Today's data center networks are designed using densely interconnected hosts in the data center.There are multiple paths between source host and destination server.Therefore,how to balance traffic is key issue wit...Today's data center networks are designed using densely interconnected hosts in the data center.There are multiple paths between source host and destination server.Therefore,how to balance traffic is key issue with the fast growth of network applications.Although lots of load balancing methods have been proposed,the traditional approaches cannot fully satisfy the requirement of load balancing in data center networks.The main reason is the lack of efficient ways to obtain network traffic statistics from each network device.As a solution,the OpenFlow protocol enables monitoring traffic statistics by a centralized controller.However,existing solutions based on OpenFlow present a difficult dilemma between load balancing and packet reordering.To achieve a balance between load balancing and packet reordering,we propose an OpenFlow based flow slice load balancing algorithm.Through introducing the idea of differentiated service,the scheme classifies Internet flows into two categories:the aggressive and the normal,and applies different splitting granularities to the two classes of flows.This scheme improves the performance of load balancing and also reduces the number of reordering packets.Using the trace-driven simulations,we show that the proposed scheme gains over 50%improvement over previous schemes under the path delay estimation errors,and is a practical and efficient algorithm.展开更多
Load balancing is typically used in the frequency domain of cellular wireless networks to balance paging, access, and traffic load across the available bandwidth. In this paper, we extend load balancing into the spati...Load balancing is typically used in the frequency domain of cellular wireless networks to balance paging, access, and traffic load across the available bandwidth. In this paper, we extend load balancing into the spatial domain, and we develop two approaches--network load balancing and single-carrier multilink--for spatial load balancing. Although these techniques are mostly applied to cellular wireless networks and Wi-Fi networks, we show how they can be applied to EV-DO, a 3G cellular data network. When a device has more than one candidate server, these techniques can be used to determine the quality of the channel between a server and the device and to determine the Ipad on each server. The proposed techniques leverage the advantages of existing EV-DO network architecture and are fully backward compatible. Network operators can substantially increase network capacity and improve user experience by using these techniques. Combining load balancing in the frequency and spatial domains improves connectivity within a network and allows resources to be optimally allocated according to the p-fair criterion. Combined load balancing further improves performance.展开更多
机械制造与装配车间电力需求的精准预测对合理安排机械生产加工、减少不必要的电能储备损耗有着重要意义。本文给出一种基于灰色理论优化vlPSO-LSSVM (variable linear Particle Swarm Optimization-Least Squares Support Vector Machi...机械制造与装配车间电力需求的精准预测对合理安排机械生产加工、减少不必要的电能储备损耗有着重要意义。本文给出一种基于灰色理论优化vlPSO-LSSVM (variable linear Particle Swarm Optimization-Least Squares Support Vector Machine)策略的电力储备需求预测模型。首先将预测的电力需求曲线通过滑动窗口将其划分为多个样本序列,结合灰色线性预测和支持向量机非线性映射快速精准的优势,在短期训练集内同时输出灰色预测序列和vlPSO-LSSVM预测序列;再定义训练规则,以一天(96个点)为一个周期,当周期中任意滑动窗内灰色预测序列不在LSSVM预测序列的包络线内时,这一滑动窗选用LSSVM预测序列作为预测输出,否则采用灰色预测序列作为输出。之后通过工程实例对本文模型进行验证,并与经典长短期记忆神经网络模型、BPNN和AR-RBFNN的预测结果对比分析。结果表明,基于灰色vlPSO-LSSVM模型的预测精确度显著优于其他算法,对机械生产车间制定合理的电力储备计划有较好的参考价值。展开更多
基金supported by a grant from the National Basic Research Development Program of China(973 Program)(No.2012CB315901,2012CB315906)the National High Technology Research and Development Program of China(863 Program)(No.2011AA01A103)
文摘Today's data center networks are designed using densely interconnected hosts in the data center.There are multiple paths between source host and destination server.Therefore,how to balance traffic is key issue with the fast growth of network applications.Although lots of load balancing methods have been proposed,the traditional approaches cannot fully satisfy the requirement of load balancing in data center networks.The main reason is the lack of efficient ways to obtain network traffic statistics from each network device.As a solution,the OpenFlow protocol enables monitoring traffic statistics by a centralized controller.However,existing solutions based on OpenFlow present a difficult dilemma between load balancing and packet reordering.To achieve a balance between load balancing and packet reordering,we propose an OpenFlow based flow slice load balancing algorithm.Through introducing the idea of differentiated service,the scheme classifies Internet flows into two categories:the aggressive and the normal,and applies different splitting granularities to the two classes of flows.This scheme improves the performance of load balancing and also reduces the number of reordering packets.Using the trace-driven simulations,we show that the proposed scheme gains over 50%improvement over previous schemes under the path delay estimation errors,and is a practical and efficient algorithm.
文摘Load balancing is typically used in the frequency domain of cellular wireless networks to balance paging, access, and traffic load across the available bandwidth. In this paper, we extend load balancing into the spatial domain, and we develop two approaches--network load balancing and single-carrier multilink--for spatial load balancing. Although these techniques are mostly applied to cellular wireless networks and Wi-Fi networks, we show how they can be applied to EV-DO, a 3G cellular data network. When a device has more than one candidate server, these techniques can be used to determine the quality of the channel between a server and the device and to determine the Ipad on each server. The proposed techniques leverage the advantages of existing EV-DO network architecture and are fully backward compatible. Network operators can substantially increase network capacity and improve user experience by using these techniques. Combining load balancing in the frequency and spatial domains improves connectivity within a network and allows resources to be optimally allocated according to the p-fair criterion. Combined load balancing further improves performance.
文摘机械制造与装配车间电力需求的精准预测对合理安排机械生产加工、减少不必要的电能储备损耗有着重要意义。本文给出一种基于灰色理论优化vlPSO-LSSVM (variable linear Particle Swarm Optimization-Least Squares Support Vector Machine)策略的电力储备需求预测模型。首先将预测的电力需求曲线通过滑动窗口将其划分为多个样本序列,结合灰色线性预测和支持向量机非线性映射快速精准的优势,在短期训练集内同时输出灰色预测序列和vlPSO-LSSVM预测序列;再定义训练规则,以一天(96个点)为一个周期,当周期中任意滑动窗内灰色预测序列不在LSSVM预测序列的包络线内时,这一滑动窗选用LSSVM预测序列作为预测输出,否则采用灰色预测序列作为输出。之后通过工程实例对本文模型进行验证,并与经典长短期记忆神经网络模型、BPNN和AR-RBFNN的预测结果对比分析。结果表明,基于灰色vlPSO-LSSVM模型的预测精确度显著优于其他算法,对机械生产车间制定合理的电力储备计划有较好的参考价值。
文摘基于反馈的两级交换结构FTSA(Feedback-based Two-stage Switch Architecture)在仿真中表现出极其优异的时延性能,但该结构对调度算法的时间限制使其理论性能无法实现。针对这一问题,该文基于2-错列对称的crossbar连接模式提出一种改进的反馈制两级交换结构FTSA-2-SS(FTSA using the 2-Staggered Symmetryconnection pattern),应用该连接模式可使信元传输与调度算法并行工作,从而将算法的时域空间拓展到一个时槽。此外,该文还利用双信元缓冲模式和RB(Re-sequencing Buffer)来解决由此而带来的信元冲突和失序问题。理论分析表明FTSA-2-SS和FTSA具有相同的稳定性,仿真结果显示FTSA-2-SS的时延性能优于其他非反馈负载均衡结构。
