检索结果-内蒙古大学图书馆

International Conference on Intelligent Transportation

作者： Ximeng Liao Jihee Han Aaron Paz Martinez Mahnam Saeednia Transport and Planning Department Faculty of Civil Engineering TU Delft The Netherlands German Aerospace Center (DLR) Institute of Vehicle Concepts Berlin Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Under the pressing need for increasing sustainability of freight transport, the rail sector faces challenges in competing with its road counterpart which can be contributed to lower flexibility and reliability. To overcome these challenges, intermodal rail freight systems should undergo a transformative evolution to enhance flexibility and seamlessly integrate with broader mobility services. Crucial to the success of any such innovative solution is integration with the existing railway infrastructure. This study explores the concept of modular vehicles (MV s) in rail systems, focusing particularly on the innovative ‘Pod’ system introduced in the Pods4Rail project. It introduces a framework for Pods scheduling on the railway network, incorporating an overlap-level-based platooning. Experiments show that implementing this system can significantly reduce the makespan and optimize railway capacity utilization, especially when dealing with larger problem sizes.

关键词： Rails Sensitivity analysis scheduling algorithms Roads Pressing Rail transportation Reliability Sustainable development Intelligent transportation systems Logistics

来源：评论

学校读者我要写书评

暂无评论

Dynamic scheduling Strategies for Resource Optimization in Computing Environments

arXiv

引用

arXiv 2024年

作者： Wang, Xiaoye Western University London Canada

The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management still face many challenges. This paper proposes a container scheduling method based on multi-objective optimization, which aims to balance key performance indicators such as resource utilization, load balancing and task completion efficiency. By introducing optimization models and heuristic algorithms, the scheduling strategy is comprehensively improved, and experimental verification is carried out using the real Google Cluster Data dataset. The experimental results show that compared with traditional static rule algorithms and heuristic algorithms, the optimized scheduling scheme shows significant advantages in resource utilization, load balancing and burst task completion efficiency. This shows that the proposed method can effectively improve resource management efficiency and ensure service quality and system stability in complex dynamic cloud environments. At the same time, this paper also explores the future development direction of scheduling algorithms in multi-tenant environments, heterogeneous cloud computing, and cross-edge and cloud collaborative computing scenarios, and proposes research prospects for energy consumption optimization, adaptive scheduling and fairness. The research results not only provide a theoretical basis and practical reference for container scheduling under cloud-native architecture, but also lay a foundation for further realizing intelligent and efficient resource management. Copyright © 2024, The Authors. All rights reserved.

关键词： scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

A Task Admission scheduling Strategy Based on Adaptive Time Slicing in Mobile Edge Computing

A Task Admission Scheduling Strategy Based on Adaptive Time ...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yanpu Tang Zhuofan Liao Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The rise of Multi-access Edge Computing (MEC) speeds up mobile user services and resolves service delays caused by long-distance transmission to cloud servers. However, in task-intensive scenarios, edge server processing limitations lead to buffer congestion, increasing latency and reducing Quality of Service (QoS). Furthermore, the challenges of edge server task processing are increased by the varying deadline requirements of different tasks, the time variability of task arrivals, and the real-time fluctuations of the network. In this paper, we propose an Adaptive Time Slice Admission scheduling (ATSAS) strategy to solve these problems. Specifically, ATSAS proposes an Adaptive Time Slice Algorithm (ATSA) and a task Admission scheduling Algorithm (ASA). ATSA dynamically adjusts the time slice based on real-time network conditions and task flow characteristics. ASA, assisted by ATSA, dynamically adjusts task priorities based on the remaining data amount, deadline requirements, real-time network conditions, and the location of the task buffer. After that, ASA makes different scheduling strategies for tasks. Through the coordination of ATSA and ASA, the proposed strategy ensures the high efficiency and fairness of processing tasks in different realistic scenarios. The simulation results show that, compared with the existing O2A and OTDS in different scenarios, the proposed ATSAS reduces the average number of waiting services in the edge server buffer by 38.63% and 35.34%, and accelerates the average response speed of tasks by about 39.76% and 32.41%.

