计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 309-314.doi: 10.11896/jsjkx.181002000

• 交叉与前沿 • 上一篇    下一篇

基于改进细菌觅食算法的云计算资源调度策略

赵宏伟, 田力威   

  1. (沈阳大学信息工程学院 沈阳110044)
  • 收稿日期:2018-09-28 出版日期:2019-11-15 发布日期:2019-11-14
  • 通讯作者: 田力威 教授,主要研究方向为数据智能处理技术,E-mail:tianliwei@163.com
  • 作者简介:赵宏伟 男,教授,主要研究方向为云计算、大数据、群体智能算法优化,E-mail:zhw30@163.com。
  • 基金资助:
    本文受国家自然科学基金面上项目(71672117),辽宁省自然科学基金(20170540646,20180550658)资助。

Cloud Computing Resource Scheduling Strategy Based on Improved Bacterial Foraging Algorithm

ZHAO Hong-wei, TIAN Li-wei   

  1. (School of Information Engineering,Shenyang University,Shenyang 110044,China)
  • Received:2018-09-28 Online:2019-11-15 Published:2019-11-14

摘要: 资源调度是云计算的核心问题之一,调度算法的好坏直接影响着系统的处理能力。生物群体智能算法是一类模仿群体生物在自然界进化过程中表现出的群体智能性的算法,具有良好的协调性和整体稳定性。将菌群觅食算法应用到云计算资源调度的计算方法中,可以利用菌群算法对节点进行复制和消亡,对云计算资源调度节点的分配情况进行控制。针对传统菌群算法中随机选择趋化过程所造成的资源变化区间过大的问题,文中提出了改进的基于群体感应交流机制的细菌觅食CBFO优化算法和在群体协作过程中引入细菌趋化动作的MPSOBS优化算法,根据节点周围的节点情况和整个菌群的情况选取趋化因子,使趋化的过程更加准确。仿真实验结果表明,所提算法在任务的执行时间、系统负载均衡和资源服务质量方面均优于BFO算法,在提高资源利用率的同时能保证云应用的服务质量。

关键词: 群体智能, 细菌觅食, 云计算, 资源调度

Abstract: As one of the core problems of cloud computing,the efficiency of scheduling algorithm has a direct impact on the operation capacity of the system.Swarm intelligence algorithm,with good coordination and overall stability,is one kind of swarm intelligence algorithms which imitates swarm intelligence in the process of evolution swarm.This paper presented a calculation method of bacteria foraging algorithm applied to cloud computing resource scheduling algorithm,which can be used to control the node allocation of cloud computing resource scheduling by using bacterial swarm algorithm to copy and perish the nodes.According to the problem of too much resource change interval caused by the random selection chemotax in the traditional flora swarm algorithm,the bacteria foraging CBFO optimization algorithm based on Quorum Sensing mechanism and the MPSOBS optimization algorithm introducing bacteria chemotaxis action in the process of group collaboration were proposed in this paper.According to the environment around the nodes and the situation of the whole flora,the chemotaxis factor is selected to make the process of chemotaxis more accurate,which is implemented on the cloud computing platform.The simulation results show that the proposed algorithm is more efficient than the BFO algorithm in terms of task execution time,system load balancing and resource service quality,and can improve the service quality of cloud applications while improving resource utilization.

Key words: Bacterial foraging, Cloud computing, Resource scheduling, Swarm intelligence

中图分类号: 

