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检索条件"主题词=Scheduling algorithms"

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Task scheduling algorithms for Energy Optimization Under scheduling Duration and Reliability Constraints

Task Scheduling Algorithms for Energy Optimization Under Sch...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Shizhuang Li Bide Hao Shichun Yang Qianlong Duan Wenlong Chen Fan Zhou School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

High-performance domain controllers' hardware and software are put to the test by the growing array of additional capabilities found in smart, connected cars. In smart connected automobiles, the energy consumption of high-performance domain controllers is contributing to the whole vehicle's energy consumption at an increasing rate, even though hardware systems' computing capacity is also expanding quickly. Thus, it is crucial to research reducing processor energy consumption while maintaining system performance and dependability. This paper investigates the problem of energy-optimized task scheduling in an on-board operating system for SOA-oriented architectures by combining the DVFS technique and the DAG task model under the constraints of scheduling duration and reliability. Firstly, the energy-optimal task scheduling problem for heterogeneous multicore systems is described, focusing on the constraints of scheduling duration and reliability. The shortcomings of traditional task scheduling algorithms are also analyzed. Secondly, a task scheduling algorithm based on the meta-heuristic Whale Optimization Algorithm (WOA) is proposed. This algorithm includes the design of encoding, decoding, and constraint processing schemes, and assigns processing units and corresponding DVFS levels to each task in the DAG task set to achieve energy consumption optimization while satisfying constraints. To address the suboptimal performance of the Whale Optimization Algorithm in high-dimensional problems, a multi-strategy optimization approach is introduced. This enhanced algorithm incorporates chaotic mapping, adaptive nonlinear convergence factors, dynamic inertia coefficients, the Lévy flight strategy, and the evolutionary population dynamics strategy. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments.

关键词： Energy consumption Job shop scheduling scheduling algorithms Heuristic algorithms Operating systems Dynamic scheduling Whale optimization algorithms Nonlinear dynamical systems Reliability Vehicle dynamics

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Performance Evaluation of Q-Learning-Based List scheduling algorithms in Heterogeneous Systems

Performance Evaluation of Q-Learning-Based List Scheduling A...

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International Conference on Computer and Communications (ICCC)

作者： Qiwen Xue Hamza Djigal Maryam Cheraghy Department of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Task scheduling plays a crucial role in Heterogeneous Systems (HS). The makespan (or scheduling length) minimization is the key criterion of the task scheduling algorithm in HS. Recently, the rapid development of machine learning has led to its widespread application in various fields. This paper extends existing heuristic-based task scheduling algorithms using Reinforcement learning techniques. Moreover, we use the Q-learning technique to extend two heuristic scheduling algorithms, the PEFT and IPPTS. We aim to combine these algorithms with Q-learning to improve their task prioritization. We compare the HEFT, QL-HEFT, QL-PEFT, QL-PPTS, and QL-IPPTS using real-world scientific workflows. The experimental results show that QL-HEFT outperforms QL-PEFT, QL-PPTS, and QL-IPPTS by 27.12 percent, 16.75 percent, and 10.7 percent, respectively

关键词： Performance evaluation Q-learning scheduling algorithms Redundancy Neural networks Memory management Minimization scheduling Standards Convergence

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Research on Task scheduling algorithms in Control System Oriented Parallel Testing Techniques

Research on Task Scheduling Algorithms in Control System Ori...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Jingyi Chen Gong Meng Shenhang Wang Dongpeng Li Yang Yu Ziyuan Zhan School of Electronic and Information Engineeer Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China System Design Department Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350388855

ISBN: (纸本)9798350388862

Task scheduling algorithms have consistently been the foundation of parallel testing technology, particularly in the domain of control systems. The complexity of testing tasks in these systems presents a significant challenge to the task scheduling of parallel testing technology. Existing algorithms often fail to fully consider the back-and-forth dependencies between tasks and resource preemption when dealing with parallel testing tasks in control systems. Consequently, the generated scheduling schemes lack efficiency and flexibility. To address this issue, a set of task planning rules has been proposed, based on an analysis of test task information and common conflict patterns. These rules aim to enhance the efficacy of parallel test scheduling in control systems. A multiple swarm genetic algorithm, which incorporates the ant colony mechanism, is thus devised. This algorithm combines the pheromone guidance mechanism of the ant colony algorithm with the crossover and mutation operations of the genetic algorithm. Furthermore, the multiple swarms parallel search strategy is employed in order to achieve the dual-objective optimization of the total test time and the balance of the test resources. The simulation results demonstrate that the designed algorithm is capable of achieving stable convergence while simultaneously enhancing the dual-objective optimization rate and optimization stability.

