The cloud service level agreement(SLA)manage the relationship between service providers and consumers in cloud computing.SLA is an integral and critical part of modern era IT vendors and communication contracts.Due to...The cloud service level agreement(SLA)manage the relationship between service providers and consumers in cloud computing.SLA is an integral and critical part of modern era IT vendors and communication contracts.Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers,the SLA emerges as a key aspect between the consumers and providers.Continuous monitoring of Quality of Service(QoS)attributes is required to implement SLAs because of the complex nature of cloud communication.Many other factors,such as user reliability,satisfaction,and penalty on violations are also taken into account.Currently,there is no such policy of cloud SLA monitoring to minimize SLA violations.In this work,we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts,for critical and non-critical parameters.The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation.This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach(RPAA)which will monitor SLA at runtime,analyze the SLA parameters and try to find the possibility of SLA violations.We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection.We have defined two main components of SLA-PRAA i.e.,(a)Handler and(b)Accounting and Billing Manager.We have also described the function of both components through algorithms.The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.展开更多
With the growing environmental concerns, green supply chain management （GSCM） is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the par...With the growing environmental concerns, green supply chain management （GSCM） is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the participating members along a supply chain is important to GSCM. The GreenSCOR model developed by the Supply Chain Council provides a generic framework for measuring the total carbon footprint and environmental footprint in a supply chain. The model is based on the Supply Chain Operations Reference （SCOR） model, which represents a supply chain network in a hierarchically structured manner. This paper describes the GreenSCOR framework and its potential application to the construction industry. This paper also presents a web services approach to incorporate the GreenSCOR model to the implementation of collaborative information systems. Each process element in the SCOR model is represented and delivered as individual web service units, which can be reused and integrated using standard web services technologies. The service units are combined and managed in a prototype web service collaborative framework, called SC Collaborator, which is designed and developed for supporting construction supply chain management. An illustrative example is presented to demonstrate the implementation of the GreenSCOR-based SC Collaborator framework.展开更多
With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key te...With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.展开更多
Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people i...Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.展开更多
Cooperative financing, with existing problems involving legal person man-agement and property supervision is highly demanded by farmers in China. It is fea-sible to explore a shareholding system to resolve the managem...Cooperative financing, with existing problems involving legal person man-agement and property supervision is highly demanded by farmers in China. It is fea-sible to explore a shareholding system to resolve the management mode issue of rural credit cooperatives in order to introduce investments, formulate the right struc-ture for rational stock and to establish effective monitoring mechanism for property right. Hence, information issuing would be reinforced and a rural credit cooperative would be established to be a modern financial enterprise with transparent property rights.展开更多
文摘The cloud service level agreement(SLA)manage the relationship between service providers and consumers in cloud computing.SLA is an integral and critical part of modern era IT vendors and communication contracts.Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers,the SLA emerges as a key aspect between the consumers and providers.Continuous monitoring of Quality of Service(QoS)attributes is required to implement SLAs because of the complex nature of cloud communication.Many other factors,such as user reliability,satisfaction,and penalty on violations are also taken into account.Currently,there is no such policy of cloud SLA monitoring to minimize SLA violations.In this work,we have proposed a cloud SLA monitoring policy by dividing a monitoring session into two parts,for critical and non-critical parameters.The critical and non-critical parameters will be decided on the interest of the consumer during SLA negotiation.This will help to shape a new comprehensive SLA based Proactive Resource Allocation Approach(RPAA)which will monitor SLA at runtime,analyze the SLA parameters and try to find the possibility of SLA violations.We also have implemented an adaptive system for allocating cloud IT resources based on SLA violations and detection.We have defined two main components of SLA-PRAA i.e.,(a)Handler and(b)Accounting and Billing Manager.We have also described the function of both components through algorithms.The experimental results validate the performance of our proposed method in comparison with state-of-the-art cloud SLA policies.
文摘With the growing environmental concerns, green supply chain management （GSCM） is gaining significant attention in the construction industry. Tracking and monitoring the environmental effects brought forth by the participating members along a supply chain is important to GSCM. The GreenSCOR model developed by the Supply Chain Council provides a generic framework for measuring the total carbon footprint and environmental footprint in a supply chain. The model is based on the Supply Chain Operations Reference （SCOR） model, which represents a supply chain network in a hierarchically structured manner. This paper describes the GreenSCOR framework and its potential application to the construction industry. This paper also presents a web services approach to incorporate the GreenSCOR model to the implementation of collaborative information systems. Each process element in the SCOR model is represented and delivered as individual web service units, which can be reused and integrated using standard web services technologies. The service units are combined and managed in a prototype web service collaborative framework, called SC Collaborator, which is designed and developed for supporting construction supply chain management. An illustrative example is presented to demonstrate the implementation of the GreenSCOR-based SC Collaborator framework.
基金supported by National Natural Science Foundation of China under Grants No.62076249,62022092,62293545.
文摘With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.
基金Supported by the National Key Research and Development Program of China(Nos.2021YFC3101801,2023YFC3008200)the National Natural Science Foundation of China(Nos.42476219,41976200)+6 种基金the National Foreign Experts Program(No.S20240134)the Innovative Team Plan of the Department of Education of Guangdong Province(No.2023KCXTD015)the Tropical Ocean Environment in Western Coastal Waters Observation and Research Station of Guangdong Province(No.2024B1212040008)the Independent Research Project of the Southern Ocean Laboratory(No.SML2022SP301)the Shandong Innovation and Development Research Institute Think Tank Projectthe Guangdong Ocean University Scientific Research Program(No.060302032106)the Start-up Fund for Ph D Researchers(No.060302032104)。
文摘Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.
基金Supported by Key Research Topic by Anhui Administrative College(YJKT0910ZD02)~~
文摘Cooperative financing, with existing problems involving legal person man-agement and property supervision is highly demanded by farmers in China. It is fea-sible to explore a shareholding system to resolve the management mode issue of rural credit cooperatives in order to introduce investments, formulate the right struc-ture for rational stock and to establish effective monitoring mechanism for property right. Hence, information issuing would be reinforced and a rural credit cooperative would be established to be a modern financial enterprise with transparent property rights.
