[Objective]The paper was to preliminarily study and discus the biological characteristics of Haobroloma subbicorne.[Method]The bio-logical habits of H.subbicorne aduls were observed by indoor observation and field obs...[Objective]The paper was to preliminarily study and discus the biological characteristics of Haobroloma subbicorne.[Method]The bio-logical habits of H.subbicorne aduls were observed by indoor observation and field observation.Resultl H.subbicorne aduls,with characters of polyphagy and feign death,liked young leaves and often fed from the edge of a leaf,and preferred warm and siuny environment.Aduts mated,for-aged,flied,and walked during the day.H.subbicorme adults were the most active from 13:00 pr to 14:00 pm during the day.[Condusion]The study will provide a theoretical baais for further research and scientifie prevention and control of H.subbicome.展开更多
A virtual machine placement optimization model based on optimized ant colony algorithm is proposed.The model is able to determine the physical machines suitable for hosting migrated virtual machines.Thus,it solves the...A virtual machine placement optimization model based on optimized ant colony algorithm is proposed.The model is able to determine the physical machines suitable for hosting migrated virtual machines.Thus,it solves the problem of redundant power consumption resulting from idle resource waste of physical machines.First,based on the utilization parameters of the virtual machine,idle resources and energy consumption models are proposed.The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines.Next,a multi-objective optimization strategy is derived for virtual machine placement in cloud environments.Finally,an optimal virtual machines placement scheme is determined based on feature metrics,multi-objective optimization,and the ant colony algorithm.Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model,the convergence rate is increased by 16%,and the optimized highest average energy consumption is reduced by 18%.The model exhibits advantages in terms of algorithm efficiency and efficacy.展开更多
文摘[Objective]The paper was to preliminarily study and discus the biological characteristics of Haobroloma subbicorne.[Method]The bio-logical habits of H.subbicorne aduls were observed by indoor observation and field observation.Resultl H.subbicorne aduls,with characters of polyphagy and feign death,liked young leaves and often fed from the edge of a leaf,and preferred warm and siuny environment.Aduts mated,for-aged,flied,and walked during the day.H.subbicorme adults were the most active from 13:00 pr to 14:00 pm during the day.[Condusion]The study will provide a theoretical baais for further research and scientifie prevention and control of H.subbicome.
基金This paper is supported by the National Natural Science Founds of China(No.61602376)the Natural Science Research Project of Shaanxi Education Department(Nos.16JK1573,112-431016021)+1 种基金the Ph.D.Research Startup Funds of Xi’an University of Technology(Nos.112-256081504,112-451115002,112-451116015)Research on the training mechanism of computer application ability of non computer majors in Petroleum Universities(No.SGH140627).
文摘A virtual machine placement optimization model based on optimized ant colony algorithm is proposed.The model is able to determine the physical machines suitable for hosting migrated virtual machines.Thus,it solves the problem of redundant power consumption resulting from idle resource waste of physical machines.First,based on the utilization parameters of the virtual machine,idle resources and energy consumption models are proposed.The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines.Next,a multi-objective optimization strategy is derived for virtual machine placement in cloud environments.Finally,an optimal virtual machines placement scheme is determined based on feature metrics,multi-objective optimization,and the ant colony algorithm.Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model,the convergence rate is increased by 16%,and the optimized highest average energy consumption is reduced by 18%.The model exhibits advantages in terms of algorithm efficiency and efficacy.
