检索结果-内蒙古大学图书馆

IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Jui Mhatre Minh Nguyen Ahyoung Lee Hoseon Lee Department of Computer Science Kennesaw State University Marietta GA USA Department of Electrical and Computer Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

Long Range Wide Area Network (LoRaWAN) is a promising communication technology for environmental monitoring due to its low power consumption and long-range capabilities. Despite its advantages, several challenges are associated with LoRaWAN due to technical limitations, environmental factors, and operational complexities. Continued advancements in adaptive algorithms and AI-based optimization are essential for overcoming these challenges and fully realizing the potential of LoRaWAN in diverse IoT applications. Transmission parameter allocation is one of the most studied aspects of LoRaWAN, typically required to reduce energy consumption and improve the signal quality in dense LoRaWAN. Evaluations of the optimization algorithms for parameter allocation are usually done using simulators. However, they do not imitate the dynamic nature of the network environment and other signal characteristics. Thus, it becomes difficult to understand the performance of these algorithms when deployed on real devices. This paper introduces transmission parameter allocation strategies using a State–Action–Reward–State–Action (SARSA) and Deep Q-Learning Network (DQN) based Reinforcement Learning (RL)-based scheduling algorithm for allocating transmission parameters in LoRaWAN communication. We experimentally evaluate this algorithm in a water quality monitoring system using actual LoRaWAN devices to assess Signal to Noise ratio (SNR), Received Signal Strength Indicator (RSSI), Time on Air (ToA), and power consumption of our RL-based algorithms with the default and Adaptive Data Rate (ADR) in LoRaWAN communication.

关键词： Energy consumption Q-learning scheduling algorithms Heuristic algorithms LoRaWAN Water quality Logic gates scheduling Resource management Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Hybrid Population-Based Bi-Objective Task scheduling Algorithm for Fog Computing

Hybrid Population-Based Bi-Objective Task Scheduling Algorit...

引用

Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Shilpa Dinesh Vispute Nishi Gupta Priyanka Vashisht Department of Computer Science and Engineering The North Cap University Gurugram Haryana Department of Computer Science and Engineering Amity University Gurugram Haryana

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Applications utilizing the Internet of Things (IoT) operate on a platform that works efficiently and has capabilities of handling vast volumes of data processing. The type of application will determine if this platform is Cloud, Edge, or Fog. On the Fog Computing platform, realtime applications require a faster response time. Nowadays most demanding area in the Fog-Cloud environment is task scheduling. Fog computing is therefore mostly used to schedule processes with the least amount of energy and time overhead. The most important element that forms the foundation for reducing energy consumption and makespan is the choice of task scheduling methodology. This paper presented a Hybrid Population-based Bi-Objective Task scheduling (HPBOTS) algorithm that addresses the task scheduling problem by combining the principles of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulated experiment shows better performance of the proposed hybrid population-based bi-objective algorithm for makespan and energy efficiency compared to GA and PSO. Simulation results demonstrate a reduction in energy results by 8.28% and 11.21% when compared with PSO and GA. Also, the result shows proposed algorithm takes 4.02% less than the PSO makespan result and 2.08% less than the GA makespan result.

关键词： Schedules scheduling algorithms Simulation scheduling Internet of Things Time factors Task analysis

来源：评论

学校读者我要写书评

暂无评论

Incremental scheduling Using Genetic Algorithm

Incremental Scheduling Using Genetic Algorithm

引用

International Symposium on Electronics in Marine (ELMAR)

作者： Omar Hekal Daniel Onwuchekwa Roman Obermaisser Chair of Embedded Systems University of Siegen Siegen Germany

ISBN: (数字)9798350375428

ISBN: (纸本)9798350375435

Safety-critical systems are integral to ensuring the reliability and safety of operations in sectors where failures can lead to severe losses. These systems are characterized by their complex and reactive nature, requiring stringent adherence to functional and timing constraints. In real-time applications like those in the automotive industry, guaranteed functionality and timeliness are paramount. Task scheduling within these systems is a crucial component in preventing catastrophic failures. This paper presents an incremental scheduling algorithm based on a genetic algorithm designed to enhance the adaptability of embedded systems and minimize system disruptions, which will contribute to cutting down the system's re-validation cost. Our proposed two-level scheduling algorithm aims to minimize the changes to existing schedules, thereby reducing the computational overhead and re-validation costs, ensuring system stability and reliability. The first level computes a base schedule for the initial application model, while the second level adapts to new tasks with minimal adjustments. This approach maintains integrity and real-time delivery, outperforming traditional dynamic scheduling methods. The results showed that the proposed scheduling method outperforms the traditional genetic algorithm in minimizing changes to the base schedule by up to 57% for different application sizes.

