Fuzzy-based Adaptive Routing Algorithm for Networks-on-Chip

In this paper, we propose two adaptive routing algorithms to alleviate congestion in the network. In the first algorithm, the routing decision is assisted by the number of occupied buffer slots at the corresponding input buffer of the next router and the congestion level of that router. Although this algorithm performs better than the conventional method, DyXY, in some cases the proposed algorithm leads to non-optimal decisions. Fuzzy controllers compensate for ambiguities in the data by giving a level of confidence rather than declaring the data simply true or false. To make a better routing decision, we propose an adaptive routing algorithm based on fuzzy logic for Networks-on-chip where the routing path is determined based on the current condition of the network. The proposed algorithm avoids congestion by distributing traffic over the routers that are less congested or have a spare capacity. The output of the fuzzy controller is the congestion level, so that at each router, the neighboring router with the lowest congestion value is chosen for routing a packet. To evaluate the proposed routing method, we use two multimedia applications and two synthetic traffic profiles. The experimental results show that the fuzzy-based routing scheme improves the performance over the DyXY routing algorithm by up to 25% with a negligible hardware overhead.     

A novel heterogeneous congestion criterion for mesh-based networks-on-chip

In recent years, congestion in Networks-on-chip (NoC) has emerged as an important research topic due to the increasing number of processing cores. To solve the congestion, all the methods that have been proposed require a congestion criterion to detect whether a node is congested or not. All the congestion criteria that have been developed so far have similar behavior for all nodes in the network. In this paper, for the first time, a heterogeneous congestion criterion is proposed for a two-dimensional mesh network that is determined for each node based on its betweenness centrality. This criterion can be generalized to the other topology such as torus easily. This criterion is calculated before the network starts up and does not have any overhead in run time. Using this criterion will reduce the average latency of any congestion-aware method, such as congestion-aware routing algorithms. The evaluation section shows that the use of this criterion in three famous routing algorithms reduces the average latency up to 48% (21% on average for all algorithms and traffic patterns) in both real and synthetic traffics. In addition, the usage of this criterion reduces the power consumption in all simulation conditions because of reducing the average latency and lack of overhead. It is also shown at the end of the evaluation section that an increase in the network size will result in better performance of this criterion.     

基于环境感知的多路径路由算法

认知网络能够提高网络端到端的性能,确保服务质量(QoS)要求。而目前普遍使用的路由算法不具备网络认知能力。针对这一问题,提出一种具有认知能力的负载均衡多路径路由算法,该算法结合了Q学习算法和蚁群算法各自的优点,通过蚁群算法完成路径的建立和维护,Q学习算法实现拥塞规避和负载均衡。使用OPNET仿真比较,表明该算法在时延、带宽利用方面均具有较好的性能。     

结合距离与队列积压的无线Mesh网络拥塞感知路由协议

针对无线Mesh网络中路由的拥塞问题,提出了一种结合距离与队列积压信息的拥塞感知路由协议(DR-CAR)。首先,结合链路质量源路由(LQSR)协议中的距离度量和E-Backpressure协议中的队列积压度量构建一种新的链路质量度量。然后,每个节点通过探测数据包来计算链路质量,并通过控制数据包和其邻居节点进行交互,以此来更新链路质量。最后,节点根据链路质量来选择下一跳节点,从而构建从源节点到目的节点之间的最佳路径。另外,在MAC层中为控制数据包分配最高的优先级,同时保证控制数据包的安全性。仿真实验表明,在不同的链路负载下,该协议在网络传输时延和网络吞吐量方面都具有优越的性能,具有可行性和有效性。     

Adaptive load balancing in learning-based approaches for many-core embedded systems

Adaptive routing algorithms improve network performance by distributing traffic over the whole network. However, they require congestion information to facilitate load balancing. To provide local and global congestion information, we propose a learning method based on dual reinforcement learning approach. This information can be dynamically updated according to the changing traffic condition in the network by propagating data and learning packets. We utilize a congestion detection method which updates the learning rate according to the congestion level. This method calculates the average number of free buffer slots in each switch at specific time intervals and compares it with maximum and minimum values. Based on the comparison result, the learning rate sets to a value between 0 and 1. If a switch gets congested, the learning rate is set to a high value, meaning that the global information is more important than local. In contrast, local is more emphasized than global information in non-congested switches. Results show that the proposed approach achieves a significant performance improvement over the traditional Q-routing, DRQ-routing, DBAR and Dynamic XY algorithms.     

