A d hoc网络是由彼此对等的、自主的无线节点组成的自组织多跳网络.由于A d hoc网络的特点,使得网络拓扑设计的优化问题变得更加复杂,传统的方法甚至难于实现.本文应用微粒群算法,构造了一个适合自组网网络架构设计的运算法则,建立了一...A d hoc网络是由彼此对等的、自主的无线节点组成的自组织多跳网络.由于A d hoc网络的特点,使得网络拓扑设计的优化问题变得更加复杂,传统的方法甚至难于实现.本文应用微粒群算法,构造了一个适合自组网网络架构设计的运算法则,建立了一个新的对约束的处理技巧,通过引入共生机制,实现了网络设计在约束下的多目标优化设计,其微粒群的解关于约束是理想的,解集间是非次等的,不分优劣.仿真结果表明,该方法是有效的,它的实时性适应了A d hoc网络结构动态的变化.展开更多
To satisfy the ever-increasing bandwidth demand of modern data centers, researchers have proposed hybrid Data Center Networks(DCNs), which employ high-bandwidth Optical Circuit Switches(OCSs) to compensate for Electri...To satisfy the ever-increasing bandwidth demand of modern data centers, researchers have proposed hybrid Data Center Networks(DCNs), which employ high-bandwidth Optical Circuit Switches(OCSs) to compensate for Electrical Packet Switches(EPS). Existing designs, such as Helios and c-Through, mainly focus on reconfiguring optical devices to meet the estimated traffic requirements. However, these designs face two major challenges in their OCS-based networks, namely, the complex control mechanism and cabling problems. To solve these challenges, we propose TIO, a hybrid DCN that employs Visible Light Communication(VLC) instead of wired OCS design to connect racks. TIO integrates the wireless VLC-based Jellyfish and wired EPS-based Fat Tree seamlessly and combines the opposite and complementary characteristics, including wireless VLC direct connection and wired electrical packet switching, random graph, and Clos topology properties. To further exploit the merits of TIO, we design a hybrid routing scheme and congestion-aware flow scheduling method. Comprehensive evaluations indicate that TIO outperforms the Jellyfish and Fat Tree in both topology properties and network performance, and the flow scheduling method also evidently improves performance.展开更多
Visible-Light Communication(VLC) has the potential to provide dense and fast connectivity at low cost. In this paper we propose SFNet, a novel VLC-enabled hybrid data center network that extends the design of wireless...Visible-Light Communication(VLC) has the potential to provide dense and fast connectivity at low cost. In this paper we propose SFNet, a novel VLC-enabled hybrid data center network that extends the design of wireless Data Center Networks(DCNs) into three further dimensions:(1) fully wireless at the inter-rack level;(2) no need for a centralized control mechanism on wireless links;and(3) no need for any infrastructure-level alterations to data centers. Previous proposals typically cannot realize these three rationales simultaneously. To achieve this vision,the proposed SFNet augments fat-tree by organizing all racks into a wireless small-world network via VLC links. The use of VLC links eliminates hierarchical switches and cables in the wireless network, and thus reduces hardware investment and maintenance costs. To fully exploit the benefits of the topology of SFNet, we further propose its topology design and optimization method, routing scheme, and online flow scheduling algorithm. Comprehensive experiments indicate that SFNet exhibits good topological properties and network performance.展开更多
Topology design of artificial neural networks （ANNs） is an important problem for large scale applications. This paper describes a new efficient pruning method, the multi-weight optimal brain surgeon （MWOBS） method...Topology design of artificial neural networks （ANNs） is an important problem for large scale applications. This paper describes a new efficient pruning method, the multi-weight optimal brain surgeon （MWOBS） method, to optimize neural network topologies. The advantages and disadvantages of the OBS and unit-OBS were analyzed to develop the method. Actually, optimized topologies are difficult to get within reasonable times for complex problems. Motivating by the mechanism of natural neurons, the MW-OBS method balances the accuracy and the time complexity to achieve better neural network performance. The method will delete multiple connections among neurons according to the second derivative of the error function, so the arithmetic converges rapidly while the accuracy of the neural network remains high. The stability and generalization ability of the method are illustrated in a Java program. The results show that the MWOBS method has the same accuracy as OBS, but time is similar to that of unit-OBS. Therefore, the MWOBS method can be used to efficiently optimize structures of neural networks for large scale applications.展开更多
文摘A d hoc网络是由彼此对等的、自主的无线节点组成的自组织多跳网络.由于A d hoc网络的特点,使得网络拓扑设计的优化问题变得更加复杂,传统的方法甚至难于实现.本文应用微粒群算法,构造了一个适合自组网网络架构设计的运算法则,建立了一个新的对约束的处理技巧,通过引入共生机制,实现了网络设计在约束下的多目标优化设计,其微粒群的解关于约束是理想的,解集间是非次等的,不分优劣.仿真结果表明,该方法是有效的,它的实时性适应了A d hoc网络结构动态的变化.
