智能多代理客户服务器负荷状态自动检测技术
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摘要
智能多代理客户服务器负荷状态表现为一组非线性时间序列,通过对智能多代理客户服务器负荷状态的准确自动检测,避免智能多代理客户服务器负荷状态过载和服务器数据转发集中拥堵,保障客户服务器稳定可靠运行。提出一种基于Lyapunov指数谱预测的智能多代理客户服务器负荷状态自动检测算法。构建了智能多代理客户服务器负荷状态数据传输链路模型,采用IIR滤波实现对智能多代理客户服务器负荷状态数据信息流的抗干扰滤波处理,对智能多代理客户服务器负荷状态时域分量进行Lyapunov指数谱特征提取,根据Lyapunov指数在谱图中的规则性实现对负荷状态走势的自动检测,实现智能多代理客户服务器负荷状态自动检测算法改进。仿真实验结果表明,采用该算法进行智能多代理客户服务器负荷状态自动检测的自动性较好,指向性较高,抗干扰力较强,在多代理客户服务器负荷管理和调度中具有较好的应用性。
The load state of intelligent multi-agent client server is represented as a group of nonlinear time series,and the load state of intelligent multi-agent client server is detected automatically and accurately.To avoid the overload of intelligent multi-agent client server and the centralized congestion of server data forwarding,so as to ensure the stable and reliable operation of the client and server.An intelligent multi-agent client/server load state automatic detection algorithm based on Lyapunov exponential spectrum prediction is proposed.The load state data transmission link model of intelligent multi-agent client server is constructed,and the anti-interference filtering processing of load state data flow of intelligent multi-agent client server is realized by using IIR filter.The time domain component of load state of intelligent multi-agent client server is extracted by Lyapunov exponent spectrum feature,and the trend of load state is automatically detected according to the rule of Lyapunov exponent in spectrum diagram.The algorithm of intelligent multi-agent client-server load state automatic detection is improved.The simulation results show that the proposed algorithm has the advantages of good automaticity,high directivity and strong anti-interference ability for intelligent multi-agent client/server load state detection.It has good application in load management and scheduling of multi-agent client and server.
The load state of intelligent multi-agent client server is represented as a group of nonlinear time series,and the load state of intelligent multi-agent client server is detected automatically and accurately.To avoid the overload of intelligent multi-agent client server and the centralized congestion of server data forwarding,so as to ensure the stable and reliable operation of the client and server.An intelligent multi-agent client/server load state automatic detection algorithm based on Lyapunov exponential spectrum prediction is proposed.The load state data transmission link model of intelligent multi-agent client server is constructed,and the anti-interference filtering processing of load state data flow of intelligent multi-agent client server is realized by using IIR filter.The time domain component of load state of intelligent multi-agent client server is extracted by Lyapunov exponent spectrum feature,and the trend of load state is automatically detected according to the rule of Lyapunov exponent in spectrum diagram.The algorithm of intelligent multi-agent client-server load state automatic detection is improved.The simulation results show that the proposed algorithm has the advantages of good automaticity,high directivity and strong anti-interference ability for intelligent multi-agent client/server load state detection.It has good application in load management and scheduling of multi-agent client and server.
引文
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[3] 谢胜利,孙功宪,肖明,等.欠定和非完全稀疏性的盲信号提取[J].电子学报,2010,38(5):1028-1031.
[4] ZHAO Wei,HUANG Xiao-jing,YOU Rong-yi.Removal of White Noise from ECG Signal Based on Morphological Component Analysis.Chinese journal of biomedical engineering,2014,23(1):1-6
[5] ZHENG Yuan-zhuang,YOU Rong-yi.Wavelet Variance Analysis of EEG Based on Window Function.Chinese journal of biomedical engineering,2014,23(2):54-59.
[6] Lü Xinzheng,LIU Hezhou,and WANG Qizhi.A new technology of pulse sorting based on paired localization[J].Aerospace Electronic Warfare,2017,33(3):47-49.
[7] 张永,李卓然,刘小丹.基于主动学习SMOTE的非均衡数据分类[J].计算机应用与软件,2012,29(3):91-93.
[8] JU C H,ZOU J B.An incremental classification algorithm for data stream based on information entropy diversity measure[J].Telecommunications Science,2015,31(2):86-96.
[9] LYU Y X,WANG C Y,WANG C,et al.Online classification algorithm for uncertain data stream in big data[J].Journal of Northeastern University(Natural Science Edition),2016,37(9):1245-1249
[10] CHEN Y,LI L J.Very fast decision tree classification algorithm based on red-black tree for data stream with continuous attributes[J].Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition),2017,37(2):86-90.
[11] 关兆雄,庞维欣.基于在线迁移的虚拟化资源整合研究[J].自动化与仪器仪表,2018,3:59-62.
[12] 赵晶,虞志刚,冯旭,陆洲.无线传感器网络多路径传输时延优化调度算法研究[J].中国电子科学研究院学报,2018,13(3):264-271.
[13] 刘畅,蔡旭,陈强.具有储能功能的链式STATCOM研究[J].电源学报,2018,16(4):21-27.
[14] FERCOQ O,RICHTáRIK P.Accelerated,parallel and proximal coordinate descent[J].SIAM Journal on Optimization,2014,25(4):1997-2023.
[15] LOW Y,BICKSON D,GONZALEZ J,et al.Distributed GraphLab:a framework for machine learning and data mining in the cloud[J].Proceedings of the VLDB Endowment,2012,5(8):716-727.
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