期刊文献+

基于Mininet的LRMC QoS路由算法仿真研究 被引量:2

Simulation Research on LRMC QoS Routing Algorithm Based on Mininet
下载PDF
导出
摘要 软件定义网络(Software-Defined Networking,SDN)使得网络功能可以由控制层以软件编程形式实现并下发给数据转发层的交换机执行,提高网络控制灵活性的同时也增加了硬件成本。针对传统网络架构因数据业务多样化导致的网络构建成本高、多类型业务(Quality of service,QoS)无法得到有效保障的问题,提出了一种可满足多类型业务QoS的动态自适应路由算法-拉格朗日松弛多约束(Lagrangian Relaxation based Multiple Constains,LRMC)QoS路由算法,在Mininet仿真网络中部署实现。利用Mininet CLI等功能对LRMC多约束QoS路由算法进行仿真对比试验。仿真结果参数表明,上述算法在连通时间收敛性及性能上均表现出明显优势。 The application of Software-Defined Networking(SDN)enables network functions to be implemented by the control layer in the form of dynamic software and delivered to the SDN switch for execution,which improves the flexibility of network control and also increases hardware costs.In view of the inconvenience caused by the high cost of SDN switches and the small number of types,the Mininet system was used to simulate the SDN network.Aiming at the problem of high network construction cost and ineffective guarantee of qualityof services Quality of service(QoS)caused by the diversification of data services in the traditional network architecture,a dynamic adaptive routing algorithm,the Lagrangian Relaxation based Multiple Constains(LRMC)QoS routing algorithm that can meet the QoS of multiple types of services,was proposed and implemented in the mininet simulation network.Mininet CLI and other functions were used to simulate and compare the LRMC multi-constrained QoS routing algorithm.Simulation results show that the algorithm has obvious advantages in connection time convergence and performance.
作者 傅妍芳 宋新美 张赵晨子 苏一昶 FU Yan-fang;SONG Xin-mei;ZHANG Zhao-chen-zi;SU Yi-chang(School of Computer Science and Engineering,Xi'an Technological University,Xi'an Shanxi 710021,China;Industrial and Commercial Bank of China,Xi'an Shanxi 710021,China)
出处 《计算机仿真》 北大核心 2022年第8期212-217,共6页 Computer Simulation
关键词 软件定义网络 多类型业务 拉格朗日松弛多约束算法 多约束算法 SDN QoS LRMCalgorithm Multi-constraint algorithm
  • 相关文献

参考文献4

二级参考文献30

  • 1Olston C, Chiou G, Chimis L, et al. Nova: Continuous Pig/Hadoop Workflows [C]// Proceedings of the SIGMOD'2011, Athens, Greece. USA: ACM, 2011: 1081-1090.
  • 2Mao Y, Wu W, Zhang H, et al. GreenPipe: A Hadoop Based Workflow System on Energy-Efficient Clouds [C]// Proceedings of the IPDPSW'2012, Shanghai, China. USA: IEEE, 2012: 2211-2219.
  • 3Wang L, Tao J, Ranjan R, et al. G-Hadoop: MapReduce Across Distributed Data Centers for Data-Intensive Computing [J]. Future Generation Computer Systems (S0167-739X), 2013, 29(3): 739-750.
  • 4Ahmed R, DeSmedt P, Kent W, et al. Pegasus: A System for Seamless Integration of Heterogeneous Information Sources [C]//Compcon Spring'91. Digest of Papers. USA IEEE, 1991: 128-136.
  • 5Du Z, Hu J, Chen Y. Optimized QoS-aware Replica Placement Heuristics and Applications in Astronomy Data Grid [J]. Journal of Systems and Software (S0164-1212), 2011, 84(7): 1224-1232.
  • 6Fedak G, He H, Cappello F. BitDew: A Programmable Environment for Large-Scale Data Management and Distribution [J]. Journal of Network and Computer Applications (S 1084-8045), 2009, 32(5): 961-975.
  • 7Gu Y, Grossman R. Toward Efficient and Simplified Distributed Data Intensive Computing [J]. IEEE Transactions on Parallel and Distributed Systems (S1045-9219). 2011, 22(6): 974-984.
  • 8Jayalath C, Stephen J, Eugster P. From The Cloud to The Atmosphere: Running MapReduce Across Data Centers [J]. IEEE Transactions on Computers (S0018-9340), 2014, 63(1): 74-87.
  • 9Dean J, Ghemawat S, MapReduce: Simplified Data Processing on Large Clusters [J]. Communications of The ACM (S0001-0782). 2008, 51(1): 107-113.
  • 10Sakellariou R, Zhao H, Tsiakkouri E, et al. Scheduling Workflows with Budget Constraints [C]// Integrated Research in GRID Computing. USA: Springer, 2007: 189-202.

共引文献16

同被引文献18

引证文献2

二级引证文献1

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