LDBR: Low-deflection bufferless router for cost-sensitive network-on-chip design

In network-on-chip (NoC) designs, the bufferless router is more energy-efficient than the conventional router with buffers. However, in the bufferless network, deflections cause great performance loss. In this paper, three deflection models are firstly constructed for analyzing the causes of deflections. Then, we propose a low-deflection bufferless router (LDBR), in which a multi-channel network interface and a novel deflection routing based on turn model are designed for reducing the deflections during packet transmissions. Finally, LDBR is evaluated against the latest bufferless routers using synthetic and real-world traffic patterns. The experimental results exhibit that the deflection rate of LDBR network is reduced by 41% compared to other bufferless networks, and LDBR also shows superiority in cost and power consumption across all workloads.     

Non-derivative algorithm design for efficient routing over unreliable stochastic networks

We study the question of routing for minimum average drop rate over unreliable servers that are susceptible to random buffer failures, which arises in unreliable data or manufacturing networks. Interestingly, we first reveal that the traditional Join-the-Shortest-Queue (JSQ) or optimal Randomized Splitting (RS) strategies are consistently outperformed by the Constant Splitting Rule (CSR) where the incoming traffic is split with a constant fraction towards the available servers.This finding motivates us to obtain the optimal splitting fraction under CSR. However, the objective function to be minimized depends on the mean queue length of the servers, whose closed-form expression is not available and often intractable for general arrival and service processes. Thus, we use non-derivative methods to solve this optimization problem by approximately evaluating the objective value at each iteration. To that end, we explicitly characterize the approximation error by utilizing the regenerating nature of unreliable buffers. By adaptively controlling the precision of this approximation, we show that our proposed algorithm converges to an optimal splitting decision in the almost sure sense. Yet, previous works on non-derivative methods assume continuous differentiability of the objective function, which is not the case in our setup. We relax this strong assumption to the case when the objective function is locally Lipschitz continuous, which is another contribution of this paper.     

Caching and optimized request routing in cloud-based content delivery systems

Geographically distributed cloud platforms enable an attractive approach to large-scale content delivery. Storage at various sites can be dynamically acquired from (and released back to) the cloud provider so as to support content caching, according to the current demands for the content from the different geographic regions. When storage is sufficiently expensive that not all content should be cached at all sites, two issues must be addressed: how should requests for content be routed to the cloud provider sites, and what policy should be used for caching content using the elastic storage resources obtained from the cloud provider. Existing approaches are typically designed for non-elastic storage and little is known about the optimal policies when minimizing the delivery costs for distributed elastic storage.In this paper, we propose an approach in which elastic storage resources are exploited using a simple dynamic caching policy, while request routing is updated periodically according to the solution of an optimization model. Use of pull-based dynamic caching, rather than push-based placement, provides robustness to unpredicted changes in request rates. We show that this robustness is provided at low cost. Even with fixed request rates, use of the dynamic caching policy typically yields content delivery cost within 10% of that with the optimal static placement. We compare request routing according to our optimization model to simpler baseline routing policies, and find that the baseline policies can yield greatly increased delivery cost relative to optimized routing. Finally, we present a lower-cost approximate solution algorithm for our routing optimization problem that yields content delivery cost within 2.5% of the optimal solution.     

基于MIKE Flood的中小河流溃堤洪水演进数值模拟

针对水文资料缺乏的中小河流,以贵溪市罗塘河下游段为例,应用MIKE软件对河流溃堤洪水演进进行了研究。采用瞬时单位线法推求断面各频率的设计洪水,并将其作为洪水演进模型的输入;利用MIKE软件建立了贵溪市罗塘河下游段的一、二维耦合水动力模型,对该河下游段溃堤洪水演进过程进行了模拟;通过模拟分析,得到了溃口流量过程和堤防保护区洪水淹没过程。研究成果可为中小河流洪水风险分析和灾害损失评估提供技术支撑。     

A data porting tool for coupling models with different discretization needs

The presented work is part of a larger research program dealing with developing tools for coupling biogeochemical models in contaminated landscapes. The specific objective of this article is to provide researchers with a data porting tool to build hexagonal raster using information from a rectangular raster data (e.g. GIS format). This tool involves a computational algorithm and an open source software (written in C). The method of extending the reticulated functions defined on 2D networks is an essential key of this algorithm and can also be used for other purposes than data porting. The algorithm allows one to build the hexagonal raster with a cell size independent from the geometry of the rectangular raster. The extended function is a bi-cubic spline which can exactly reconstruct polynomials up to degree three in each variable. We validate the method by analyzing errors in some theoretical case studies followed by other studies with real terrain elevation data. We also introduce and briefly present an iterative water routing method and use it for validation on a case with concrete terrain data.     