基金partially supported by the National Natural Science Foundation for Outstanding Excellent Young Scholars of China(No.61422214)the National Natural Science Foundation of China(No.61772544)+3 种基金the National Key Basic Research and Development(973)Program of China(No.2014CB347800)the Hunan Provincial Natural Science Fund for Distinguished Young Scholars(No.2016JJ1002)the Guangxi Cooperative Innovation Center of Cloud Computing and Big Data(Nos.YD16507 and YD17X11)the NUDT Research Plan(No.ZK17-03-50)
文摘To satisfy the ever-increasing bandwidth demand of modern data centers, researchers have proposed hybrid Data Center Networks(DCNs), which employ high-bandwidth Optical Circuit Switches(OCSs) to compensate for Electrical Packet Switches(EPS). Existing designs, such as Helios and c-Through, mainly focus on reconfiguring optical devices to meet the estimated traffic requirements. However, these designs face two major challenges in their OCS-based networks, namely, the complex control mechanism and cabling problems. To solve these challenges, we propose TIO, a hybrid DCN that employs Visible Light Communication(VLC) instead of wired OCS design to connect racks. TIO integrates the wireless VLC-based Jellyfish and wired EPS-based Fat Tree seamlessly and combines the opposite and complementary characteristics, including wireless VLC direct connection and wired electrical packet switching, random graph, and Clos topology properties. To further exploit the merits of TIO, we design a hybrid routing scheme and congestion-aware flow scheduling method. Comprehensive evaluations indicate that TIO outperforms the Jellyfish and Fat Tree in both topology properties and network performance, and the flow scheduling method also evidently improves performance.
文摘Visible-Light Communication(VLC) has the potential to provide dense and fast connectivity at low cost. In this paper we propose SFNet, a novel VLC-enabled hybrid data center network that extends the design of wireless Data Center Networks(DCNs) into three further dimensions:(1) fully wireless at the inter-rack level;(2) no need for a centralized control mechanism on wireless links;and(3) no need for any infrastructure-level alterations to data centers. Previous proposals typically cannot realize these three rationales simultaneously. To achieve this vision,the proposed SFNet augments fat-tree by organizing all racks into a wireless small-world network via VLC links. The use of VLC links eliminates hierarchical switches and cables in the wireless network, and thus reduces hardware investment and maintenance costs. To fully exploit the benefits of the topology of SFNet, we further propose its topology design and optimization method, routing scheme, and online flow scheduling algorithm. Comprehensive experiments indicate that SFNet exhibits good topological properties and network performance.
文摘Topology design of artificial neural networks （ANNs） is an important problem for large scale applications. This paper describes a new efficient pruning method, the multi-weight optimal brain surgeon （MWOBS） method, to optimize neural network topologies. The advantages and disadvantages of the OBS and unit-OBS were analyzed to develop the method. Actually, optimized topologies are difficult to get within reasonable times for complex problems. Motivating by the mechanism of natural neurons, the MW-OBS method balances the accuracy and the time complexity to achieve better neural network performance. The method will delete multiple connections among neurons according to the second derivative of the error function, so the arithmetic converges rapidly while the accuracy of the neural network remains high. The stability and generalization ability of the method are illustrated in a Java program. The results show that the MWOBS method has the same accuracy as OBS, but time is similar to that of unit-OBS. Therefore, the MWOBS method can be used to efficiently optimize structures of neural networks for large scale applications.
