期刊文献+

跨云环境下任务调度综述 被引量:5

Survey on Task Scheduling in Inter-Cloud Environment
在线阅读 下载PDF
导出
摘要 随着云计算技术的不断发展,越来越多的企业和组织开始采用跨云的方式进行IT交付.跨云环境可以更有效地应对传统单云环境资源利用率低、资源受限以及供应商锁定等问题,并对云资源进行统一管理.由于跨云环境中资源具有异构性,导致跨云任务调度变得更为复杂.基于此,如何合理地调度用户任务并将其分配到最佳的跨云资源上执行,成为了跨云环境中需要解决的重要问题.拟从跨云环境的角度出发,探讨该环境下任务调度算法研究的进展及挑战.首先,结合跨云环境特征将云计算分为联盟云、多云环境并进行详细介绍,同时回顾已有的任务调度类型并分析其优缺点;其次,根据研究现状选取代表性文献对跨云环境下任务调度算法进行整理、分析;最后探讨了跨云环境下任务调度算法研究中的不足和未来的研究趋势,为跨云环境下任务调度算法的进一步研究提供了参考. As cloud computing technology advances continuously,there are a growing number of enterprises and organizations choosing the inter-cloud approach to apply on IT delivery.Inter-cloud environments can efficiently solve problems such as low resource utilization,resource limitation,and vendor lock-in in traditional single-cloud environments,and manage cloud resources in an integrated model.Due to the heterogeneity of resources in the inter-cloud environment,which will complicate the scheduling of inter-cloud tasks.Based on the current status,how to logically schedule user tasks and allocate them to the most suitable inter-cloud resources for execution has developed to be an important issue to be solved in the inter-cloud environment.From the perspective of the inter-cloud environment,we discuss the progress and future challenges of research on the task of scheduling algorithms under this environment.Firstly,combined with the characteristics of an inter-cloud environment,cloud computing is divided into federated cloud and multi-cloud environments and introduced in detail.Meanwhile,the existing task scheduling types are reviewed and their advantages and disadvantages are analyzed.Secondly,based on the classification and current research procedure,representative documents are selected to analyze the algorithms for task scheduling on inter-cloud.Finally,shortcomings in research on algorithms for task scheduling in inter-cloud and future research trends are discussed,which provide a reference for further research on inter-cloud task scheduling.
作者 唐续豪 刘发贵 王彬 李超 蒋俊 唐泉 陈维明 何凤文 Tang Xuhao;Liu Fagui;Wang Bin;Li Chao;Jiang Jun;Tang Quan;Chen Weiming;He Fengwen(School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006;Peng Cheng Laboratory,Shenzhen,Guangdong 518000;Guangdong Provincial Tax Service,State Taxation Administration,Guangzhou 510627)
出处 《计算机研究与发展》 EI CSCD 北大核心 2023年第6期1262-1275,共14页 Journal of Computer Research and Development
基金 广东省基础与应用基础研究重大项目(2019B030302002) 广东省科技计划项目(2021B1111600001) 广州市重点领域研发计划项目(202007030006) 广州市“中国制造2025”产业发展资金项目(x2jsD8183470) 广东省工程技术研究中心建设项目(粤科产学研字[2016]176号)。
关键词 云计算 跨云 任务调度 联盟云 多云 cloud computing inter-cloud task scheduling federated cloud multi-cloud
作者简介 唐续豪,1996年生.博士研究生.CCF学生会员.主要研究方向为云计算和物联网,csxuhaotang@mail.scut.edu.cn;通信作者:刘发贵,1963年生.博士,教授,博士生导师.CCF会员.主要研究方向为云计算、大数据和物联网.fgliu@scut.edu.cn;王彬,1993年生.博士,助理研究员.CCF会员.主要研究方向为云计算、边缘计算和节能调度;李超,1993年生.博士研究生.主要研究方向为边缘计算和强化学习;蒋俊,1994年生.博士研究生.主要研究方向为机器学习、云计算、数据流分类和模糊系统;唐泉,1997年生.博士研究生.主要研究方向为计算机视觉和深度学习;陈维明,1964年生.学士.主要研究方向为云计算;何凤文,1980年生.硕士.主要研究方向为云计算.
  • 相关文献

参考文献9

二级参考文献44

  • 1Bergh F.,Engelbrecht A.P..Training product unit networks using cooperative particle swarm optimizers.In:Proceedings of International Joint Conference on Neural Networks,Washington,2001,1:126~131
  • 2Yoshida H.,Kawata K.,Yoshikazu F..A Particle swarm optimization for reactive power and voltage control considering voltage security assessment.IEEE Transactions on Power System,2000,15(4):1232~1239
  • 3Gao L.,Gao H.B..Particle swarm optimization based algorithm for cutting parameters selection.In:Proceedings of IEEE World Congress on Intelligent Control and Automation,Hangzhou,2004,4 :2847~ 2851
  • 4Parsopoulos K.E.,Vrahatis M.N..Recent approaches to global optimization problems through particle swarm optimiza tion.Natural Computing,2002,12(1):235~306
  • 5Salman A.,Ahmad I..Particle swarm optimization for task assignment problem.Microprocessors and Microsystems,2002,26(8):363~371
  • 6Kennedy J.,Eberhart R.C..A discrete binary version of the particle swarm algorithm.In:Proceedings of IEEE Conference on Systems,Man,and Cybernetics,Orlando,1997,5:4104~4108
  • 7Kennedy J.,Eberhart R.C..Particle swarm optimization.In:Proceedings of IEEE International Conference on Neutral Net works,Australia,1995,4:1942~1948
  • 8Shi Y.H.,Eberhart R.C..A modified particle swarm optimizer.In:Proceedings of IEEE Conference on Evolutionary Computation,Anchorage,1998,69~73
  • 9Wright A..Genetic Algorithms for Real Parameter Optimization-Foundations of Genetic Algorithms.San Mateo:Morgan Kaufmann Publishers,1991
  • 10Michalewicz Z.et al..How to Solve It:Modern Heuristics.Berlin:Springer-Verlag,2000

共引文献371

同被引文献55

引证文献5

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部