检索结果-内蒙古大学图书馆

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Kiran Jagannadha Reddy Pamula Udayaraju Venkata Pradeep Reddy Pamaiahgari V. Dilip Kumar Walmart Bentonville AR USA Department of CSE SRM University AP India Wendys International LLC OH USA Department of CSE SRKR Engineering College

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud Computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free; otherwise, it must schedule the same or relevant resource. This paper demonstrates a Resource-Aware scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. The proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. The simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. The evaluation found that the RAS model is more suitable for cloud resource allocation.

关键词： Cloud computing Adaptation models Schedules Costs scheduling algorithms Computational modeling Heuristic algorithms Resource management Time factors Optimization

来源：评论

学校读者我要写书评

暂无评论

A DQN-based resource scheduling optimization algorithm for multi-computing nodes of large AI models

A DQN-based resource scheduling optimization algorithm for m...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： YuBin Gong JingJing Liu Xu Huang XueKe Bi WenBo Cui College of Electronic and Information Engineering Beijing Jiaotong University Beijing China China Mobile Group Liaoning Company Limited China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In the booming era of artificial intelligence, the demand for computing resources in AI large-model training is increasing exponentially. However, the traditional centralized computing resource management model fails to meet this growing demand. This paper focuses on the resource utilization issue of multiple computing center nodes and introduces the Deep Q-Network (DQN) algorithm to optimize the logic and strategy of computing task scheduling. A system model encompassing a specific state space, action space, and reward mechanism is established. A one-dimensional DNN network serves as the DQN training model, which is trained by means of the Mean Squared Error (MSE) loss function and the RMSProp optimizer. In the experiment, the DQN is compared with five traditional algorithm models. The results demonstrate that the DQN exhibits the smallest and most balanced fluctuations in resource utilization, boasting the best performance. Consequently, it offers an effective solution for computing resource management.

关键词： Training Fluctuations Mechatronics scheduling algorithms Computational modeling Reinforcement learning Reliability engineering scheduling Resource management Optimization

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Virtual Machine scheduling Algorithm in Cloud Data Center

An Energy-Efficient Virtual Machine Scheduling Algorithm in ...

引用

International Conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Md. Adnan Morshed Anabil Sarker Mehzabin Meem Fahad Ahammed K. M. Safin Kamal Ahmed Wasif Reza Dept. of CSE East West University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Power consumption has a significant influence on resource allocation, which has a negative effect on the environment. To lessen the negative effects, an effective resource allocation algorithm is needed. In this paper, we suggested a unique hybrid approach for energy-efficient scheduling of virtual machines (VMs) in cloud data centers called Energy Efficient Particle Swarm Optimization (EE-PSO), which combines Genetic algorithms (GA) and Particle Swarm Optimization (PSO). This integration is special since it makes use of both algorithms’ advantages to improve scheduling effectiveness and energy consumption. The novelty lies in how GA and PSO are combined, which differs from previous attempts where these algorithms were either used separately or in a less integrated manner. EE-PSO represents significant achievement toward achieving the goals of decreasing power usage and promoting green computing practices.

关键词： Data centers Cloud computing scheduling algorithms Scalability Energy efficiency Virtual machines scheduling Resource management Particle swarm optimization Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Online scheduling via Gradient Descent for Weighted Flow Time Minimization

arXiv

引用

arXiv 2024年

作者： Chen, Qingyun Im, Sungjin Petety, Aditya Electrical Engineering and Computer Science University of California 5200 N. Lake Road MercedCA95344 United States

