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检索条件"主题词=Scheduling algorithms"

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Data Blocks scheduling Schemes for Regional Distributed Storage Networks

Data Blocks Scheduling Schemes for Regional Distributed Stor...

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IEEE International Conference on Blockchain (Blockchain)

作者： Tongtong Cheng Yang Liu Cheng Chi Zihang Yin China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

In the Web3, the data distributed storage has become a crucial method for secure, reliable, and efficient data storage, and an efficient distributed scheduling and distribution strategy is essential for achieving rapid data response. This paper proposes a distributed data scheduling strategy that improves the firefly algorithm to achieve optimal scheduling path selection for regional data storage. By considering the storage nodes spatial status, it enables the rapid distribution and storage of data blocks. The storage path is implemented by smart contracts and stored on the blockchain with the data block identifier. Through simulation, our proposed scheduling algorithm demonstrates better performance and higher trustworthiness compared to traditional storage models.

关键词： scheduling algorithms Smart contracts Memory Optimal scheduling scheduling Stability analysis Blockchains

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An Innovative Digital Twin of Integrated Transportation and Power Networks for Efficient scheduling of Autonomous Electric Vehicles

An Innovative Digital Twin of Integrated Transportation and ...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Reza Razi Haider Ali Frédéric Colas Bruno Francois Centrale Lille Junia ULR 2697 - L2EP Univ. Lille Arts et Métiers Institute of Technology Lille France

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The use of autonomous electric vehicles has gained significant attention due to the growing interest in sustainable transportation solutions. Shared autonomous vehicles also have the potential to minimize market investments, improve transit systems, and reduce local environmental impact. To address the challenges of scheduling and coordinating shared AEV fleets while considering their integration with power networks, a digital twin-based platform is proposed. The digital twin concept involves creating a virtual replica of a complex system, enabling real-time monitoring, analysis, and optimization. This paper introduces an adaptive digital twin model falling within the third phase of digital twin evolution, integrating transportation and power networks for efficient shared AEV scheduling. The platform utilizes real-time simulators for transportation and power networks, connected through communication protocols. The proposed system aims to enhance scheduling algorithms, consider power grid conditions, and optimize AEV charging infrastructure.

关键词： Protocols scheduling algorithms Heuristic algorithms Transportation Europe Electric vehicles Real-time systems Digital twins Vehicle dynamics Sustainable development

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Reputation-Aware scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach

Reputation-Aware Scheduling for Secure Internet of Drones: A...

引用

IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Hajar Moudoud Zakaria Abou El Houda Bouzian Brik L@bISEN ISEN Yncrea Ouest France Institut National de la Recherche Scientifique (INRS-EMT) Varennes Quebec Canada Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

The rapid integration of unmanned aerial vehicles (UAVs) into the Internet of Things (IoT) has paved the way for the Internet of Drones (IoD). Leveraging advanced technologies, including 5G, IoD introduces new opportunities across various industries. However, with the rapid proliferation of insecure drones, the perspective on IoD has changed from being a facilitator of smart cities to becoming a powerful tool for cyberattacks. To tackle this issue, this paper introduces a novel framework that uses a Multi-Agent federated learning and deep reinforcement learning approach to secure IoD networks against new emerging threats while preserving privacy. Moreover, we develop a reputation-aware scheduling algorithm that allocates bandwidth to reliable drones, emphasizing the reduction of communication expenses during the learning process and prioritizing participants demonstrating superior model learning performance. The effectiveness of our proposed framework is evaluated using real-world IoD-based attack data. The results obtained confirm that our proposed framework improves IoD security while ensuring privacy and resilient defense against potential threats in the IoD ecosystem.

关键词： Privacy Q-learning Federated learning Smart cities scheduling algorithms Ecosystems Deep reinforcement learning

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Fairness in CPU scheduling: A Probabilistic Algorithm

Fairness in CPU Scheduling: A Probabilistic Algorithm

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

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

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

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.

关键词： Measurement Computers Codes scheduling algorithms Probabilistic logic scheduling Resource management

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Research on Resource scheduling Algorithm Optimization in Cloud Computing Environment Based on Deep Learning

Research on Resource Scheduling Algorithm Optimization in Cl...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Chuanchen Wang Ruifeng Guo Pengfei Xiu Xuerao Li Hongliang Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

With the rapid advancement of cloud computing technology, user demands for Quality of Service (QoS) are continuously increasing. As a crucial link between user requirements and resource provisioning, resource scheduling has become a key focus in algorithm optimization research. Deep learning, a core technique in artificial intelligence, has been widely adopted to improve the efficiency of resource allocation in cloud computing, enabling intelligent and dynamic solutions. This paper provides a comprehensive review of deep learning-based resource scheduling algorithms, analyzing current progress and identifying existing challenges. It begins by introducing the fundamental concepts of resource scheduling and its pivotal role in maintaining service quality. Common deep learning models and their application scenarios in this domain are summarized. Next, recent research advances are discussed in detail, covering design principles, model training techniques, optimization strategies, and real-world outcomes. The strengths and limitations of various algorithms are compared, with a particular focus on metrics such as real-time responsiveness, accuracy, and computational complexity. Furthermore, this paper explores ongoing challenges, including data imbalance, limited generalization capabilities, constraints in real-time performance, and the need for energy efficiency. Finally, it envisions future research directions, emphasizing the potential of reinforcement learning and transfer learning to address these challenges. It is posited that continuous breakthroughs in deep learning will drive novel approaches for resource scheduling, advancing cloud computing services toward greater intelligence and efficiency.

