MapReduce is a popular program- ming model for processing large-scale datasets in a distributed environment and is a funda- mental component of current cloud comput- ing and big data applications. In this paper, a hea...MapReduce is a popular program- ming model for processing large-scale datasets in a distributed environment and is a funda- mental component of current cloud comput- ing and big data applications. In this paper, a heartbeat mechanism for MapReduce Task Scheduler using Dynamic Calibration （HMTS- DC） is proposed to address the unbalanced node computation capacity problem in a het- erogeneous MapReduce environment. HMTS- DC uses two mechanisms to dynamically adapt and balance tasks assigned to each com- pute node： 1） using heartbeat to dynamically estimate the capacity of the compute nodes, and 2） using data locality of replicated data blocks to reduce data transfer between nodes. With the first mechanism, based on the heart- beats received during the early state of the job, the task scheduler can dynamically estimate the computational capacity of each node. Us- ing the second mechanism, unprocessed Tasks local to each compute node are reassigned and reserved to allow nodes with greater capacities to reserve more local tasks than their weaker counterparts. Experimental results show that HMTS-DC performs better than Hadoop and Dynamic Data Placement Strategy （DDP） in a dynamic environment. Furthermore, an en- hanced HMTS-DC （EHMTS-DC） is proposed bv incorporatin historical data. In contrastto the ＂slow start＂ property of HMTS-DC, EHMTS-DC relies on the historical computation capacity of the slave machines. The experimental results show that EHMTS-DC outperforms HMTS-DC in a dynamic environment.展开更多
The aim of this study was to develop and explore a stochastic lattice gas cellular automata （LGCA） model for epidemics. A computer program was development in order to implement the model. An irregular grid of cells ...The aim of this study was to develop and explore a stochastic lattice gas cellular automata （LGCA） model for epidemics. A computer program was development in order to implement the model. An irregular grid of cells was used. A susceptible-infected-recovered （SIR） scheme was represented. Stochasticity was generated by Monte Carlo method. Dynamics of model was explored by numerical simulations. Model achieves to represent the typical SIR prevalence curve. Performed simulations also show how infection, mobility and distribution of infected individuals may influence the dynamics of propagation. This simple theoretical model might be a basis for developing more realistic designs.展开更多
The genetic models are greatly important in the analysis of genetic epidemiologic studies and many of the studies are conducted using the trend test under the additive model. However, for many complex diseases and tra...The genetic models are greatly important in the analysis of genetic epidemiologic studies and many of the studies are conducted using the trend test under the additive model. However, for many complex diseases and traits, the underlying genetic model for a genetic locus is usually uncertain. So a robust test free of genetic model is appropriate. In this paper, the authors propose a model-embedded trend test by incorporating Hardy-Weinberg equilibrium information and obtain the explicit formula to calculate its statistical significance. Extensive simulation studies show the proposed test is more robust than the existing procedures. Finally, a real application is further analyzed to show the performance of the proposed test.展开更多
In this paper, a susceptible-exposed infective-recovered-susceptible （SEIRS） epidemic model with vaccination has been formulated. We studied the global stability of the corresponding single-group model, multi-group ...In this paper, a susceptible-exposed infective-recovered-susceptible （SEIRS） epidemic model with vaccination has been formulated. We studied the global stability of the corresponding single-group model, multi-group model with strongly connected network and multi-group model without strongly connected network by means of analyzing their basic reproduction numbers and the application of Lyapunov functionals. Finally, we provide some numerical simulations to illustrate our analysis results.展开更多
文摘MapReduce is a popular program- ming model for processing large-scale datasets in a distributed environment and is a funda- mental component of current cloud comput- ing and big data applications. In this paper, a heartbeat mechanism for MapReduce Task Scheduler using Dynamic Calibration （HMTS- DC） is proposed to address the unbalanced node computation capacity problem in a het- erogeneous MapReduce environment. HMTS- DC uses two mechanisms to dynamically adapt and balance tasks assigned to each com- pute node： 1） using heartbeat to dynamically estimate the capacity of the compute nodes, and 2） using data locality of replicated data blocks to reduce data transfer between nodes. With the first mechanism, based on the heart- beats received during the early state of the job, the task scheduler can dynamically estimate the computational capacity of each node. Us- ing the second mechanism, unprocessed Tasks local to each compute node are reassigned and reserved to allow nodes with greater capacities to reserve more local tasks than their weaker counterparts. Experimental results show that HMTS-DC performs better than Hadoop and Dynamic Data Placement Strategy （DDP） in a dynamic environment. Furthermore, an en- hanced HMTS-DC （EHMTS-DC） is proposed bv incorporatin historical data. In contrastto the ＂slow start＂ property of HMTS-DC, EHMTS-DC relies on the historical computation capacity of the slave machines. The experimental results show that EHMTS-DC outperforms HMTS-DC in a dynamic environment.
文摘The aim of this study was to develop and explore a stochastic lattice gas cellular automata （LGCA） model for epidemics. A computer program was development in order to implement the model. An irregular grid of cells was used. A susceptible-infected-recovered （SIR） scheme was represented. Stochasticity was generated by Monte Carlo method. Dynamics of model was explored by numerical simulations. Model achieves to represent the typical SIR prevalence curve. Performed simulations also show how infection, mobility and distribution of infected individuals may influence the dynamics of propagation. This simple theoretical model might be a basis for developing more realistic designs.
基金partial supported by Special National Key Research and Development Plan under Grant No.2016YFD0400206the Breakthrough Project of Strategic Priority Program of Chinese Academy of Sciences under Grant No.XDB13040600+2 种基金Youth Innovation Promotion Association of Chinese Academy of Sciencethe National Science Foundation of China under Grant Nos.11371353,11661080,61134013Special Fund of the University of Chinese Academy of Sciences for Scientific Research Cooperation
文摘The genetic models are greatly important in the analysis of genetic epidemiologic studies and many of the studies are conducted using the trend test under the additive model. However, for many complex diseases and traits, the underlying genetic model for a genetic locus is usually uncertain. So a robust test free of genetic model is appropriate. In this paper, the authors propose a model-embedded trend test by incorporating Hardy-Weinberg equilibrium information and obtain the explicit formula to calculate its statistical significance. Extensive simulation studies show the proposed test is more robust than the existing procedures. Finally, a real application is further analyzed to show the performance of the proposed test.
基金This research is supported by National Natural Science Foundation of China （No. 11371111）, Weihai Science and Technology Development Plan Project （No. 2013DXGJ06） and Shandong Provincial Natural Science Foundation of China （No. ZR2015AM018）.
文摘In this paper, a susceptible-exposed infective-recovered-susceptible （SEIRS） epidemic model with vaccination has been formulated. We studied the global stability of the corresponding single-group model, multi-group model with strongly connected network and multi-group model without strongly connected network by means of analyzing their basic reproduction numbers and the application of Lyapunov functionals. Finally, we provide some numerical simulations to illustrate our analysis results.
