应用服务器集群是平台即服务(platform as a service,PaaS)模式的主要运行环境。针对云环境下动态变化的用户负载和共享、异构的集群环境,提出一种自适应集群调整方法,根据集群负载状况实现资源按需供给。该方法建立了PaaS平台的性能分...应用服务器集群是平台即服务(platform as a service,PaaS)模式的主要运行环境。针对云环境下动态变化的用户负载和共享、异构的集群环境,提出一种自适应集群调整方法,根据集群负载状况实现资源按需供给。该方法建立了PaaS平台的性能分析模型,并据此提出自适应的资源供给机制和负载均衡机制。实验结果表明,通过调节集群节点逻辑资源池的大小和集群节点数量,配合自适应负载均衡方法,达到了资源按需供给的目的。展开更多
在LEACH协议的基础上进行改进提出了一种高能效无线传感器网络协议——LEACH-M。LEACH协议中,簇首节点与基站之间直接传送数据,离基站较远区域的簇首能耗较大,这影响了系统寿命。LEACH-M协议在簇首形成阶段采用CSMA/CA(carrier sense mu...在LEACH协议的基础上进行改进提出了一种高能效无线传感器网络协议——LEACH-M。LEACH协议中,簇首节点与基站之间直接传送数据,离基站较远区域的簇首能耗较大,这影响了系统寿命。LEACH-M协议在簇首形成阶段采用CSMA/CA(carrier sense multi-access with collision avoidance)作为MAC协议,并在簇首节点与基站之间引入了改进的多跳路由算法,使网络中各簇的能耗更加均匀。仿真结果表明,与LEACH相比,LEACH-M协议具有更好的能量有效性,并且提高了无线传感器网络的寿命。展开更多
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern co...Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.展开更多
We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtaine...We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtained information to up-date the routing tables. Routing decisions can be made by the fuzzy logic technique based on local information about the current network state and the knowledge constructed by a previous set of behaviors of other agents. The fuzzy logic technique allows multiple constraints such as path delay and path utilization to be considered in a simple and intuitive way. Simulation tests show that AFAR outperforms OSPF, AntNet and ASR, three of the currently most important state-of-the-art algorithms, in terms of end-to-end delay, packet delivery, and packet drop ratio. AFAR is a promising alternative for routing of data in next generation networks.展开更多
A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images,...A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.展开更多
文摘应用服务器集群是平台即服务(platform as a service,PaaS)模式的主要运行环境。针对云环境下动态变化的用户负载和共享、异构的集群环境,提出一种自适应集群调整方法,根据集群负载状况实现资源按需供给。该方法建立了PaaS平台的性能分析模型,并据此提出自适应的资源供给机制和负载均衡机制。实验结果表明,通过调节集群节点逻辑资源池的大小和集群节点数量,配合自适应负载均衡方法,达到了资源按需供给的目的。
文摘在LEACH协议的基础上进行改进提出了一种高能效无线传感器网络协议——LEACH-M。LEACH协议中,簇首节点与基站之间直接传送数据,离基站较远区域的簇首能耗较大,这影响了系统寿命。LEACH-M协议在簇首形成阶段采用CSMA/CA(carrier sense multi-access with collision avoidance)作为MAC协议,并在簇首节点与基站之间引入了改进的多跳路由算法,使网络中各簇的能耗更加均匀。仿真结果表明,与LEACH相比,LEACH-M协议具有更好的能量有效性,并且提高了无线传感器网络的寿命。
文摘Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
基金Project supported by the Iranian Telecommunication Research Center
文摘We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtained information to up-date the routing tables. Routing decisions can be made by the fuzzy logic technique based on local information about the current network state and the knowledge constructed by a previous set of behaviors of other agents. The fuzzy logic technique allows multiple constraints such as path delay and path utilization to be considered in a simple and intuitive way. Simulation tests show that AFAR outperforms OSPF, AntNet and ASR, three of the currently most important state-of-the-art algorithms, in terms of end-to-end delay, packet delivery, and packet drop ratio. AFAR is a promising alternative for routing of data in next generation networks.
基金partially supported by the National Natural Science Foundation of China under Grants No. 61071146, No. 61171165the Natural Science Foundation of Jiangsu Province under Grant No. BK2010488+1 种基金sponsored by Qing Lan Project, Project 333 "The Six Top Talents" of Jiangsu Province
文摘A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.