In this paper, we explore how a natural generalization of Shortest Remaining Processing Time (SRPT) can be a powerful meta-algorithm for online scheduling. The meta-algorithm processes jobs to maximally reduce the objective of the corresponding offline scheduling problem of the remaining jobs: minimizing the total weighted completion time of them (the residual optimum). We show that it achieves scalability for minimizing total weighted flow time when the residual optimum exhibits supermodularity. Scalability here means it is O(1)-competitive with an arbitrarily small speed augmentation advantage over the adversary, representing the best possible outcome achievable for various scheduling problems. Thanks to this finding, our approach does not require the residual optimum to have a closed mathematical form. Consequently, we can obtain the schedule by solving a linear program, which makes our approach readily applicable to a rich body of applications. Furthermore, by establishing a novel connection to substitute valuations in Walrasian markets, we show how to achieve supermodularity, thereby obtaining scalable algorithms for various scheduling problems, such as matroid scheduling, generalized network flow, and generalized arbitrary speed-up curves, etc., and this is the first non-trivial or scalable algorithm for many of them. 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 Robust Multi-market scheduling Strategy for Battery Energy Storage Systems

A Robust Multi-market Scheduling Strategy for Battery Energy...

引用

IEEE General Meeting Power& Energy Society

作者： Sunho Jang Siddharth Bhela Nan Xue Electrical & Computer Engineering University of Michigan Ann Arbor MI USA Siemens Technology NJ USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

In this paper, we propose a novel robust battery energy storage system (BESS) scheduling algorithm that makes offers to multiple ancillary service markets. The proposed algorithm enables BESS to provide frequency regulation and synchronized reserve services while concurrently fulfilling commitments to a long-term capacity market without violating any operational constraints. In particular, the offers to the ancillary service markets scheduled by the proposed algorithm remain valid under any possible frequency regulation signal and synchronized reserve events within the defined uncertainty set. The case study demonstrates that the proposed algorithm is robust to the given uncertainties and outperforms two selected benchmarks.

关键词： Uncertainty Battery energy storage system scheduling algorithms Benchmark testing Robustness Real-time systems Planning Synchronization Frequency control

来源：评论

学校读者我要写书评

暂无评论

An Efficient Task scheduling for Security and Stability Control System Models on Multi-Core Systems

An Efficient Task Scheduling for Security and Stability Cont...

引用

International Conference on Dependable Systems and Their Applications (DSA)

作者： Zhi Wang Bo Shen College of Computer Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

In recent years, the large-scale integration of new energy sources, such as wind and photovoltaic power into power grid, has necessitated higher standards for the safe, stable, and flexible control of power systems. The Security and Stability Control System (SSCS) is proposed to monitor the operation of the grid. To address the cost and scale limitations, simulation has been emerging as a crucial method for analyzing safety and stability control systems. However, as the grid’s complexity increases, the precision and computation workload of the SSCS associated to the grid simulation model have significantly grown. Real-time task scheduling in multi-core systems is important for detecting abnormal events of complex stability control simulation models in time. This paper introduces a new scheduling algorithm specifically designed for the simulation model in power system. It also proposes an interference analysis method to address delays caused by contention for shared hardware resources. We conduct extensive experiments to analyze the proposed algorithm. Compared with existing scheduling algorithms, the scheduling algorithm of the paper achieves superior performance and effectiveness.

关键词： Upper bound scheduling algorithms Computational modeling Stability criteria Interference Power system stability Control systems Real-time systems Power grids Security

来源：评论

学校读者我要写书评

暂无评论

Federated Reinforcement Learning with Constellation Collaboration for Dynamic Laser Inter-Satellite Link scheduling

Federated Reinforcement Learning with Constellation Collabor...

引用

IEEE International Conference on Communications (ICC)

作者： Guanhua Wang Fang Yang Jian Song Zhu Han Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing P. R. China Shenzhen International Graduate School Tsinghua University Shenzhen P. R. China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The scheduling of the laser inter-satellite links (LISLs) can effectively improve the network performance but is challenging in mega-constellations due to the large amount of satellites. In this paper, inter-OP LISL scheduling is realized by federated reinforcement learning, which reduces complexity by decomposing global scheduling into independent decisions for each satellite and solved by multi-agent reinforcement learning. Based on the constellations collaboration, federated learning (FL) with partial and global aggregations is utilized to reduce the overhead for the model update, where the geostationary orbit (GEO)-GEO collaboration for global aggregation can be performed infrequently since the indirect associations among the GEO satellites according to the motion of low Earth orbit (LEO) satellite. Meanwhile, simulation results indicate that the proposed method reduces the average hop count by over two hops and decreases the number of LISLs by over 25% compared to the fixed LISLs, and the model update overhead is reduced to 46.6% of that in the centralized method.

