Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate des...Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.展开更多
Deep geothermal resources mainly refer to the thermal energy stored in subsurface rocks and fluids therein at a depth of 3-10 km,which is a kind of renewable and sustainable clean energy unaffected by weather and seas...Deep geothermal resources mainly refer to the thermal energy stored in subsurface rocks and fluids therein at a depth of 3-10 km,which is a kind of renewable and sustainable clean energy unaffected by weather and seasonal changes.Large scale exploitation of the deep geothermal resources is of great significance to ensuring national energy security and achieving the“Carbon Peak and Carbon Neutrality”.Based on the latest terrestrial heat flow data,this paper estimated the potential of deep geothermal resources in the terrestrial areas of China,and the results show that the total amount of geothermal resources within 3e10 km under the Earth's surface in the terrestrial areas of China is 24.6×10^(15)GJ.In line with climate zones categorized,the geothermal resource proportion is 43.81%for severe cold regions,29.19%for cold regions,6.92%for mild regions,13.82%for hot summer and cold winter regions,and 6.26%for hot summer and warm winter regions.Statistics according to the burial depth range reveal that the resources within depth ranges of 3-5 km,5-7 km and 7-10 km under the Earth's surface are 4.3119×10^(15)GJ,6.37674×10^(15)GJ and 13.89594×10^(15)GJ respectively,showing an increasing trend of geothermal potential with increasing burial depth.The deep geothermal resources are mainly of medium-to-high temperature reserves,and the energy supply strategy can be optimized by combining the climate conditions and population distribution,as well as considering power generation.In regions of cold or severe cold climate,the geothermal resources may be applied to geothermal power generation and district heating in combination;in regions of hot summer and cold winter or mild climates,the resources can be used for geothermal power generation combined with cooling and heating;in regions of hot summer and warm winter climates,the resources may be applied to geothermal power generation combined with cooling and industrial and agricultural utilization.Exploitation of deep geothermal resources also can be combined with carbon dioxide sequestration,multi-mineral resources extraction and energy storage to realize comprehensive exploitation and utilization of various energy resources.It is suggested that theoretical technology research should be combined with pilot tests and field demonstrations,and large-scale economic exploitation of deep geothermal resources should be arranged in a coordinated manner,following the principles of overall planning and step-by-step implementation.展开更多
Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle w...Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods.展开更多
The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network...The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.展开更多
基金This work was supported in part by the Natural Science Foundation of the Education Department of Henan Province(Grant 22A520025)the National Natural Science Foundation of China(Grant 61975053)the National Key Research and Development of Quality Information Control Technology for Multi-Modal Grain Transportation Efficient Connection(2022YFD2100202).
文摘Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.
基金supported by the Joint Petrochemical Fund project of National Natural Science Foundation of China”Deep Geological Processes and Resource Effects in the Basin”(Fund No.U20B6001).
文摘Deep geothermal resources mainly refer to the thermal energy stored in subsurface rocks and fluids therein at a depth of 3-10 km,which is a kind of renewable and sustainable clean energy unaffected by weather and seasonal changes.Large scale exploitation of the deep geothermal resources is of great significance to ensuring national energy security and achieving the“Carbon Peak and Carbon Neutrality”.Based on the latest terrestrial heat flow data,this paper estimated the potential of deep geothermal resources in the terrestrial areas of China,and the results show that the total amount of geothermal resources within 3e10 km under the Earth's surface in the terrestrial areas of China is 24.6×10^(15)GJ.In line with climate zones categorized,the geothermal resource proportion is 43.81%for severe cold regions,29.19%for cold regions,6.92%for mild regions,13.82%for hot summer and cold winter regions,and 6.26%for hot summer and warm winter regions.Statistics according to the burial depth range reveal that the resources within depth ranges of 3-5 km,5-7 km and 7-10 km under the Earth's surface are 4.3119×10^(15)GJ,6.37674×10^(15)GJ and 13.89594×10^(15)GJ respectively,showing an increasing trend of geothermal potential with increasing burial depth.The deep geothermal resources are mainly of medium-to-high temperature reserves,and the energy supply strategy can be optimized by combining the climate conditions and population distribution,as well as considering power generation.In regions of cold or severe cold climate,the geothermal resources may be applied to geothermal power generation and district heating in combination;in regions of hot summer and cold winter or mild climates,the resources can be used for geothermal power generation combined with cooling and heating;in regions of hot summer and warm winter climates,the resources may be applied to geothermal power generation combined with cooling and industrial and agricultural utilization.Exploitation of deep geothermal resources also can be combined with carbon dioxide sequestration,multi-mineral resources extraction and energy storage to realize comprehensive exploitation and utilization of various energy resources.It is suggested that theoretical technology research should be combined with pilot tests and field demonstrations,and large-scale economic exploitation of deep geothermal resources should be arranged in a coordinated manner,following the principles of overall planning and step-by-step implementation.
基金supported by the National Natural Science Foundation of China under Grant No.62173126the National Natural Science Joint Fund project under Grant No.U1804162+2 种基金the Key Science and Technology Research Project of Henan Province under Grant No.222102210047,222102210200 and 222102320349the Key Scientific Research Project Plan of Henan Province Colleges and Universities under Grant No.22A520011 and 23A510018the Key Science and Technology Research Project of Anyang City under Grant No.2021C01GX017.
文摘Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods.
基金supported by the National Natural Science Foundation of China(NSFC)No.62172189 and 61772235the Natural Science Foundation of Guangdong Province No.2020A1515010771+1 种基金the Science and Technology Program of Guangzhou No.202002030372the UK Engineering and Physical Sciences Research Council(EPSRC)grants EP/P004407/2 and EP/P004024/1,and Innovate UK grant 106199-47198.
文摘The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.