The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In clo...The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.展开更多
According to the connotation and structure of science and technology resources and some relevant data of more than 286 cities at prefecture level and above during 2001-2010, using modified method--Data Envelopment Ana...According to the connotation and structure of science and technology resources and some relevant data of more than 286 cities at prefecture level and above during 2001-2010, using modified method--Data Envelopment Analysis （DEA）, science and tech- nology （S＆T） resource allocation efficiency of different cities in different periods has been figured out, which, uncovers the distributional difference and change law of S＆T resource allocation efficiency from the time-space dimension. Based on that, this paper has analyzed and discussed the spatial distribution pattern and evolution trend of S＆T resource allocation efficiency in different cities by virtue of the Exploratory Spatial Data Analysis （ESDA）. It turned out that： （1） the average of S＆T resource allocation efficiency in cities at prefecture level and above has always stayed at low levels, moreover, with repeated fluctuations between high and low, which shows a decreasing trend year by year. Besides, the gap between the East and the West is widening. （2） The asymmetrical distribution of S＆T resource allocation effi- ciency presents a spatial pattern of successively decreasing from Eastern China, Central China to Western China. The cities whose S＆T resource allocation efficiency are at higher level and high level take on a cluster distribution, which fits well with the 23 forming urban agglomerations in China. （3） The coupling degree between S＆T resource allocation efficiency and economic environment assumes a certain positive correlation, but not completely the same. The differentiation of S＆T resource allocation efficiency is common in regional devel- opment, whose existence and evolution are directly or indirectly influenced by and regarded as the reflection of many elements, such as geographical location, the natural endowment and environment of S＆T resources and so on. （4） In the perspective of the evolution of spatial structure, S＆T resource allocation efficiency of the cities at prefecture level and above shows a notable spatial autocorrelation, which in every period presents a positive correlation. The spatial distribution of S＆T resource allocation efficiency in neighboring cities seems to be similar in group, which tends to escalate stepwise. Meanwhile, the whole differentiation of geographical space has a diminishing tendency. （5） Viewed from LISA agglomeration map of S＆T resource allocation efficiency in different periods, four agglomeration types have changed differently in spatial location and the range of spatial agglomeration. And the conti- nuity of S＆T resource allocation efficiency in geographical space is gradually increasing.展开更多
As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while pro...As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while providing opportunities for these firms to take the path of independent transformation.Employing the data of Chinese listed enterprises from 2006 to 2016 and adopting a difference-in-differences(DID)model,we evaluated the impact of LCC construction on the upgrading of HCEEs and its mechanisms.The results indicate that LCC construction enhances the upgrading of HCEEs in the pilot cities.The conclusions remain stable after a series of robustness tests.The mechanism analysis reveals that LCC construction triggers the upgrading of HCEEs by promoting resource allocation efficiency,R&D investment,and green technology innovation.The heterogeneity results indicate that this positive effect is more pronounced for HCEEs in regions with more stringent environmental law enforcement.This study also observes that the upgrading impact is more promi‐nent for state-owned enterprises,enterprises with higher bargaining power,and enterprises whose managers have a long-term vision.The above results provide directions for upgrading HCEEs and replicable evidence for cities in developing economies to fulfill the win-win target of environmental protection and economic transfor‐mation.展开更多
文摘The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.
基金Key Projects of Philosophy of the Social Science funded by the Ministry of Education,No.11JD039National Key Public Bidding Project for Soft Science Research Plan,No.2012GXS1D002National Natural Science Foundation of China,No.41001083
文摘According to the connotation and structure of science and technology resources and some relevant data of more than 286 cities at prefecture level and above during 2001-2010, using modified method--Data Envelopment Analysis （DEA）, science and tech- nology （S＆T） resource allocation efficiency of different cities in different periods has been figured out, which, uncovers the distributional difference and change law of S＆T resource allocation efficiency from the time-space dimension. Based on that, this paper has analyzed and discussed the spatial distribution pattern and evolution trend of S＆T resource allocation efficiency in different cities by virtue of the Exploratory Spatial Data Analysis （ESDA）. It turned out that： （1） the average of S＆T resource allocation efficiency in cities at prefecture level and above has always stayed at low levels, moreover, with repeated fluctuations between high and low, which shows a decreasing trend year by year. Besides, the gap between the East and the West is widening. （2） The asymmetrical distribution of S＆T resource allocation effi- ciency presents a spatial pattern of successively decreasing from Eastern China, Central China to Western China. The cities whose S＆T resource allocation efficiency are at higher level and high level take on a cluster distribution, which fits well with the 23 forming urban agglomerations in China. （3） The coupling degree between S＆T resource allocation efficiency and economic environment assumes a certain positive correlation, but not completely the same. The differentiation of S＆T resource allocation efficiency is common in regional devel- opment, whose existence and evolution are directly or indirectly influenced by and regarded as the reflection of many elements, such as geographical location, the natural endowment and environment of S＆T resources and so on. （4） In the perspective of the evolution of spatial structure, S＆T resource allocation efficiency of the cities at prefecture level and above shows a notable spatial autocorrelation, which in every period presents a positive correlation. The spatial distribution of S＆T resource allocation efficiency in neighboring cities seems to be similar in group, which tends to escalate stepwise. Meanwhile, the whole differentiation of geographical space has a diminishing tendency. （5） Viewed from LISA agglomeration map of S＆T resource allocation efficiency in different periods, four agglomeration types have changed differently in spatial location and the range of spatial agglomeration. And the conti- nuity of S＆T resource allocation efficiency in geographical space is gradually increasing.
基金This paper was supported by the Fundamental Research Funds for the Central Universities[Grant number:JBK2202018].
文摘As a crucial environmental reform system to realize“carbon peaking”and“carbon neutrality”,the pilot policy of low-carbon cities(LCCs)puts pressure and challenges on high-carbon emitting enterprises(HCEEs)while providing opportunities for these firms to take the path of independent transformation.Employing the data of Chinese listed enterprises from 2006 to 2016 and adopting a difference-in-differences(DID)model,we evaluated the impact of LCC construction on the upgrading of HCEEs and its mechanisms.The results indicate that LCC construction enhances the upgrading of HCEEs in the pilot cities.The conclusions remain stable after a series of robustness tests.The mechanism analysis reveals that LCC construction triggers the upgrading of HCEEs by promoting resource allocation efficiency,R&D investment,and green technology innovation.The heterogeneity results indicate that this positive effect is more pronounced for HCEEs in regions with more stringent environmental law enforcement.This study also observes that the upgrading impact is more promi‐nent for state-owned enterprises,enterprises with higher bargaining power,and enterprises whose managers have a long-term vision.The above results provide directions for upgrading HCEEs and replicable evidence for cities in developing economies to fulfill the win-win target of environmental protection and economic transfor‐mation.