  • TP331
[1]HOSSAIN S.Infrastructure as a service[J].Cloud Computing Service & Deployment Models Layers & Management,2013,22(7):26-49.
[2]TSAFRIR D,SCHUSTER A,BENYEHUDA M,et al.Deconstructing Amazon EC2 Spot Instance Pricing[C]∥Third International Conference on Cloud Computing Technology and Science.Washington,DC,USA,2012:304-311.
[3]KIPPENBROCK T,HOLLOWAY E,MOORE D D.GoogleDocs[J].CIN:Computers,Informatics,Nursing,2010,28(3):138-140.
[4]ERLYKIN A D,HARPER D A T,SLOAN T,et al.Data from:Mass extinctions over the last 500 myr:an astronomical cause?[J].Palaeontology,2017,60(2):365-372.
[5]GAO S,LIU X,ZHANG R,et al.Analysis of Block OMP using Block RIP[J].微生物学报,1999,97(7):162-171.
[6]BROWNE M C,CLARKE E M,MISHRA B.Automatic Verification of Sequential Circuits Using Temporal Logic[J].IEEE Transactions on Computers,2006,35(12):1035-1044.
[7]MITRA S,DATTA S,AND T P.Introduction to Physical Polymer Science[J].Macromolecular Chemistry & Physics,2010,207(8):787-787.
[8]MAJHI J,SMID M.Multi-criteria geometric optimization problems in layered manufacturing[C]∥The Fourteenth Symposium on Computational Geometry.ACM,1998:19-28.
[9]WANG G H,LI Q H,LIU A F.Multi-objective optimizationcloud workflow scheduling evolutionary genetic algorithm [J].Computer Sciences,2018,45(5):31-37.(in Chinese)
王国豪,李庆华,刘安丰,多目标最优化云工作流调度进化遗传算法[J].计算机科学,2018,45(5):31-37.
[10]WEI X R,WANG F.A Reliability-Driven Cloud WorkflowScheduling Genetic Algorithm [J].Application Research of Computers,2018,35(5):1390-1394.(in Chinese)
魏秀然,王峰.一种可靠性驱动的云工作流调度遗传算法[J].计算机应用研究,2018,35(5):1390-1394.
[11]TIAN G H,MENG D,ZHAN J F.Resource dynamic provisioning strategy based on failure rules in cloud computing environment[J].Journal of Computer,2010,33(10):1859-1872.(in Chinese)
田冠华,孟丹,詹剑锋.云计算环境下基于失效规则的资源动态提供策略[J].计算机学报,2010,33(10):1859-1872.
[12]WANG Z J,CHEN Y J.Research on I/O Resource Utility Optimization Scheduling Algorithm for Cloud Storage [J].Computer Research and Development,2013,50(8):1657-1666.(in Chinese)
王健宗,谌炎俊.面向云存储的I/O资源效用优化调度算法研究[J].计算机研究与发展,2013,50(8):1657-1666.
[13]VAQUERO L M,RODERO-MERINO L,MORÁN D.Locking the sky:a survey on IaaS cloud security[J].Computing,2011,91(1):93-118.
[14]ARABNEJAD H,BARBOSA J.A Budget Constrained Scheduling Algorithm for Workflow Applications[J].Journal of Grid Computing,2014,12(4):665-679.
[15]XU Z J,CHEN S X.Research on Fusion Algorithm Based on Membrane Computing and Ant Colony Algorithm in Cloud Computing Resource Scheduling [J].Computer Measurement and Control,2017,25(1):120-127.(in Chinese)
徐浙君,陈善雄.基于膜计算和蚁群算法的融合算法在云计算资源调度中的研究[J].计算机测量与控制,2017,25(1):120-127.
[1] 柳鹏, 刘波, 周娜琴, 彭心怡, 林伟伟.
混合云工作流调度综述
Survey of Hybrid Cloud Workflow Scheduling
计算机科学, 2022, 49(5): 235-243. https://doi.org/10.11896/jsjkx.210300303
[2] 高诗尧, 陈燕俐, 许玉岚.
云环境下基于属性的多关键字可搜索加密方案
Expressive Attribute-based Searchable Encryption Scheme in Cloud Computing
计算机科学, 2022, 49(3): 313-321. https://doi.org/10.11896/jsjkx.201100214
[3] 宁玉辉, 姚喜.
一种应急指挥系统的设计与实现
Design and Implementation of Emergency Command System
计算机科学, 2021, 48(6A): 613-618. https://doi.org/10.11896/jsjkx.201000136
[4] 宋海宁, 焦健, 刘永.
高速公路中的移动边缘计算研究
Research on Mobile Edge Computing in Expressway
计算机科学, 2021, 48(6A): 383-386. https://doi.org/10.11896/jsjkx.200900212
[5] 王政, 姜春茂.
一种基于三支决策的云任务调度优化算法
Cloud Task Scheduling Algorithm Based on Three-way Decisions
计算机科学, 2021, 48(6A): 420-426. https://doi.org/10.11896/jsjkx.201000023
[6] 潘瑞杰, 王高才, 黄珩逸.
云计算下基于动态用户信任度的属性访问控制
Attribute Access Control Based on Dynamic User Trust in Cloud Computing
计算机科学, 2021, 48(5): 313-319. https://doi.org/10.11896/jsjkx.200400013
[7] 殷子樵, 郭炳晖, 马双鸽, 米志龙, 孙怡帆, 郑志明.
群智体系网络结构的自治调节:从生物调控网络结构谈起
Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network
计算机科学, 2021, 48(5): 184-189. https://doi.org/10.11896/jsjkx.210200161
[8] 马泽华, 刘波, 林伟伟, 李加伟.
无服务器平台资源调度综述
Survey of Resource Scheduling for Serverless Platforms
计算机科学, 2021, 48(4): 261-267. https://doi.org/10.11896/jsjkx.200800023
[9] 陈玉平, 刘波, 林伟伟, 程慧雯.
云边协同综述
Survey of Cloud-edge Collaboration
计算机科学, 2021, 48(3): 259-268. https://doi.org/10.11896/jsjkx.201000109
[10] 蒋慧敏, 蒋哲远.
企业云服务体系结构的参考模型与开发方法
Reference Model and Development Methodology for Enterprise Cloud Service Architecture
计算机科学, 2021, 48(2): 13-22. https://doi.org/10.11896/jsjkx.200300044
[11] 王文娟, 杜学绘, 任志宇, 单棣斌.
基于因果知识和时空关联的云平台攻击场景重构
Reconstruction of Cloud Platform Attack Scenario Based on Causal Knowledge and Temporal- Spatial Correlation
计算机科学, 2021, 48(2): 317-323. https://doi.org/10.11896/jsjkx.191200172
[12] 毛瀚宇, 聂铁铮, 申德荣, 于戈, 徐石成, 何光宇.
区块链即服务平台关键技术及发展综述
Survey on Key Techniques and Development of Blockchain as a Service Platform
计算机科学, 2021, 48(11): 4-11. https://doi.org/10.11896/jsjkx.210500159
[13] 王勤, 魏立斐, 刘纪海, 张蕾.
基于云服务器辅助的多方隐私交集计算协议
Private Set Intersection Protocols Among Multi-party with Cloud Server Aided
计算机科学, 2021, 48(10): 301-307. https://doi.org/10.11896/jsjkx.210300308
[14] 雷阳, 姜瑛.
云计算环境下关联节点的异常判断
Anomaly Judgment of Directly Associated Nodes Under Cloud Computing Environment
计算机科学, 2021, 48(1): 295-300. https://doi.org/10.11896/jsjkx.191200186
[15] 徐蕴琪, 黄荷, 金钟.
容器技术在科学计算中的应用研究
Application Research on Container Technology in Scientific Computing
计算机科学, 2021, 48(1): 319-325. https://doi.org/10.11896/jsjkx.191100111
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!