关键词： Job shop scheduling scheduling algorithms Simulation Control systems Approximation algorithms Stability analysis Optimization Testing Genetic algorithms Convergence

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Comparing various workflow scheduling algorithms based on cost-time tradeoff criteria for clouds

Comparing various workflow scheduling algorithms based on co...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Meenakshi Bist Amit Aggarwal Computer Science & Engineering Veer Madho Singh Bhandari Uttarakhand Technical University Dehradun India Veer Madho Singh Bhandari Uttarakhand Technical University Campus Gopeshwar

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Cloud scheduling of workflow applications have become a great challenge, especially when we are dealing with many QoS (Quality of Service) constraints. scheduling Workflow tasks onto instances of VMs in heterogeneous cloud environments have become challenging. Many cloud scheduling algorithms based on multi-objective QoS constraints. We have compared cloud scheduling algorithms justifying cost-time trade of criteria in our research. Our work emphasizes on both static and dynamic cloud scheduling algorithm for clouds. We have compared these under various parameters and conditions such as research gaps or drawbacks of the proposed algorithm, trade-off function used, task selection strategy used and many more.

关键词： Green energy scheduling algorithms Heuristic algorithms Quality of service Dynamic scheduling

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Analysis of Round-Robin and Random scheduling algorithms on Sensor Node Activation Towards Latency of Wireless Sensor Network

Analysis of Round-Robin and Random Scheduling Algorithms on ...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Ferry Wahyu Wibowo Andi Sunyoto Bayu Setiaji Wihayati Department of Informatics Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

A Wireless Sensor Network (WSN) is a network system consisting of wireless sensor nodes that collect and transmit data. One crucial factor that affects WSN performance is latency, which is the time required to send data to the gateway. High latency can cause data delivery delays, resulting in data becoming irrelevant. At the same time, receiving real-time data quickly is the primary monitoring method. In addition, high latency can cause greater energy consumption because the sensor node device must be active longer. One way to overcome latency is to implement traffic management techniques such as scheduling data delivery or using priority delivery methods, which can reduce congestion and latency. This paper discusses the effect of latency on WSN using scheduling algorithms, namely Round-Robin and Random scheduling. The results were then taken 30 times to obtain a statistical comparison. The results obtained in the experiment received a Round-Robin scheduling algorithm that was superior to the random scheduling algorithm with a mean and standard deviation value of 12.68 and 2.33, respectively.

关键词： Wireless communication Wireless sensor networks scheduling algorithms Logic gates Real-time systems scheduling Delays Standards Monitoring Network systems

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Energy-efficiency Analysis of Different scheduling algorithms for Mobile Crowd Computing

Energy-efficiency Analysis of Different Scheduling Algorithm...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Pijush Kanti Dutta Pramanik Tarun Biswas School of Computer Applications and Technology Galgotias University Greater Noida India Department of Computer Science & Engineering Indian Institute of Information Technology Ranchi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs.

关键词： Performance evaluation scheduling algorithms Energy efficiency scheduling Mobile handsets Computational efficiency Optimization Genetic algorithms

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Analysis of real-time scheduling algorithms under overload conditions in a multi-core ECU

Analysis of real-time scheduling algorithms under overload c...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： SeongHyeon Lim SeJeong Lim YoungSoo Do JaeWook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a vehicle’s software functionality increases, the number of ECUs required also increases. The addition of more ECUs to a vehicle is not a beneficial modification from a multitude of perspectives, including financial, operational, and environmental. One possible method for reducing the number of ECUs is to consolidate them using multi-core processors. In this paper, we apply representative real-time scheduling algorithms to a multi-core processor and analyze the stability of each algorithm when the ECU is overloaded. We also evaluate two methods for distributing tasks from a communication core to processing cores. One is based on odd and even priorities, and the other is a random, priority-independent distribution.