EDXY – A low cost congestion-aware routing algorithm for network-on-chips

In this paper, an adaptive routing algorithm for two-dimensional mesh network-on-chips (NoCs) is presented. The algorithm, which is based on Dynamic XY (DyXY), is called Enhanced Dynamic XY (EDXY). It is congestion-aware and more link failure tolerant compared to the DyXY algorithm. On contrary to the DyXY algorithm, it can avoid the congestion when routing from the current switch to the destination whose X position (Y position) is exactly one unit apart from the switch X position (Y position). This is achieved by adding two congestion wires (one in each direction) between each two cores which indicate the existence of congestion in a row (column). The same wires may be used to alarm a link failure in a row (column). These signals enable the routing algorithm to avoid these paths when there are other paths between the source and destination pair. To assess the latency of the proposed algorithm, uniform, transpose, hotspot, and realistic traffic profiles for packet injection are used. The simulation results reveal that EDXY can achieve lower latency compared to those of other adaptive routing algorithms across all workloads examined, with a 20% average and 30% maximum latency reduction on SPLASH-2 benchmarks running on a 49-core CMP. The area of the technique is about the same as those of the other routing algorithms.     

A new algorithm for packet routing problems using chaotic neurodynamics and its surrogate analysis

We propose a new algorithm for routing packets which effectively avoids packet congestion in computer networks. The algorithm involves chaotic neurodynamics. To realize effective packet routing, we first composed a basic method by a neural network, which routes packets with shortest path information between two nodes in the computer network. When the computer network has an irregular topology, the basic routing method does not work well, because most of packets cannot be transmitted to their destinations due to packet congestion in the computer network. To avoid such an undesirable problem, we employed chaotic neurodynamics to extend the basic method. Numerical experiments show that our proposed method exhibits good performance for scale-free networks. We also analyze why the proposed routing method is effective, comparing the proposed method with several stochastic methods. We introduced the method of surrogate data, a statistical hypothesis testing which is often used in the field of nonlinear time-series analysis. Analysis of the proposed method by the method of surrogate data reveals that the chaotic neurodynamics is most effective to decentralize the packet congestion in the computer network.     

具有路由性能和蚁群优化的移动P2P路由策略*

目前的移动P2P网络路由策略不能较好适应网络拓扑结构的动态多变、网络和移动设备的资源有限等特点,以及不能较好解决路由建立和维护所带来的网络拥塞和资源消耗。针对上述问题,采用有限洪泛路由查询和移动agent路由查询相结合的策略,为每个移动节点提供丰富可靠、及时高效的路由信息。同时,使用改进的蚁群算法,综合考虑网络带宽、时延等多个路由性能指标,作为路由策略中路由选择机制。仿真研究证明,将所提出的理论与方法应用于移动P2P的路由选择和维护等问题,本算法在控制消息的开销、平均响应效率等方面具有良好的性能,对于网络     

一种用于片上网络的拥塞感知哈密尔顿最短路径路由算法

类脑处理器能够支持多种脉冲神经网络SNN的部署来完成多种任务。片上网络NoC能够用较少的资源和功耗解决片上复杂的互连通信问题。现有的类脑处理器多采用片上网络来连接多个神经元核,以支持神经元之间的通信。SNN在时间步内瞬时突发的通信会在短时间内产生大量的脉冲报文。在这种通信行为下,片上网络会在短时间内达到饱和,造成网络拥塞。片上网络中非拥塞感知路由算法会进一步加剧网络拥塞状态,如何在每一个时间步内有效处理这些数据包,从而降低网络延迟,提高吞吐率,成为了目前需要解决的问题。首先对SNN的瞬时猝发通信特性进行了分析;然后提出一种拥塞感知的哈密尔顿路径路由算法,以降低NoC平均延迟和提高吞吐率;最后,使用Verilog HDL实现该路由算法,并通过模拟仿真进行性能评估。在网络规模为16×16的2D Mesh结构的片上网络中,相对于没有拥塞感知的路由算法,在数量猝发模式和概率猝发模式下,所提出的拥塞感知路由算法的NoC平均延迟分别降低了13.9%和15.9%;吞吐率分别提高了21.6%和16.8%。     