关键词： Satellites scheduling algorithms Simulation Collaboration Low earth orbit satellites Reinforcement learning Dynamic scheduling

来源：评论

学校读者我要写书评

暂无评论

Effective Evacuation Planning by Leveraging Building Information 14

Effective Evacuation Planning by Leveraging Building Informa...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singhal, Rahul Choudhary, Lakshya Sarawagi, Ansh Makkar, Mohit Lnmiit Dept. of Mechanical-Mechatronics Engg. Jaipur India Lnmiit Dept. of Electronics and Communication Engg. Jaipur India

ISBN: (纸本)9798350335095

This paper addresses the necessity of evacuation planning in buildings. Prior evacuation planning for individuals inside the building ensures their safety during various disaster scenarios. The prime goal of safe evacuation is complete the process in the minimum time that individuals can rely on and reduces panic amongst them. The challenge lies in effectively utilising the building's architecture and floor plans to outline the procedures that efficiently guide occupants to building exits. The results demonstrate the benefits of utilising the building information in the prior planning evacuation process for clearing people from the building during emergencies. © 2023 IEEE.

关键词： scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Research on real-time dispatching of urban rail transit based on blockchain 9

Research on real-time dispatching of urban rail transit base...

引用

9th International Conference on Computer and Communications, ICCC 2023

作者： Li, Zhanbo Zhu, Qingchen Zhuang, Yan Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350317251

On the Metro lines, subway train scheduling varies with some trains experiencing delays and others operating on a regular schedule. The adjustment of timings between these two categories of trains is crucial. Different strategies are required for adjusting travel times and dwell times, making the subway system more complex than urban metro routes in such scenarios. In terms of train scheduling algorithms at stations to ensure reliability, this paper proposes a block-chain-based subway scheduling design based on foundational data including the initial operational schedules of subway trains, dwell times at various stations, and travel times between stations. © 2023 IEEE.

关键词： scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Hybrid Algorithm on Timeline Matrix for Task scheduling in Clouds

Hybrid Algorithm on Timeline Matrix for Task Scheduling in C...

引用

Chinese Automation Congress (CAC)

作者： Anni Zhang Yuanqing Xia Department of Mathematics Beijing Institute of Technology Beijing China School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In recent years, cloud computing is an emerging industry that serves many people who do not own sufficient compute resources in a flexible and efficient approach. However, it is difficult to optimize the cost of computing while meeting the QoS requirements of users, especially for those time-constrained workflows. In this article, we present a hybrid particle swarm optimization (PSO) scheduling algorithm, called HPSOTM, which aims to optimize the scheduling strategy of workflows while meeting deadline constraints in a cloud environment. In HPSOTM, a more suitable and refined optimization and solution-repair strategy is constructed for the timeline matrix of the virtual machine pool to improve the efficiency. At the same time, it also enriches the diversity of solutions in particle swarms and improves the convergence speed of feasible solutions. It mainly contains two major highlight features: 1) design a new slot-aware rule to reduce VM idle time, which could reduce execution costs; 2) apply the concept of partial critical path to complete deadline distribution, which could fit to time constraints precisely. Extensive experiments are conducted and the results show that the proposed algorithm outperforms other existing algorithms that minimize the execution cost of scheduling workflows with deadline constraints.

关键词： Industries Cloud computing Job shop scheduling Costs scheduling algorithms Quality of service Virtual machines Time factors Particle swarm optimization Optimization

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：