关键词： Industrial electronics Multicore processing scheduling algorithms Real-time systems Stability analysis Software Reliability

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Dynamic particle swarm scheduling algorithms for heterogeneous actuators

Dynamic particle swarm scheduling algorithms for heterogeneo...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Lingyu Yang Ming Ni Xinsheng Yu Jinzhong Wan The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai China Jimei University Xiamen China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

Heterogeneous computing solves the problem of attack and defense asymmetry in cyberspace by establishing a reliable system from the perspective of endogenous security, for the lack of heterogeneous executor quantization in the current scheduling algorithms for active defense systems, this paper proposes a heterogeneous executor set adaptation function based on the probability of loophole attack and the index of heterogeneity measure, and puts forward a dynamic particle swarm scheduling algorithm based on the genetic idea, which utilizes the weights and the compression factor to control the convergence rate, with stable global search ability and optimization ability. The results show that the algorithm has better performance in terms of effectiveness and reliability than previous schemes while ensuring the security advantage.

关键词： Weight measurement scheduling algorithms Heuristic algorithms Dynamic scheduling Particle measurements Security Reliability Indexes Particle swarm optimization Optimization

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Language-Agnostic Compilation scheduling algorithms for Partial AOT Compilations

Language-Agnostic Compilation Scheduling Algorithms for Part...

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Collaborative Advances in Software and COmputiNg (CASCON), International Conference on

作者： Georgiy Krylov Kenneth B. Kent Gerhard W. Dueck Younes Manton Faculty of Computer Science University of New Brunswick Fredericton New Brunswick IBM Runtimes Lab Markham Ontario Canada

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

Adopting Ahead-of-Time (AOT) compilation to improve a programming language's performance shows significant gains in execution time during consecutive program executions. However, such gains come at the cost of spending computing resources on compiling methods. This is especially noticeable when implementing “dynamic-AOT” compilation, where loadable code is generated only for some of the methods and the rest of the program can be interpreted or compiled Just-in-Time. We propose a language-independent algorithm for scheduling partial compilations, an alternative to generating machine code for all the methods in the program at once. By distributing the compilation efforts among several program executions, the algorithm enables reducing the peak program execution duration for compilation runs. The compilation decisions are made to allow loading previously-compiled code, thereby reducing the partially-compiled program execution time. This is achieved by learning the program's partial call graph and guiding the compilation scheduling and loading decisions using this information. This research uses a WebAssembly runtime equipped with an AOT compiler to study the effects of the proposed algorithm. The AOT compiler uses Eclipse OMR-a language runtimes construction toolkit; but the proposed algorithm is independent of the target language and can also be used by other runtime construction toolkits. This paper includes a comparison of the execution times of a program compiled fully Ahead-of-Time and the proposed compilation scheduling algorithms. Using the proposed approach significantly reduces the maximum time taken by the compilation runs in most cases and reaches a peak performance similar to the traditional AOT compilation approach.

关键词： Codes Runtime Program processors Costs scheduling algorithms Loading Static analysis Performance gain Software Steady-state

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Enhancing IoT Workloads: A Comparative Study of Fog and Edge Computing scheduling algorithms

Enhancing IoT Workloads: A Comparative Study of Fog and Edge...

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Circuit, Systems and Communication (ICCSC), International Conference on

作者： Saad-Eddine Chafi Younes Balboul Mohammed Fattah Said Mazer Moulhime El Bekkali Artificial Intelligence Data Sciences and Emerging Systems Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

This work presents a comparative analysis of fog and edge computing scheduling algorithms, focusing on their impact on IoT workload optimization. The analysis considers task allocation, load balancing, energy efficiency, latency, and scalability. An advanced experimental setup with diverse workload scenarios simulates real-world IoT environments. Existing algorithms, alongside modifications and novel introductions, are evaluated. Real-world applications showcase practical considerations and the adaptability of these algorithms. Refined metrics capture both immediate and long-term effects on scalability. The results inform future fog and edge computing research, considering emerging technologies. The study identifies key factors for optimizing IoT workloads and highlights the importance of scheduling algorithms for maximizing performance.

关键词： Measurement scheduling algorithms Scalability Load management Energy efficiency scheduling Resource management

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