关键词： Schedules Job shop scheduling Machine learning algorithms Embedded systems Costs scheduling algorithms Heuristic algorithms Real-time systems Reliability Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

DQN and Heuristic Precoding-aware scheduling in Cell-free mMIMO Networks

DQN and Heuristic Precoding-aware Scheduling in Cell-free mM...

引用

IEEE Conference on Wireless Communications and Networking

作者： Adam Girycki Md Arifur Rahman Sofie Pollin IS-Wireless Pulawska Plaza Piaseczno Poland Department of Electrical Engineering KU Leuven Belgium

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

We propose a deep Q-Learning (DQN) and heuristic-based radio resource scheduling (RRS) algorithms, which determine serving access points (APs), for maximum ratio transmission (MRT) precoder in the cell-free massive multiple-input multiple-output (CF mMIMO) networks. We show that, DQN-based algorithm yields up to 6% higher sum spectral efficiency (SE) than the heuristic and the reinforcement learning based algorithms for MRT precoder, but the heuristic algorithm yields 1000 times shorter computation time than the DQN-based algorithm. Thus, the low complexity MRT heuristic algorithms are the trade off between the performance and the cost.

关键词： Q-learning Costs Spectral efficiency scheduling algorithms Heuristic algorithms Noise Interference

来源：评论

学校读者我要写书评

暂无评论

Online AI-Generated Content Request scheduling with Deep Reinforcement Learning

Online AI-Generated Content Request Scheduling with Deep Rei...

引用

IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Chenglong Feng Ying Zheng X. Yuedong Fudan University Shanghai China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Artificial Intelligence-Generated Content (AIGC) represents a technique that AI automatically generates various forms of content that meet the personalized requirements of humans. To offer an easy access to AIGC techniques, an edge computing system is introduced with heterogeneous computing capability to process users' text-to-image AIGC requests. For text-to-image AIGC tasks, inference steps configuration of diffusion models has a great impact on inference time and quality of generated results. Thus, it is necessary to design an online scheduling scheme to dispatch users' AIGC requests to suitable servers and tune proper inference steps for AIGC requests in order to achieve better quality of service. Based on this motivation, we define a novel online text-to-image AIGC request scheduling problem under a heterogeneous edge computing scenario with the objective of striking a balance between tardiness of AIGC requests and quality of generated results. We formulate it into an integer programming problem. Then we transform this problem into a Markov Decision Process (MDP) model and adopt a deep reinforcement learning-based algorithm to solve this online problem. Simulation results show that our approach performs better and has a strong generalization ability, compared with the baseline random policy and greedy policy.

关键词： scheduling algorithms Simulation Text to image Transforms Quality of service Deep reinforcement learning Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Hybrid Artificial Bee Colony and Fibonacci Indicator Algorithm for the Resource-Constrained Project scheduling Problem

SSRN

引用

SSRN 2024年

作者： Etminaniesfahani, Alireza Gu, Hanyu School of Mathematical and Physical Sciences University of Technology Sydney 15 Broadway Ultimo SydneyNSW2007 Australia

The Artificial bee colony (ABC) is a widely used metaheuristic optimisation algo rithm due to its simplicity, adaptability, and efficiency. However, it has a notable drawback with a limited local search capability. ABFIA, the recently proposed extension of the ABC, has shown to have a strong exploration and fast convergence and has proven effective in addressing continuous global optimisation problems. Addressing combinatorial optimisation problems using ABFIA presents several challenges. These include the need to deal with a large number of constraints, such as the precedence and resource constraints in RCPSP, as well as to explore the com plex and discrete solution space efficiently. This study proposes a new extension of ABFIA, called combinatorial ABFIA (CABFIA), to tackle RCPSP. The algorithm’s performance has been investigated on 1560 instances from the well-known library PSPLIB with 30, 60, and 120 tasks. The results indicate that the proposed Combi natorial ABFIA yields superior outcomes compared to the best-reported results in the literature and the state-of-the-art algorithms. © 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

CHESS: Concurrent Charging with Efficient Phase scheduling

CHESS: Concurrent Charging with Efficient Phase Scheduling

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Dié Wu Chenglong Zhang Jingwen Li Tang Liu Kun She Jianhong Zhao University of Electronic Science and Technology of China Chengdu Sichuan China Sichuan Normal University Chengdu Sichuan China Chengdu Aerospace Communication Device Company Limited Chengdu Sichuan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Concurrent wireless charging offers significant performance improvements for Wireless Rechargeable Sensor Networks (WRSNs). However, wave interference, arising from interactions between electromagnetic waves from multiple chargers, disrupts this process. This results in uneven power distribution, potentially leading to significantly attenuated or even negligible energy reception at certain locations. This paper addresses this challenge by introducing the Concurrent cHarging with Efficient phaSe scheduling (CHESS) problem. CHESS maximizes the energy received by critical sensors through a novel on-demand phase scheduling approach. To achieve this, we propose a practical charging model with charger phases and wave interference effects. Subsequently, a charger grouping algorithm reduces computational complexity, followed by a phase vector searching algorithm to identify optimal phases for maximizing sensor energy reception. Finally, a phase scheduling algorithm enables dynamic adaptation to the evolving energy demands. Simulations show significant efficiency improvements, outperforming baseline algorithms by an average of ${8 9. 8 \%}$

