterogeneous resource requirements of different jobs running in one computing node. 3. Communication overhead arising from parallel job execution. Therefore, this work proposes a workflow scheduling approach in cloud-native environments. The proposed approach is named as the Heterogeneous Workflow scheduling Algorithm (HWS), which is a novel solution designed to tackle the complexities associated with task scheduling in cloud-native big data environments. HWS addresses the above issues to avoid the resource contention in group dependencies, and reduce the job execution time and the communication overhead. The proposed approach consists of two primary components: the job priority evaluation module and the job assignment module. Experimental results show that HWS outperforms two heuristic-based workflow scheduling algorithms, MPEFT and IPPTS, with improvements of 11.4% and 11.3% over MPEFT and IPPTS, respectively. © 2024 IEEE.

关键词： scheduling algorithms

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A Novel Method of Elderly Service scheduling and Path Planning Based on Clustering Algorithm 11th

A Novel Method of Elderly Service Scheduling and Path Planni...

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11th International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Yu, Bing Yan, Xiangzhu Center of Well-being Training Changchun Humanities and Sciences College Changchun China

Based on relevant research on the scheduling problem of home care appointments and path planning under time window constraints both domestically and internationally, this article considers the current situation of medical resource scarcity in the domestic home care industry and the characteristics of the home care industry that are different from general service industries. It proposes a clustering algorithm to study this problem and uses fuzzy time windows to describe customer needs under different task urgency. The practical results show that the proposed clustering algorithm can effectively analyze and solve the current development status and characteristics of the domestic home-based elderly care industry combined with a path planning scheme. Under the constraint of fuzzy time window, it studies the appointment scheduling problem under the conditions of determining service duration and fuzzy service duration, and finally proposes a solution method for the two types of problems, which has theoretical significance. © 2024 The Authors. Published by Elsevier B.V.

关键词： scheduling algorithms

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scheduling of Flexible Manufacturing System with Penalty Cost Using Simulated Genetic Algorithm 15

Scheduling of Flexible Manufacturing System with Penalty Cos...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Vijai Singh, Varsha Inderprastha Engineering College dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India Indian Institute of Information Technology Allahabad dept. of Information Technology Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350370249

This paper discuss about the scheduling problem for flexible manufacturing systems (FMSs) using the genetic algorithm. In FMSs, the operation of a job is performed on more than one machine. Therefore, FMS scheduling is considered to be a computationally hard problem. In this paper, various approaches are reviewed that are applied to the FMS scheduling problem. This paper also attempts to find a possible solution to the scheduling problem of FMS. To identify the order in which the various jobs would be assigned to a number of machines, this paper uses a genetic algorithm with Simulated Annealing to generate a machine sequence. In this, the developed tool requires tabular input. To obtain a schedule with minimum make span, many iterations of the genetic algorithm are applied. In this system, an optimal schedule is given for FMS by taking the makespan time and minimum penalty cost, and therefore calculating the minimum combined objective function. © 2024 IEEE.

关键词： scheduling algorithms

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TopoOrderPart: a Multi-level scheduling-Driven Partitioning Framework for Processor-Based Emulation 24

TopoOrderPart: a Multi-level Scheduling-Driven Partitioning ...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Bi, Shunyang Tang, Jing You, Hailong Wu, Haonan Li, Cong Sun, Richard Faculty of Integrated Circuit Xidian University Xi’an China S2C Inc. Shenzhen China

ISBN: (纸本)9798400710773

In a compilation flow of processor-based emulation (PBE), partitioning involves dividing a large netlist into smaller pieces and assigning them to different processors. Furthermore, the scheduling process must adhere to the levels of the netlist, which are determined by topological ordering, and the logic gates in the same level can be emulated in parallel. However, during the netlist partitioning stage, assigning most gates at the same level to one processor would undermine the benefits of parallelization in scheduling, leading to overall performance degradation. This paper proposes the TopoOrderPart, the first scheduling-driven partitioning framework for simultaneous balancing topological order and minimizing the cut size, which holds significant value in reducing time steps of scheduling. In particular, the topological order balancing and cut size are considered throughout the multilevel paradigm, and balance-aware coarsening achieves balancing between clusters in the early stage, with super-far root growing initial partitioning obtaining the better partition by selecting those root nodes in distant relationship within the connection space and two novel TopoRefine algorithms further enhancing the solution. Experimental results show TopoOrderPart can improve 69% topological order balancing and 0.53× run time while maintaining comparable cut size, compared to the state-of-the-art partitioner. © 2024 Copyright is held by the owner/author(s).

关键词： scheduling algorithms

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