基于多尺度量子谐振子算法的相空间概率聚类算法
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摘要
针对大型集群难以进行任务调度和资源分配的问题,提出一种基于多尺度量子谐振子算法的相空间概率聚类算法(PSPCA-MQHOA)。首先,将集群工作状态投影到相空间中,把复杂的集群工作状态转化为相空间中的点集;进而,将相空间网格化,形成多尺度量子谐振子算法(MQHOA)以处理离散目标函数;最后,利用MQHOA优化过程中波函数变化的概率解释对集群节点进行概率聚类。PSPCA-MQHOA继承了MQHOA物理模型明确、搜索能力强、结果精确等优点,并且由于以相空间作为离散化的目标函数,迭代次数大大减少。实验结果表明PSPCA-MQHOA能适用于多种负载状态的集群。
A Phase Space Probabilistic Clustering Algorithm based on Multi-scale Quantum Harmonic Oscillator Algorithm( PSPCA-MQHOA) was proposed to solve the task scheduling and resource allocation of large clusters. Firstly, the cluster operating status was projected into the phase space, and the complex working state was transformed into the point set in the phase space. Furthermore, the phase space was meshed to form the Multi-scale Quantum Harmonic Oscillator Algorithm( MQHOA) for discrete objective function. Finally, probabilistic clustering of cluster nodes was carried out by using the probability interpretation of wave function in the MQHOA process. PSPCA-MQHOA inherits the advantages of MQHOA, such as explicit physical model, strong search capabilities and accurate results, and it has few iterations due to the discretized phase space. Experimental results show that PSPCA-MQHOA can be applied to clusters in a variety of load conditions.
A Phase Space Probabilistic Clustering Algorithm based on Multi-scale Quantum Harmonic Oscillator Algorithm( PSPCA-MQHOA) was proposed to solve the task scheduling and resource allocation of large clusters. Firstly, the cluster operating status was projected into the phase space, and the complex working state was transformed into the point set in the phase space. Furthermore, the phase space was meshed to form the Multi-scale Quantum Harmonic Oscillator Algorithm( MQHOA) for discrete objective function. Finally, probabilistic clustering of cluster nodes was carried out by using the probability interpretation of wave function in the MQHOA process. PSPCA-MQHOA inherits the advantages of MQHOA, such as explicit physical model, strong search capabilities and accurate results, and it has few iterations due to the discretized phase space. Experimental results show that PSPCA-MQHOA can be applied to clusters in a variety of load conditions.
引文
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[3]华夏渝,郑骏,胡文心.基于云计算环境的蚁群优化计算资源分配算法[J].华东师范大学学报(自然科学版),2010(1):127-134.(HUA X Y,ZHENG J,HU W X.Ant colony optimization algorithm for computing resource allocation based on cloud computing environment[J].Journal of East China Normal University(Natural Science),2010(1):127-134.)
[4]ERGU D,KOU G,PENG Y,et al.The analytic hierarchy process:task scheduling and resource allocation in cloud computing environment[J].The Journal of Supercomputing,2013,64(3):835-848.
[5]刘伯成,陈庆奎.云计算中的集群资源模糊聚类划分模型[J].计算机科学,2011,38(10A):157-160,168.(LIU B C,CHEN QK.Fuzzy clustering partition model for computer cluster in cloud computing[J].Computer Science,2011,38(10A):157-160,168.)
[6]姚婧,何聚厚.基于模糊聚类分析的云计算负载平衡策略[J].计算机应用,2012,32(1):213-217.(YAO J,HE J H.Load balance strategy of cloud computing based on fuzzy clustering analysis[J].Journal of Computer Applications,2012,32(1):213-217.)
[7]WITTEN I H,FRANK E,HALL M A.Data Mining:Practical Machine Learning Tools and Techniques[M].3rd ed.San Francisco,CA:Morgan Kaufmann Publishers Inc.,2011:285-287.
[8]MADDAH M,WELLS W M,Ⅲ,WARFIELD S K,et al.Probabilistic clustering and quantitative analysis of white matter fiber tracts[C]//IPMI 2007:Proceedings of the 20th International Conference on Information Processing in Medical Imaging,LNCS 4584.Berlin:Springer-Verlag,2007:372-383.
[9]VOGT J E,KLOFT M,STARK S,et al.Probabilistic clustering of time-evolving distance data[J].Machine Learning,2015,100(2/3):635-654.
[10]LU Z,LEEN T K.Penalized probabilistic clustering[J].Neural Computation,2007,19(6):1528-1567.
[11]王鹏.云计算系统相空间广义热力学参数定义及分析[J].计算机应用,2012,32(8):2172-2175.(WANG P.Definitions and analysis of general thermodynamic parameters in cloud computing phase space[J].Journal of Computer Applications,2012,32(8):2172-2175.)
[12]王鹏,黄焱,任超,等.多尺度量子谐振子高维函数全局优化算法[J].电子学报,2013,41(12):2468-2473.(WANG P,HUANG Y,REN C,et al.Multi-scale quantum harmonic oscillator for high-dimensional function global optimization algorithm[J].Acta Electronica Sinica,2013,41(12):2468-2473.)
[13]燕京京,王鹏,范家兵,等.基于量子谐振子模型的聚类中心选取算法[J].电子学报,2016,44(2):405-412.(YAN J J,WANG P,FAN J B,et al.Clustering center selecting algorithm based on quantum harmonic oscillator model[J].Acta Electronica Sinica,2016,44(2):405-412.)
[14]张磊,王鹏,黄焱,等.基于相空间的云计算仿真系统研究与设计[J].计算机科学,2013,40(2):84-86.(ZHANG L,WANGP,HUANG Y,et al.Research and design of cloud computing simulation system based on phase space[J].Computer Science,2013,40(2):84-86.)
[15]郭又铭,王鹏,唐华,等.基于相空间的云计算专用监控系统[J].计算机工程,2013,39(7):40-44.(GUO Y M,WANG P,TANG H,et al.Specialized cloud computing monitoring system based on phase space[J].Computer Engineering,2013,39(7):40-44.)
[16]王鹏,黄焱,李坤,等.云计算集群相空间负载均衡度优先调度算法研究[J].计算机研究与发展,2014,51(5):1095-1107.(WANG P,HUANG Y,LI K,et al.Load balancing degree first algorithm on phase space for cloud computing cluster[J].Journal of Computer Research andt Development,2014,51(5):1095-1107.)
[17]王鹏,黄焱.多尺度量子谐振子优化算法物理模型[J].计算机科学与探索,2015,9(10):1271-1280.(WANG P,HUANG Y.Physical model of multi-scale quantum harmonic oscillator optimization algorithm[J].Journal of Frontiers of Computer Science and Technology,2015,9(10):1271-1280.)