无线传感器网络中一种拥塞感知的多路径流量分配算法

拥塞控制是无线传感器网络中的一个关键问题,拥塞不但增加丢包率,影响传输可靠性,而且会浪费宝贵的能量资源。本文从保证数据传输可靠性的多路径路由出发,提出了 一个拥塞感知的多路径流量分配算法COTA。COTA从全网出发,基于路径的启发式信息分配流量,避免给潜在的热点区域分配过多负载。此外,繁忙节点使用可靠性相关的报文文调度策略有效保证高可靠报文优先使用带宽。详细的模拟实验表明了COTA在可靠性、吞吐率等方面的优势。     

片上网络中面向报文有序传输的自适应路由算法研究

与确定性路由算法相比,自适应路由算法可以提高片上网络的通信性能,但是报文可能会无序到达。在目的节点对报文排序将会导致严重的面积和计算开销,甚至可能会抵消采用自适应路由算法带来的性能增益。为此,本文首先提出一种部分自适应路由算法,以满足报文的有序到达。然后,描述了对本文算法提供支持的路由器硬件结构。最后,在二维片上网络下对本文算法及确定性和自适应路由算法进行了性能评估和比较。与XY算法相比,本文算法显著降低了报文延时,提升了饱和点。同时讨论了对路由器面积和功耗影响。虽然路由器的功耗有所上升,但是由于报文交付性能提升,因此每个flit的能耗增长可忽略不计。     

Ad Hoc移动网络多路径研究

在Ad Hoc移动网络中,由于结点的移动性,网络拓扑结构的易变性,路由成为研究的热点和难点。当前AdHoc路由协议一般都是单路径协议。然而由于多路径路由方式可以大大减少路由开销,提高数据传输率,减少网络拥塞,越来越多的研究表明,它将是未来Ad Hoc网络路由的主要方式。本文介绍了几种典型的多路径路由协议,并对这些多路径协议进行评价,对其性能进行比较,然后介绍多路径协议在QoS、能源和安全方面的应用,最后指出未来多路径研究的关键问题。     

Routing with adaptive path and limited flooding for mobile ad hoc networks

In MANET, each mobile host can freely move around and the network topology is dynamically changing. To send a datagram, a source host broadcasts a route discovery packet to the network. All neighboring nodes receiving this packet will rebroadcast this packet until it reaches the destination. It will have large flooding overhead, poor network performance and undesirable battery power consumption. To improve network performance, we design a novel routing protocol called RAPLF (Routing with Adaptive Path and Limited Flooding) for mobile ad hoc networks. Simulation results show that our protocol has better performance especially in packet delivery rate and flooding overhead when compared to similar protocols.     

一种可变效用的DTN路由方案研究

由于容迟网络（DTN）为满足极端情况下的端到端服务,使得容迟网络的路由机制复杂而且有别于现存的各种网络。基于洪泛的蔓延（Epidemic）路由,由于其广播特性,网络容易拥塞,基于效用（utility）的单复制路由机制,开销小,但是递交率不高,延迟大。文中根据这两种路由机制的优点,结合数据包产生的初期应尽最大努力递交这一思想,提出了一种可变效用的路由机制。通过ONE仿真器仿真了蔓延路由,PRoPHET路由,Spray and Wait路由机制以及文中提出的可变效用路由机制,结果表明文中提出的可变效用路由机制在递交率、平均延迟两个指标上表现的非常出色。     

软件定义网络中基于分段路由的多路径调度算法

针对当前软件定义网络(SDN)在应对大量数据流时造成的流表利用率低、转发响应较慢以及当前网络调度算法容易造成网络局部拥塞和负载不均衡等问题,提出一种基于分段路由的多路径调度算法SRMF。首先,SDN控制器根据网络拓扑连接情况下发初始流表;综合考虑网络链路剩余带宽、丢包率和数据流估测带宽需求进行路径权重计算;最后,根据路径权重选择最优路径并构造分段流表下发到边缘交换机。实验结果表明分段路由转发技术在多种网络拓扑下较一般转发技术在流表项开销方面有明显优势,SRMF算法与Hedera、ECMP相比,在业务流端到端时延、端到端时延抖动、网络吞吐率、丢包率等方面有一定的优势。     

