For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarc...For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach was implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.展开更多
The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relativ...The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relative with each VM placement position. In this paper, we introduce a new improved traffic constant algorithm in the data center, called Degree and Weighted Maximum Traffic Ratio(DWMTR). The proposal DWMTR algorithm defines a new weighted ratio parameter in this paper. The main body of the parameter is constructed with the ratio, current overall intra-cluster traffic divided by current overall inter-cluster traffic, when a new VM places in the data center. The DWMTR algorithm has the ability to constraint the inter-cluster traffic incensement more strictly than the current VM placement algorithms based on traffic bandwidth allocation. For this algorithm based on the theoretical analysis and simulation, it confirms the proposed DWMTR possesses smaller global interactive traffic cost than the control group algorithms in the appointed VM placement in the three-layer data center model.展开更多
Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient...Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.展开更多
This paper presents a Torque Sharing Function(TSF)control of Switched Reluctance Machines(SRMs)with different current sensor placements to reconstruct the phase currents.TSF requires precise phase current information ...This paper presents a Torque Sharing Function(TSF)control of Switched Reluctance Machines(SRMs)with different current sensor placements to reconstruct the phase currents.TSF requires precise phase current information to ensure accurate torque control.Two proposed methods with different chopping transistors or a new PWM implementation require four or two current sensors to replace the current sensors on each phase regardless of the phase number.For both approaches,the actual phase current can be easily extracted during the single phase conducting region.However,how to separate the incoming and outgoing phase current values during the commutation region is the difficult issue to deal with.In order to derive these two adjacent currents,the explanations and comparisons of two proposed methods are described.Their effectiveness is verified by experimental results on a four-phase 8/6 SRM.Finally,the approach with a new PWM implementation is selected,which requires only two current sensors for reducing the number of sensors.The control system can be more compact and cheaper.展开更多
In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers...In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers are required and where they shall be.Eachmachine being represented by a low-order linear model,there is some reservation of the results.In this paper,extensive transient simulationsare performed and each machine is represented by a high-order nonlinear model.Coherent groups are found.A weighted speed deviationindex (SDI) is defined to find out the most unstable machines in the system.PSSs are designed after the decisions of PSS number and sites.Transient simulations are carried out again for the closed-loop system.A system stability index (SSI) is used to evaluate the stability of theclosed-loop system.It is found that three PSSs are sufficient to ensure the stability of the nine-machine system.展开更多
Social networks(SNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called Social Internet of Th...Social networks(SNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called Social Internet of Things(SIot).The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources,and this task demands an efficient storage procedure.For this kind of large volume of data storage,the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency.The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently.For efficient storage of these data,we use edge server,which is part of the cloud server,in this study.Thus,the data are distributed and stored with quick access,which will reduce the latency with response.The proposed data placement approach learns with machine learning(ML)algorithm called radial basis kernel function assisted with support vector machine(RBF-SVM)to classify the data center for storing the user and friend’s data from the SIoT devices.These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots.The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning(GP)method to meet the individual user’s demand of low latency with minimum costs.This way will keep the SIoT data placement efficient and effective over time.Accordingly,this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach.(i)Rather than storing the user data in a single cloud,this study uses the edge server closest to the SIoT devices for faster access with reduced response time.(ii)The classification algorithm called RBF-SVM is used to find storage for user for reducing data replication.(iii)Dynamic GP is introduced for data placement with reduced latency and minimum cost to fulfil the dynamic nature of the SN.The simulation result of this approach obtains reduced latency of 130 ms and minimum cost compared with those of the existing data placement approaches.Therefore,our proposed data placement with ML-based learning on edge provides promising results in terms of efficiency,effectiveness,and performance with reduced latency and minimum cost.展开更多
With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine (VM) is becoming a major issue for cloud providers. The existing virtual machine placement (VMP...With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine (VM) is becoming a major issue for cloud providers. The existing virtual machine placement (VMP) solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization (MLU). On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.展开更多
Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the perf...Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.展开更多
Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM)management methods.VM dynamic manage...Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM)management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.展开更多
The mobile hybrid machining robot has a very bright application prospect in the field of high-efficiency and high-precision machining of large aerospace structures.However,an inappropriate base placement may make the ...The mobile hybrid machining robot has a very bright application prospect in the field of high-efficiency and high-precision machining of large aerospace structures.However,an inappropriate base placement may make the robot encounter a singular configuration,or even fail to complete the entire machining task due to unreachability.In addition to considering the two constraints of reachability and non-singularity,this paper also optimizes the robot base placement with stiffness as the goal to improve the machining quality.First of all,starting from the structure of the robot,the reachability and nonsingularity constraints are transformed into a simple geometric constraint imposed on the base placement:feasible base placement area.Then,genetic algorithm is used to search for the base placement with near optimal stiffness(near optimal base placement for short)in the feasible base placement area.Finally,multiple controlled experiments were carried out by taking the milling of a protuberance on the spacecraft cabin as an example.It is found that the calculated optimal base placement meets all the constraints and that the machining quality was indeed improved.In addition,compared with simple genetic algorithm,it is proved that the feasible base placement area method can shorten the running time of the whole program.展开更多
文摘For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach was implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.
