基于IFTS的云计算网络动态负载均衡方法
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摘要
针对云计算网络节点的异构性、资源配置的差异性和用户需求的不确定性等因素导致云计算网络极易出现负载不均衡的问题,在分析云计算节点负载模糊时序变化特性的基础上,构建了基于直觉模糊时间序列(IFTS)预测的云计算网络动态负载均衡模型,提出了基于IFCM的云节点计算资源自平衡方法,设计了基于IFTS预测的主动控制和基于反馈的被动调控相结合的虚拟机调度机制,并给出了云计算网络动态负载均衡策略,增强了云资源池的智能化管理水平,提升了云计算系统的整体性能.最后,通过典型实例验证了该方法的有效性和优越性.
Aiming at the problem that the cloud computing network is prone to load imbalance, for the cloud computing network node heterogeneity, the differences of the allocation of resources and users demand uncertainty factors, on the basis of analyzing the fuzzy temporal variation characteristics of cloud node load, the dynamic load balancing(DLB) model of cloud computing network based on intuitionistic fuzzy time series(IFTS) is proposed. Meanwhile, the self-balancing algorithm for cloud node computing resource based on IFCM is proposed, the virtual machine scheduling mechanism based on mixing together the active control based on IFTS prediction and the virtual control based on feedback is designed, and the cloud computing network dynamic load balancing strategy is presented, which effectively enhances the intelligent management level of cloud resource pool, improves the overall performance of cloud computing system. Finally, the validity and superiority of the proposed method are verified by a typical example.
Aiming at the problem that the cloud computing network is prone to load imbalance, for the cloud computing network node heterogeneity, the differences of the allocation of resources and users demand uncertainty factors, on the basis of analyzing the fuzzy temporal variation characteristics of cloud node load, the dynamic load balancing(DLB) model of cloud computing network based on intuitionistic fuzzy time series(IFTS) is proposed. Meanwhile, the self-balancing algorithm for cloud node computing resource based on IFCM is proposed, the virtual machine scheduling mechanism based on mixing together the active control based on IFTS prediction and the virtual control based on feedback is designed, and the cloud computing network dynamic load balancing strategy is presented, which effectively enhances the intelligent management level of cloud resource pool, improves the overall performance of cloud computing system. Finally, the validity and superiority of the proposed method are verified by a typical example.
引文
[1]郭晴,杨海霞,刘永泰.云计算环境下的复杂数据库并行调度模型仿真[J].计算机仿真,2015, 32(6):360-363.Guo Q, Yang H X, Liu Y T. Simulation of parallel scheduling for complex database under cloud computing environment[J]. Computer Simulation, 2015, 32(6):360-363.
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[5]陈波,张曦煌.基于分层与容错机制的云计算负载均衡策略[J].计算机应用,2013, 33(11):3155-3159.Chen B, Zhang X H. Load balancing strategy of cloud computing based on multi-layer and fault-tolerant mechanism[J]. Journal of Computer Applications, 2013, 33(11):3155-3159.
[6]程春玲,张登银,徐玉,等.一种面向云计算的分态式自适应负载均衡策略[J].南京邮电大学学报(自然科学版),2012,32(4):53-58.Cheng C L, Zhang D Y, Xu Y, et al. A sub-state adaptive load balancing strategy for cloud computing[J].Journal of Nanjing University Posts and Telecommunications(Natural Science), 2012, 32(4):53—58.
[7] Yang Z, Dong G, Li C. Algorithm study of load balance based on live migration of VM in cloud computing[J].International Journal of Advancements in Computing Technology, 2013, 5(3):141-150.
[8] Xu D Y, Yang S L, Liu R P. A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers[J]. Journal of Zhejiang University Science C:Computer&Electronics, 2013, 14(11):845-858.
[9] Cao J, Fu J W, Li M L, et al. CPU load prediction for cloud environment based on a dynamic ensemble model[J].Software Practice and Experience, 2014, 44(7):793-804.
[10]余钦水.云计算环境下基于预测的负载均衡技术研究与实现[D].镇江:江苏大学,2016.Yu Q S. Research and implementation of load balancing based on predicting under cloud computing environment[D]. Zhenjiang:Jiangsu University, 2016.
[11]郑寇全,雷英杰,王睿,等.基于线性IFTS的弹道中段目标融合识别方法[J].控制与决策,2014, 29(6):1047-1052.Zheng K Q, Lei Y J, Wang R, et al. Method of target fusion recognition in ballistic midcourse based on linear IFTS[J]. Control and Decision, 2014, 29(6):1047-1052.
[12] Wang Y N, Lei Y J, Lei Y, et al. A multi-factor high-order intuitionistic fuzzy time series forecasting model[J].Journal of Systems Engineering and Electronics, 2016, 27(5):1054—1062.
