Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced fe...Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced features of cloud computing, such as SaaS, PaaS and IaaS service models. So, nowadays, many organizations are trying to implement Cloud Computing based ERP system to enjoy the benefits of cloud computing. To implement any ERP system, an organization usually faces many challenges. As a result, this research has introduced how easily this cloud system can be implemented in an organization. By using this ERP system, an organization can be benefited in many ways;especially Small and Medium Enterprises (SMEs) can enjoy the highest possible benefits from this system.展开更多
Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational pro...Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.展开更多
Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduct...Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.展开更多
针对传统多尺度模型对模型点云比较方法(Multiscale Model to Model Cloud Comparison,M3C2)计算法向量与形变量时易受离群点影响的缺点,提出一种基于离群点探测准则的改进算法。首先,在估计关键点法向量时,依据改进离群点探测准则迭代...针对传统多尺度模型对模型点云比较方法(Multiscale Model to Model Cloud Comparison,M3C2)计算法向量与形变量时易受离群点影响的缺点,提出一种基于离群点探测准则的改进算法。首先,在估计关键点法向量时,依据改进离群点探测准则迭代剔除离群点,提高法向量估计的准确性,然后,通过离群点探测剔除圆柱内离群点,最后,结合正态分布加权计算形变量。实验结果表明,相较于M3C2原始算法,改进算法将法向量均方差精度指标提升50%以上,在形变量较大区域可将形变量估值均方差精度指标提高200%以上。改进算法具有更好的适用性和可靠性。展开更多
文摘Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced features of cloud computing, such as SaaS, PaaS and IaaS service models. So, nowadays, many organizations are trying to implement Cloud Computing based ERP system to enjoy the benefits of cloud computing. To implement any ERP system, an organization usually faces many challenges. As a result, this research has introduced how easily this cloud system can be implemented in an organization. By using this ERP system, an organization can be benefited in many ways;especially Small and Medium Enterprises (SMEs) can enjoy the highest possible benefits from this system.
基金sponsored by the NWO/TTW project Multi-scale integrated Trafc Observatory for Large Road Networks(MiRRORS)under Grant Number 16270.
文摘Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.
基金Supported by the National Key Research and Development Program(2017YFA0603503)National Natural Science Foundation of China(41605057 and 41661144009)
文摘Climate sensitivity and feedbacks are basic and important metrics to a climate system. They determine how large surface air temperature will increase under CO_2 forcing ultimately, which is essential for carbon reduction policies to achieve a specific warming target. In this study, these metrics are analyzed in a climate system model newly developed by the Chinese Academy of Meteorological Sciences(CAMS-CSM) and compared with multi-model results from the Coupled Model Comparison Project phase 5(CMIP5). Based on two idealized CO_2 forcing scenarios, i.e.,abruptly quadrupled CO_2 and CO_2 increasing 1% per year, the equilibrium climate sensitivity(ECS) and transient climate response(TCR) in CAMS-CSM are estimated to be about 2.27 and 1.88 K, respectively. The ECS is near the lower bound of CMIP5 models whereas the TCR is closer to the multi-model ensemble mean(MME) of CMIP5 due to compensation of a relatively low ocean heat uptake(OHU) efficiency. The low ECS is caused by an unusually negative climate feedback in CAMS-CSM, which is attributed to cloud shortwave feedback(λSWCL) over the tropical Indo-Pacific Ocean.The CMIP5 ensemble shows that more negative λSWCL is related to larger increase in low-level(925–700 hPa)cloud over the tropical Indo-Pacific under warming, which can explain about 90% of λSWCL in CAMS-CSM. Static stability of planetary boundary layer in the pre-industrial simulation is a critical factor controlling the low-cloud response and λSWCL across the CMIP5 models and CAMS-CSM. Evidently, weak stability in CAMS-CSM favors lowcloud formation under warming due to increased low-level convergence and relative humidity, with the help of enhanced evaporation from the warming tropical Pacific. Consequently, cloud liquid water increases, amplifying cloud albedo, and eventually contributing to the unusually negative λSWCL and low ECS in CAMS-CSM. Moreover, the OHU may influence climate feedbacks and then the ECS by modulating regional sea surface temperature responses.
文摘针对传统多尺度模型对模型点云比较方法(Multiscale Model to Model Cloud Comparison,M3C2)计算法向量与形变量时易受离群点影响的缺点,提出一种基于离群点探测准则的改进算法。首先,在估计关键点法向量时,依据改进离群点探测准则迭代剔除离群点,提高法向量估计的准确性,然后,通过离群点探测剔除圆柱内离群点,最后,结合正态分布加权计算形变量。实验结果表明,相较于M3C2原始算法,改进算法将法向量均方差精度指标提升50%以上,在形变量较大区域可将形变量估值均方差精度指标提高200%以上。改进算法具有更好的适用性和可靠性。