基金supported by National Major Projects (No. 2015ZX03001013-002)National Natural Science Foundation of China (No. 61173149)+1 种基金Beijing Higher Education Young Elite Teacher ProjectFundamental Research Funds for the Central Universities
文摘The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relative with each VM placement position. In this paper, we introduce a new improved traffic constant algorithm in the data center, called Degree and Weighted Maximum Traffic Ratio(DWMTR). The proposal DWMTR algorithm defines a new weighted ratio parameter in this paper. The main body of the parameter is constructed with the ratio, current overall intra-cluster traffic divided by current overall inter-cluster traffic, when a new VM places in the data center. The DWMTR algorithm has the ability to constraint the inter-cluster traffic incensement more strictly than the current VM placement algorithms based on traffic bandwidth allocation. For this algorithm based on the theoretical analysis and simulation, it confirms the proposed DWMTR possesses smaller global interactive traffic cost than the control group algorithms in the appointed VM placement in the three-layer data center model.
文摘Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.
基金The test bench was supported by The Future Planning(NRF-2016H1D5A1910536)“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resource from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20164010200940)The authors would like to thank FONDS DAVID ET ALICE VAN BUUREN and FONDATION JAUMOTTE-DEMOULIN for the funding“Prix Van Buuren-Jaumotte-Demoulin”.
文摘This paper presents a Torque Sharing Function(TSF)control of Switched Reluctance Machines(SRMs)with different current sensor placements to reconstruct the phase currents.TSF requires precise phase current information to ensure accurate torque control.Two proposed methods with different chopping transistors or a new PWM implementation require four or two current sensors to replace the current sensors on each phase regardless of the phase number.For both approaches,the actual phase current can be easily extracted during the single phase conducting region.However,how to separate the incoming and outgoing phase current values during the commutation region is the difficult issue to deal with.In order to derive these two adjacent currents,the explanations and comparisons of two proposed methods are described.Their effectiveness is verified by experimental results on a four-phase 8/6 SRM.Finally,the approach with a new PWM implementation is selected,which requires only two current sensors for reducing the number of sensors.The control system can be more compact and cheaper.
文摘In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers are required and where they shall be.Eachmachine being represented by a low-order linear model,there is some reservation of the results.In this paper,extensive transient simulationsare performed and each machine is represented by a high-order nonlinear model.Coherent groups are found.A weighted speed deviationindex (SDI) is defined to find out the most unstable machines in the system.PSSs are designed after the decisions of PSS number and sites.Transient simulations are carried out again for the closed-loop system.A system stability index (SSI) is used to evaluate the stability of theclosed-loop system.It is found that three PSSs are sufficient to ensure the stability of the nine-machine system.
文摘Social networks(SNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called Social Internet of Things(SIot).The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources,and this task demands an efficient storage procedure.For this kind of large volume of data storage,the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency.The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently.For efficient storage of these data,we use edge server,which is part of the cloud server,in this study.Thus,the data are distributed and stored with quick access,which will reduce the latency with response.The proposed data placement approach learns with machine learning(ML)algorithm called radial basis kernel function assisted with support vector machine(RBF-SVM)to classify the data center for storing the user and friend’s data from the SIoT devices.These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots.The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning(GP)method to meet the individual user’s demand of low latency with minimum costs.This way will keep the SIoT data placement efficient and effective over time.Accordingly,this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach.(i)Rather than storing the user data in a single cloud,this study uses the edge server closest to the SIoT devices for faster access with reduced response time.(ii)The classification algorithm called RBF-SVM is used to find storage for user for reducing data replication.(iii)Dynamic GP is introduced for data placement with reduced latency and minimum cost to fulfil the dynamic nature of the SN.The simulation result of this approach obtains reduced latency of 130 ms and minimum cost compared with those of the existing data placement approaches.Therefore,our proposed data placement with ML-based learning on edge provides promising results in terms of efficiency,effectiveness,and performance with reduced latency and minimum cost.
基金supported by the National Natural Science Foundation of China(61002011)the National High Technology Research and Development Program of China(863 Program)(2013AA013303)+2 种基金the Fundamental Research Funds for the Central Universities(2013RC1104)the Natural Science Foundation of Gansu Province,China(1308RJZA306)the Open Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2009KF-2-08)
文摘With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine (VM) is becoming a major issue for cloud providers. The existing virtual machine placement (VMP) solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization (MLU). On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.
基金supported by the National Key R&D Program of China(No.2017YFB1003000)the National Natural Science Foundation of China(Nos.61572129,61602112,61502097,61702096,61320106007,and 61632008)+4 种基金the International S&T Cooperation Program of China(No.2015DFA10490)the National Science Foundation of Jiangsu Province(Nos.BK20160695 and BK20170689)the Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201)the Key Laboratory of Computer Network and InformationIntegration of Ministry of Education of China(No.93K-9)supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization and Collaborative Innovation Center of Wireless Communications Technology
文摘Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.
基金supported by the National Natural Science Foundation of China(Nos.61432017 and 61772199)。
文摘Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM)management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.
基金supported by National Natural Science Foundation of China(Nos.91948301,52175025 and 51721003).
文摘The mobile hybrid machining robot has a very bright application prospect in the field of high-efficiency and high-precision machining of large aerospace structures.However,an inappropriate base placement may make the robot encounter a singular configuration,or even fail to complete the entire machining task due to unreachability.In addition to considering the two constraints of reachability and non-singularity,this paper also optimizes the robot base placement with stiffness as the goal to improve the machining quality.First of all,starting from the structure of the robot,the reachability and nonsingularity constraints are transformed into a simple geometric constraint imposed on the base placement:feasible base placement area.Then,genetic algorithm is used to search for the base placement with near optimal stiffness(near optimal base placement for short)in the feasible base placement area.Finally,multiple controlled experiments were carried out by taking the milling of a protuberance on the spacecraft cabin as an example.It is found that the calculated optimal base placement meets all the constraints and that the machining quality was indeed improved.In addition,compared with simple genetic algorithm,it is proved that the feasible base placement area method can shorten the running time of the whole program.