关键词： Distributed processing Multi-access edge computing Fluctuations scheduling algorithms Simulation Heuristic algorithms Quality of service Dynamic scheduling Real-time systems Servers

来源：评论

学校读者我要写书评

暂无评论

Train Once Apply Anywhere: Effective scheduling for Network Function Chains Running on FUMES

Train Once Apply Anywhere: Effective Scheduling for Network ...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Marcel Blöcher Nils Nedderhut Pavel Chuprikov Ramin Khalili Patrick Eugster Lin Wang SAP Vivenu USI Huawei Technologies Paderborn University

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The emergence of network function virtualization has enabled network function chaining as a flexible approach for building complex network services. However, the high degree of flexibility envisioned for orchestrating network function chains introduces several challenges to support dynamism in workloads and the environment necessary for their realization. Existing works mostly consider supporting dynamism by re-adjusting provisioning of network function instances, incurring reaction times that are prohibitively high in practice. Existing solutions to dynamic packet scheduling rely on centralized schedulers and a priori knowledge of traffic characteristics, and cannot handle changes in the environment like link *** fill this gap by presenting FUMES, a reinforcement learning based distributed agent design for the runtime scheduling problem of assigning packets undergoing treatment by network function chains to network function instances. Our design consists of multiple distributed agents that cooperatively work on the scheduling problem. A key design choice enables agents, once trained, to be applicable for unknown chains and traffic patterns including branching, and different environments including link failures. The paper presents the system design and shows its suitability for realistic deployments. We empirically compare FUMES with state-of-the-art runtime scheduling solutions showing improved scheduling decisions at lower server capacity.

关键词： Knowledge engineering Runtime scheduling algorithms Reinforcement learning Quality of service Traffic control Dynamic scheduling

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Early Packet Drop scheduling Policies in Criticality-Aware Wireless Sensor Networks

Evaluation of Early Packet Drop Scheduling Policies in Criti...

引用

International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Andras Pinter Leandro Soares Indrusiak Ian Gray Department of Computer Science University of York York United Kingdom School of Computing University of Leeds Leeds United Kingdom

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

This paper introduces three autonomous, criticality-aware packet scheduling policies that address the impact of high traffic loads and degraded conditions in wireless sensor networks. The proposed policies, collectively referred to as Early Packet Drop (EPD), leverage cross-layer information, including RPL Rank, link quality, and time-slotted Medium Access Control schedule, to mitigate Quality of Service degradation. Simulation results demonstrate that EPD consistently outperforms a Criticality-Monotonic scheduling (CMS) baseline.

关键词： Degradation Wireless communication Wireless sensor networks Schedules scheduling algorithms Simulation Telecommunication traffic

来源：评论

学校读者我要写书评

暂无评论

The Research and Implementation of Network Slices and Resources scheduling

The Research and Implementation of Network Slices and Resour...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jinfeng Wang Kun Xie Weiliang Liu Chengcheng Li Yasheng Zhang Dept. Software Engineering Beijing University of Posts and Telecommunications Beijing China Dept. Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China Dept. The 54th Research Institute of CETC China Electronics Technology Group Corporation Limited Shijiazhuang China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This thesis advocates for the enhancement of network data throughput and service capabilities through the establishment of device identity class isolation underpinned by SDN/NFV technologies. It proposes a network slicing mechanism to segregate devices into dedicated network slices with independently allocated resources. Further, a novel resource scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is introduced to ensure efficient resource distribution.

关键词： scheduling algorithms Network slicing Bandwidth Throughput Deep reinforcement learning Network function virtualization Resource management Software defined networking Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A Programmable Computing Power Network Forwarding and scheduling Method Based on SRv6

A Programmable Computing Power Network Forwarding and Schedu...

引用

Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Yuxiang Qiu Fangcheng Chu Yang Lu Lan Gan State Grid Electric Power Research Institute China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

With the rapid development of science and technology, the demand for computing service resources in various industries is increasing. At the same time, catalyzed by new technologies such as 5G, AI, cloud computing, and big data, richer and more complex information data is produced. In order to cope with the changes in the current development of informatization, computing power networks emerged as the times require. Compared with traditional networks, computing power networks can more accurately select appropriate computing power service resources for users, and can provide the best routing path based on the status of the network in real time. For now, computing power networks still face three major issues: first, how to select the best computing resources for users, second, how to plan routing paths to meet user needs, and finally, how to achieve efficient and accurate forwarding. In response to the challenges faced, this paper proposes a programmable computing power network forwarding and scheduling method based on SRv6 technology. It first selects the best computing power service resources for users by using the user satisfaction function S. Secondly, the OSPF routing algorithm and network forwarding cost indicators are used to calculate the best routing and forwarding path for users. Finally, based on the selected best path, SRv6 segment routing technology is used to achieve accurate, efficient, and low-latency forwarding.

关键词： Energy consumption Cloud computing Costs Job shop scheduling Accuracy scheduling algorithms Routing

来源：评论

学校读者我要写书评

暂无评论

Fairness in CPU scheduling: A Probabilistic Algorithm

Fairness in CPU Scheduling: A Probabilistic Algorithm

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Research on Resource Scheduling Algorithm Optimization in Cl...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：