[13]王亚男,雷英杰,雷阳,等.高阶多元直觉模糊时间序列预测模型[J].东南大学学报(自然科学版),2016, 46(3):505-512.Wang Y N, Lei Y J, Lei Y, et al. A high-order multi-variable intuitionistic fuzzy time series forecasting model[J].Journal of Southeast University(Natural Science), 2016, 46(3):505-512.
[14]郑寇全,雷英杰,王睿,等.参数自适应的长期IFTS测算法[J].系统工程与电子技术,2014, 29(5):941-945.Zheng K Q, Lei Y J, Wang R, et al. Method of long-term IFTS forecasting based on parameter adaptation[J].Systems Engineering and Electronics, 2014, 29(5):941-945.
[15]郑寇全,雷英杰,王睿.基于直觉模糊线性方程组的IFTS测方法[J].控制与决策,2014,29(5):941-945.Zheng K Q, Lei Y J, Wang R, et al. Forecasting method of IFTS based on intuitionistic fuzzy linear equations[J].Control and Decision, 2014, 29(5):941-945.
[2]徐爱萍,吴笛,徐武平,等.实时多任务异构云计算平台负载均衡算法[J].中国科学技术大学学报,2016, 46(3):215-221.Xu A P, Wu D, Xu W P, et al. Real-time multitask load balance algorithm for heterogeneous cloud computing platforms[J]. Journal of University of Science and Technology of China, 2016, 46(3):215-221.
[3] Rahman M, Iqbal S, Gao J. Load balancer as a service in cloud computing[C]//Proceeding of the 8th International Symposium on Service Oriented System Engineering, 2014:204—211.
[4] Long Q Q, Lin J, Sun Z X. Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations[J]. Simulation Modeling Practice and Theory, 2011, 19(4):1021-1034.
[5]陈波,张曦煌.基于分层与容错机制的云计算负载均衡策略[J].计算机应用,2013, 33(11):3155-3159.Chen B, Zhang X H. Load balancing strategy of cloud computing based on multi-layer and fault-tolerant mechanism[J]. Journal of Computer Applications, 2013, 33(11):3155-3159.
[6]程春玲,张登银,徐玉,等.一种面向云计算的分态式自适应负载均衡策略[J].南京邮电大学学报(自然科学版),2012,32(4):53-58.Cheng C L, Zhang D Y, Xu Y, et al. A sub-state adaptive load balancing strategy for cloud computing[J].Journal of Nanjing University Posts and Telecommunications(Natural Science), 2012, 32(4):53—58.
[7] Yang Z, Dong G, Li C. Algorithm study of load balance based on live migration of VM in cloud computing[J].International Journal of Advancements in Computing Technology, 2013, 5(3):141-150.
[8] Xu D Y, Yang S L, Liu R P. A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers[J]. Journal of Zhejiang University Science C:Computer&Electronics, 2013, 14(11):845-858.
[9] Cao J, Fu J W, Li M L, et al. CPU load prediction for cloud environment based on a dynamic ensemble model[J].Software Practice and Experience, 2014, 44(7):793-804.
[10]余钦水.云计算环境下基于预测的负载均衡技术研究与实现[D].镇江:江苏大学,2016.Yu Q S. Research and implementation of load balancing based on predicting under cloud computing environment[D]. Zhenjiang:Jiangsu University, 2016.
[11]郑寇全,雷英杰,王睿,等.基于线性IFTS的弹道中段目标融合识别方法[J].控制与决策,2014, 29(6):1047-1052.Zheng K Q, Lei Y J, Wang R, et al. Method of target fusion recognition in ballistic midcourse based on linear IFTS[J]. Control and Decision, 2014, 29(6):1047-1052.
[12] Wang Y N, Lei Y J, Lei Y, et al. A multi-factor high-order intuitionistic fuzzy time series forecasting model[J].Journal of Systems Engineering and Electronics, 2016, 27(5):1054—1062.
[13]王亚男,雷英杰,雷阳,等.高阶多元直觉模糊时间序列预测模型[J].东南大学学报(自然科学版),2016, 46(3):505-512.Wang Y N, Lei Y J, Lei Y, et al. A high-order multi-variable intuitionistic fuzzy time series forecasting model[J].Journal of Southeast University(Natural Science), 2016, 46(3):505-512.
[14]郑寇全,雷英杰,王睿,等.参数自适应的长期IFTS测算法[J].系统工程与电子技术,2014, 29(5):941-945.Zheng K Q, Lei Y J, Wang R, et al. Method of long-term IFTS forecasting based on parameter adaptation[J].Systems Engineering and Electronics, 2014, 29(5):941-945.
[15]郑寇全,雷英杰,王睿.基于直觉模糊线性方程组的IFTS测方法[J].控制与决策,2014,29(5):941-945.Zheng K Q, Lei Y J, Wang R, et al. Forecasting method of IFTS based on intuitionistic fuzzy linear equations[J].Control and Decision, 2014, 29(5):941-945.
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