电力通信网是电力系统业务实现的重要载体。SDN(Software Defined Network)架构的新型电力通信网能够为电力系统业务提供高效的信息传输技术支持。SDN控制器部署是影响SDN网络性能的重要因素。文中结合电力通信网特点,提出一种适用于电...电力通信网是电力系统业务实现的重要载体。SDN(Software Defined Network)架构的新型电力通信网能够为电力系统业务提供高效的信息传输技术支持。SDN控制器部署是影响SDN网络性能的重要因素。文中结合电力通信网特点,提出一种适用于电力通信网中的SDN控制器部署方法。该方法在建模过程中引入了节点重要度概念,基于非支配排序分类的和声搜索算法求解可靠性和时延问题,采用文中方法和CNPA(Clustering-based Network Partition Algorithm)对IEEE 30通信节点系统进行了比较仿真分析,结果表明,文中方法对电力通信网具有更好的适用性。展开更多
Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concer...Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.展开更多
Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment mo...Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment models help to analyze reliability of different deployments.Though many approaches for architecture-based reliability estimation exist,little work has incorporated the influence of system deployment and hardware resources into reliability estimation.There are many factors influencing system deployment.By translating the multi-dimension factors into degree matrix of component dependence,we provide the definition of component dependence and propose a method of calculating system reliability of deployments.Additionally,the parameters that influence the optimal deployment may change during system execution.The existing software deployment architecture may be ill-suited for the given environment,and the system needs to be redeployed to improve reliability.An approximate algorithm,A*_D,to increase system reliability is presented.When the number of components and host nodes is relative large,experimental results show that this algorithm can obtain better deployment than stochastic and greedy algorithms.展开更多
文摘Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.
基金Supported by the High Technology Research and Development Program of China(No.2008AA01A201)National High Technology Research,Development Plan of China (No.2006AA01A103)the High Technology Research and Development Program of China(No.2009AA01A404)
文摘Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment models help to analyze reliability of different deployments.Though many approaches for architecture-based reliability estimation exist,little work has incorporated the influence of system deployment and hardware resources into reliability estimation.There are many factors influencing system deployment.By translating the multi-dimension factors into degree matrix of component dependence,we provide the definition of component dependence and propose a method of calculating system reliability of deployments.Additionally,the parameters that influence the optimal deployment may change during system execution.The existing software deployment architecture may be ill-suited for the given environment,and the system needs to be redeployed to improve reliability.An approximate algorithm,A*_D,to increase system reliability is presented.When the number of components and host nodes is relative large,experimental results show that this algorithm can obtain better deployment than stochastic and greedy algorithms.