关键词： Deep learning Training Cloud computing scheduling algorithms Reviews Computational modeling Transfer learning Quality of service Real-time systems Optimization

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Resource Optimization in Cloud Environment Using Advanced Metaheuristic scheduling Algorithm

Resource Optimization in Cloud Environment Using Advanced Me...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Pasnur Deeplaxmi Cholleti Vikas Santhosh Kumar Medishetti Geetanjali College of Engineering and Technology Hyderabad T.S. India Keshav Memorial Institute of Technology Hyderabad T.S. India Nalla Narasimha Reddy Education Society's Group of Institutions Hyderabad T.S. India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Task scheduling (TS) especially in cloud environments is quite a challenge because the resources within cloud are dynamic while the tasks present within a cloud have diverse characteristics. This paper aims at presenting a new scheduling technique based on one of the metaheuristic algorithms called Harris Hawks Optimization (HHO), which is derived from the social instinct and predatory hunting behavior of the Harris's hawks. The HHO algorithm is intended to deal with the challenges that appear in the pattern of tasks by adjusting the important factors like makespan, energy and resource utilization. We evaluate the performance of HHO through comprehensive simulations and compare it against traditional scheduling methods, highlighting its efficacy in balancing load and minimizing latency. The results demonstrate that the HHO-based scheduling approach achieves notable improvements in scheduling efficiency, with 15% improvement in energy consumption, a 16% reduction in makespan, and an 18% increase in throughput. The work that is presented in the paper offers a comprehensive approach to effective coordination of tasks in cloud computing systems to propose a satisfactory way to address this challenge in the context of current application-driven compute-intensive applications.

关键词： Cloud computing Energy consumption scheduling algorithms Heuristic algorithms Green communications Metaheuristics Throughput Dynamic scheduling Resource management Load modeling

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An Empirical Design and Implementation of Job scheduling Enhancement for Kubernetes Clusters

An Empirical Design and Implementation of Job Scheduling Enh...

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International Conference on Information Networking

作者： Van-Binh Duong Duong Phung-Ha Jangwon Lee Younghan Kim School of Electronic Engineering Soongsil University Seoul Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Batch job scheduling is a popular method in scheduling topics achieving considerable results. However, it is novel to bring those achievements to the cloud where problems of environments would limit algorithms. Although some projects have been introduced recently with benefits directly targeting machine learning jobs in cloud-native environments, there are gaps to be fulfilled. Consequently, this paper proposes an empirical design and implementation of deadline-aware enhancement of job scheduling for cloud-native environments. The proposal’s target is to automatically monitor, provision, and maintain machine learning jobs for batch job scheduling in cloud-native environments. The performance evaluation shows that the proposal has succeeded in reducing the average response time of machine learning jobs scheduled by different algorithms.

关键词： Performance evaluation Machine learning algorithms scheduling algorithms Clustering algorithms Machine learning scheduling Proposals

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Computer Professional English Teaching System Based on Resource scheduling Fusion Algorithm

Computer Professional English Teaching System Based on Resou...

引用

International Conference on Computer Engineering and Applications (ICCEA)

作者： Ying Yin Lei Ma Xi’an Mingde Institute of Technology

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In the teaching of computer related English, traditional English teaching platforms suffer from problems such as low resource openness and processing capabilities, poor adaptability to resource integration, and difficulties in synchronizing and updating data. Therefore, in order to ensure the stability of teaching resources on the teaching platform, a study proposes an English teaching platform based on the Canvas graphical variable library. The teaching platform is centered around the Canvas graphical variable library, which integrates different types of teaching resources through graphical association. By utilizing the openness and resource scheduling algorithm of the Canvas graphical variable library, the teaching resource samples are adaptively imported, achieving synchronous updates of teaching resources. The experimental results show that the average resource integration of traditional English teaching platforms and the English teaching platform based on the Canvas graphical variable library proposed in the study are 91.1% and 97.7%, respectively. The above results indicate that the English teaching platform based on the Canvas graphical variable library proposed in the study can improve the adaptability of resource integration and achieve synchronous updating of teaching resources while ensuring the stability of teaching resources.

关键词： Graphics scheduling algorithms Multimedia systems Education Libraries scheduling

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Heterogeneous Workflow scheduling Algorithm

Heterogeneous Workflow Scheduling Algorithm

引用

International Conference on Applied System Innovation (ICASI)

作者： Hsuan-Zong Wu Kuan-Chou Lai Dept. of Computer Science National Taichung University of Education Taichung ROC Taichung Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

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.

关键词： Technological innovation scheduling algorithms System performance Simulation Data transfer scheduling Complexity theory

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Research on Data Center Load Balancing Based on Particle Swarm Optimization Fusion Ant Colony Optimization Algorithm 3

Research on Data Center Load Balancing Based on Particle Swa...

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3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Song, Shiyu Wang, Jun School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

In recent years, with the continuous development of big data and cloud computing services, the scale of data centers has become larger and larger, and the rapidly increasing network traffic has also put forward higher requirements for the load balancing technology of data centers. Most of the traditional traffic scheduling algorithms are derived from IP networks. These algorithms do not take into account performance parameters such as link bandwidth and delay of real networks, resulting in very unsatisfactory results. In view of the above problems, this paper proposes a traffic scheduling algorithm based on the Particle Swarm Optimization Fusion Ant Colony Optimization (P-ACO) algorithm, which is mainly for dynamic traffic scheduling of elephant flow. First, the Software Defined Networking (SDN) controller obtains the network performance parameters and topology, then uses the PSO algorithm to iteratively search for solutions, integrates the solutions into the initial pheromone distribution of the ACO algorithm, and finally combined with the global network state, the optimal solution is obtained through the improved ACO algorithm. © 2023 SPIE.

关键词： scheduling algorithms

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