LBMBS： A Load-Balanced Moveable Bubble Scheme for Tori Interconnection Networks

Moveable bubble flow control is an efficient technique to avoid deadlock for torus networks. However, it does not take into consideration the load balance feature. Therefore, this leads to inefficient resources utilization. Moreover, packets may be discarded due to the congestion of a network area. On the other hand, there are other algorithms that consider load balancing such as rHALB （r hop adaptive load-balanced）routing technique. Nonetheless, rHALB detects deadlock using the timeout threshold. This concept of deadlock detection may give incorrect responses in case the traffic is high. Therefore, packets are dropped although there is no deadlock. At the end, this affects the whole performance of the network. This paper proposes a new routing algorithm for tori interconnection networks, namely, the LBMBS （load-balanced moveable bubble scheme）. LBMBS integrates both concepts of moveable bubble and rHALB in order to provide at the end a load-balanced efficient deadlock-free algorithm. The protocol details are given and compared against other routing algorithms in the literature.     

基于贪婪路由协议的动态单/多链路故障恢复策略

针对现有的贪婪方法不能有效处理拓扑结构中链路故障的问题,提出单链路故障和多链路故障本地化恢复策略。首先,通过利用克莱因伯格的贪婪嵌入给出单链路故障恢复策略;然后,将其扩展到多链路故障的情况;最后,在基于Python/C++的仿真环境下对提出的技术进行评估。实验结果表明,该技术仅需要非常有限的资源,且造成的路由质量损耗也有限,可以实现快速切换,可依网络生成树中链路数目扩展。该技术的可扩展性、简单性和低开销使其适合于大型网络。     

网络边布局技术研究综述

网络边布局技术是网络可视化研究的重要内容,良好的边布局能够有效地展示网络的整体结构并从中揭示出某些隐含的信息模式.为了全面地总结和分析现有网络边布局技术,首先归纳了网络边布局的主要任务,回顾了指导网络边布局的美学标准.然后将网络边布局技术归纳为3类:边路由技术、边融合技术和边集束技术,分别阐述了各类技术中典型布局方法的主要原理和特征,并重点对边集束技术进行了分类分析.最后总结了目前研究中还存在的一些问题,展望了网络边布局技术的发展前景和面临的挑战,以期为相关领域的研究者提供有益参考.     

求解整合资源条件下的运输调度问题

针对传统的物流运输调度问题(Vehicle Routing Problem,VRP)中车辆之间不协作会造成资源浪费的情况,提出整合资源条件下的运输调度问题(Vehicle Routing Problem with Integration of resources,VRPIR),建立了相应的数学模型。由于混沌具有良好的遍历性,而粒子群优化算法(Particle Swarm Optimization,PSO)具有概念简单,参数少,容易实现等优点,将混沌优化方法引入到粒子群优化算法中,应用混沌粒子群优化算法(Chaos Particle Swarm Algorithm,CPSO)求解VRPIR和VRP,并用CPSO和PSO分别求解VRPIR,实验结果证明该算法优于粒子群优化算法,也证明了提出的VRPIR模型优于VRP,能节省资源,且最小化成本。     

一种车载自组织网络路况信息的数据信任模型

动态寻路是解决城市交通拥堵的重要手段。在动态寻路中,一些车辆产生和转发路况信息,使其他车辆能够避开拥堵路段。但一般车载自组织网络寻路算法缺乏验证路况信息的真实性的措施,导致恶意车辆能轻易篡改路况信息,误导其他车辆选择错误路线。提出了一种路况信息鉴别模型,该模型将基于数据的信任模型应用到路况信息真伪的鉴别中,并在一般投票算法的基础上利用D-S理论增加不确定情况下的鲁棒性。仿真实验表明,该算法在不增加额外信息交互的前提下,有效规避了恶意伪造信息,改进了车辆的行程时间。