使用多级交换网络进行高性能路由器设计

拓扑结构和路由算法是影响多级交换网络性能的重要因素．在比较多种多级互连拓扑属性的基础上,提出将3D Torus结构应用于大规模交换网络设计．然后针对3D Torus交换网络中报文路由面临的两个关键问题：多路径负载均衡和报文保序,提出一种基于维序的多路径路由算法DMR(dimension-order—based multi—path routing)．该算法可在保证报文顺序的同时在多条路径上平衡负载,提高交换网络吞吐率．最后通过模拟验证了算法的性能,并与维序路由和随机路由算法进行了比较．模拟结果表明,DMR算法的性能优于维序路由算法,能够达到随机路由算法性能水平,同时具有随机路由算法所不具备的报文保序特性．     

基于全分布式分组无线网络的蚂蚁路由算法

该文介绍了全分布式分组无线网络中路由算法。当前广泛运用的OSPF路由算法在网络的链路发生阻塞时，不能及时探测到链路状况。当链路发生拥塞时，在发包率继续增大的时候，只能简单地丢弃数据包。就此该文在分布式的无线网络中应用了蚂蚁算法。该算法由发送探测包来担任“觅食蚂蚁”在短时间内找出最优路径，分散流量，避免网络的拥塞。通过仿真结果表明：该算法在网络利用率和数据包传送时延上有较好的改善。     

基于移动状态的车载自组织网络路由算法

传统的AODV协议应用于车载自组织网络,尽管分组投递率比较高,但在数据分组需要发送时才建立路由,网络延迟较大。而DSDV中通过周期性的路由更新机制,网络延迟小,但需维护大量不必要的路由,并且拓扑结构变化使许多路由无效,导致分组投递率非常低。为了综合满足VANET分组投递率和网络延迟的要求,将AODV和DSDV两种路由建立机制相互融合,形成混合式路由协议。首先,根据车辆节点的位置、速度和方向等移动状态周期性地选择稳定且距离适中的链路,形成网络主干并更新路由;其次,当数据分组目的节点路由不存在时,发起路由发现过程建立路由,在路由请求报文前进和路由应答报文回溯过程中求出路由过期时间。仿真实验表明,尽管路由开销有所增大,分组投递率略低于AODV,但是网络延迟显著降低。     

Adaptive TCP congestion control and routing schemes using cross-layer information for mobile ad hoc networks

Compared with traditional networks, ad hoc networks possess many unique characteristics. For example, ad hoc networks can drop a packet due to network events other than buffer overflow. Unfortunately, the current layered network architecture makes it impossible to pass the information specific to one layer to other layers. As a result, if a packet is lost due to reasons other than buffer overflow, TCP adversely invokes its congestion control procedure. Similarly, the routing algorithm may misinterpret that a path is broken and adversely invoke the route recovery procedure.This study addresses the limitations of the current layered network architecture by adopting a cross-layer protocol design for TCP and routing algorithms in ad hoc networks. The objective of this approach is to enable the lower-layered ad hoc network to detect and differentiate all possible network events, including disconnections, channel errors, buffer overflow, and link-layer contention, that may cause packet loss. Using the information exploited by lower layers, the upper layer-3 routing algorithm, and the layer-4 TCP can take various actions according to the types of network events. Simulation results demonstrate that the combination of the cross-layer optimized TCP and routing algorithms can effectively improve the performance of TCP and DSR, regardless of whether it is in a stationary or a mobile ad hoc network.     

基于图卷积神经网络的智能路由算法

使用特定数学模型的路由转发算法难以满足用户多样化的服务质量需求,基于深度学习的智能路由方案因具有准确性、高效性、通用性等优势,成为路由决策的发展方向。然而,目前多数智能路由算法在网络拓扑动态变化时需要重新训练,造成路由更新不及时,难以应对网络拓扑动态变化。提出一种基于图卷积神经网络（GCN）的智能路由算法。线下利用提前采集的网络信息,根据路由开销标签训练GCN智能路由模型,通过该模型输出单跳路由开销。线上采集实时信息并根据模型输出的路由开销结果对网络层路由协议进行调整,计算最小路由开销的路由路径,实现自适应网络更新。算法利用GCN的图数据结构处理不规则的网络拓扑,通过图卷积算子自动提取特征解决路由网络多属性参数提取的问题,同时引入模糊C均值算法进行网络状态离散化分析,为数据集生成标签,从而有效监督GCN模型训练。实验结果表明,该算法较ECMP、DRL-TE和SmartRoute算法路由性能更好,其平均丢包率、时延和吞吐量指标均为最优,且相较于单一的流量模式具有更强的泛化能力。