关键词： Wireless communication Adaptation models Wireless sensor networks scheduling algorithms Heuristic algorithms Computational modeling Power distribution Interference Dynamic scheduling Vectors

来源：评论

学校读者我要写书评

暂无评论

Rule-Based scheduling for MPARs Performing Sensing and Communications

Rule-Based Scheduling for MPARs Performing Sensing and Commu...

引用

Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Augusto Aubry Antonio De Maio Luca Pallotta DIETI University of Naples “Federico II” Napoli Italy Department of Engineering (DiING) University of Basilicata Potenza Italy

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

Multifunction phased array radar (MPAR) is capable of performing sensing and communications by functionally grouping a phased array into tailored sub-apertures, each dedicated to a distinct task. Because of limited available resources, such as bandwidth, power aperture product, and time, it is important to properly allocate them to each sub-aperture. This article examines a rule-based task scheduling algorithm wherein communication (COM) looks are employed to fill the vacant time left by the radar tasks that are allocated first (namely, volume and cued search, update and confirmation tracking). The allocation of looks is carried out for each time slot based on task priorities; however, some tasks (i.e., volume, cued, and COM) are executed in parallel when the available bandwidth and power-aperture product (PAP) permit. Simulations conducted in scenarios demonstrate the validity of the proposed allocation strategy in terms of bandwidth utilization and time occupancy.

关键词： Phased arrays scheduling algorithms Simulation Bandwidth Radar Radar tracking scheduling Sensors Resource management Security

来源：评论

学校读者我要写书评

暂无评论

scheduling with Obligatory Tests

arXiv

引用

arXiv 2024年

作者： Dogeas, Konstantinos Erlebach, Thomas Liang, Ya-Chun Department of Computer Science Durham University United Kingdom Data Science Institute Columbia University United States

Motivated by settings such as medical treatments or aircraft maintenance, we consider a scheduling problem with jobs that consist of two operations, a test and a processing part. The time required to execute the test is known in advance while the time required to execute the processing part becomes known only upon completion of the test. We use competitive analysis to study algorithms for minimizing the sum of completion times for n given jobs on a single machine. As our main result, we prove using a novel analysis technique that the natural 1-SORT algorithm has competitive ratio at most 1.861. For the special case of uniform test times, we show that a simple threshold-based algorithm has competitive ratio at most 1.585. We also prove a lower bound that shows that no deterministic algorithm can be better than √2-competitive even in the case of uniform test times. 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

MJSCHED: A Moldable Job scheduling Algorithm in Heterogeneous Resource Environment

MJSCHED: A Moldable Job Scheduling Algorithm in Heterogeneou...

引用

International Conference on Computer and Communications (ICCC)

作者： Tianhai Zhao Zichen Yang Yunlan Wang Jianhua Gu Zhengxiong Hou School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Moldable jobs have the ability to dynamically adjust their resource requirements before execution, offering greater flexibility compared to rigid jobs that allocate fixed resources. Currently, most resource management systems do not fully consider the resource requirements of moldable jobs, and few scheduling algorithms address the scheduling of such jobs, especially those requiring both CPU and GPU resources. In this paper, we propose a moldable job backfill algorithm(MJSched) that dynamically adjusts job resource allocation to reduce turnaround time. First, a resource allocation strategy that minimizes turnaround time is applied to jobs at the head of the waiting queue. We utilize a greedy algorithm and a genetic algorithm to construct the set of backfill jobs, determine the computational nodes, and allocate the number of tasks for these jobs. Furthermore, a profit-first remaining resource scheduling strategy is proposed. We implement the Slurm scheduling plug-in based on our moldable job scheduling algorithm to support moldable job scheduling in heterogeneous resource environments. Simulation experiments demonstrate that our MJSched-GA algorithm effectively reduces both average job turnaround time and average job waiting time by 15.7%-22% and 18.8%-26.2% respectively, while improving cluster resource utilization by 8%-14 %.

关键词： Greedy algorithms scheduling algorithms Heuristic algorithms High performance computing Clustering algorithms Graphics processing units Dynamic scheduling Genetics Resource management Genetic algorithms

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：