An optimal multicast algorithm for cube-connected cycles

This paper presents an efficient algorithm that implements one-to-many,or multicast,communication in one-port wormhole-routed cube-connected cycles(CCCs) in the absence of hardware multicast support.By exploiting the propoeries of the switching technology and the use of virtual channels,a minimumtime multicast algorithm is presented for n-dimensional CCCs that use deterministic routing of unicast messages.The algorithm can deliver a multicast message to m-1 destinations in [log2m] message-passing steps,while avoiding contention among the constitutent unicast messages,Performance results of a simulation study on CCCs with up to 10,240 nodes are also given.     

Utility-based bandwidth allocation algorithm for heterogeneous wireless networks

In next generation wireless network (NGWN), mobile users are capable of connecting to the core network through various heterogeneous wireless access networks, such as cellular network, wireless metropolitan area network (WMAN), wireless local area network (WLAN), and ad hoc network. NGWN is expected to provide high-bandwidth connectivity with guaranteed quality-of-service to mobile users in a seamless manner; however, this desired function demands seamless coordination of the heterogeneous radio access network (RAN) technologies. In recent years, some researches have been conducted to design radio resource management (RRM) architectures and algorithms for NGWN; however, few studies stress the problem of joint network performance optimization, which is an essential goal for a cooperative service providing scenario. Furthermore, while some authors consider the competition among the service providers, the QoS requirements of users and the resource competition within access networks are not fully considered. In this paper, we present an interworking integrated network architecture, which is responsible for monitoring the status information of different radio access technologies (RATs) and executing the resource allocation algorithm. Within this architecture, the problem of joint bandwidth allocation for heterogeneous integrated networks is formulated based on utility function theory and bankruptcy game theory. The proposed bandwidth allocation scheme comprises two successive stages, i.e., service bandwidth allocation and user bandwidth allocation. At the service bandwidth allocation stage, the optimal amount of bandwidth for different types of services in each network is allocated based on the criterion of joint utility maximization. At the user bandwidth allocation stage, the service bandwidth in each network is optimally allocated among users in the network according to bankruptcy game theory. Numerical results demonstrate the efficiency of the proposed algorithm.     

Adaptive joint bandwidth and power allocation in heterogeneous wireless access environment

Joint bandwidth and power allocation for a multi-radio access(MRA)system in a heterogeneous wireless access environment is studied.Since both the number of users being served by the system and the wireless channel state are time-varying,the optimal resource allocation is no longer a static optimum and will change with the varying network state.Moreover,distributed resource allocation algorithms that require iterative updating and signaling interactions cannot converge in negligible time.Thus,it is unrealistic to assume that the active user number and the wireless channel state remain unchanged during the iterations.In this paper,we propose an adaptive joint bandwidth and power allocation algorithm based on a novel iteration stepsize selection method,which can adapt to the varying network state and accelerate the convergence rate.A distributed solution is also designed for the adaptive joint resource allocation implementation.Numerical results show that the proposed algorithm can not only track the varying optimal resource allocation result much more quickly than a traditional algorithm with fixed iteration stepsize,but can also reduce the data transmission time for users and increase the system throughput.     

A suboptimal joint bandwidth and power allocation for cooperative relay networks＂ a cooperative game theoretic approach

In commercial networks, user nodes operating on batteries are assumed to be selfish to consume their resources (i.e., bandwidth and power) solely maximizing their own benefits (e.g., the received signal-to-noise ratios (SNRs) and datarates). In this paper, a cooperative game theoretical framework is proposed to jointly perform the bandwidth and power allocation for selfish cooperative relay networks. To ensure a fair and efficient resource sharing between two selfish user nodes, we assume that either node can act as a source as well as a potential relay for each other and either node is willing to seek cooperative relaying only if the datarate achieved through cooperation is not lower than that achieved through noncooperation (i.e., direct transmission) by consuming the same amount of bandwidth and power resource. Define the cooperative strategy of a node as the number of bandwidth and power that it is willing to contribute for relaying purpose. The two node joint bandwidth and power allocation (JBPA) problem can then be formulated as a cooperative game. Since the Nash bargaining solution (NBS) to the JBPA game (JBPAG) is computationally difficult to obtain, we divide it into two subgames, i.e., the bandwidth allocation game (BAG) and the power allocation game (PAG). We prove that both the subgames have unique NBS. And then the suboptimal NBS to the JBPAG can be achieved by solving the BAG and PAG sequentially. Simulation results show that the proposed cooperative game scheme is efficient in that the performance loss of the NBS result to that of the maximal overall data-rate scheme is small while the maximal-rate scheme is unfair. The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

Resource pooling for frameless network architecture with adaptive resource allocation

The system capacity for future mobile communication needs to be increased to fulfill the emerging requirements of mobile services and innumerable applications. The cellular topology has for long been regarded as the most promising way to provide the required increase in capacity. However with the emerging densification of cell deployments, the traditional cellular structure limits the efficiency of the resource, and the coordination between different types of base stations is more complicated and entails heavy cost. Consequently, this study proposes frameless network architecture (FNA) to release the cell boundaries, enabling the topology needed to implement the FNA resource allocation strategy. This strategy is based on resource pooling incorporating a new resource dimension-antenna/antenna array. Within this architecture, an adaptive resource allocation method based on genetic algorithm is proposed to find the optimal solution for the multi-dimensional resource allocation problem. Maximum throughput and proportional fair resource allocation criteria are considered. The simulation results show that the proposed architecture and resource allocation method can achieve performance gains for both criteria with a relatively low complexity compared to existing schemes.     

Dm-KDE： dynamical kernel density estimation by sequences of KDE estimators with fixed number of components over data streams

In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.     

Resource allocation algorithm with limited feedback for multicast single frequency networks

The single frequency network (SFN) can provide a multimedia broadcast multicast service over a large coverage area. However, the application of SFN is still restricted by a large amount of feedback. Therefore, we propose a multicast resource allocation scheme based on limited feedback to maximize the total rate while guaranteeing the quality of service (QoS) requirement of real-time services. In this scheme, we design a user feedback control algorithm to effectively reduce feedback load. The algorithm determines to which base stations the users should report channel state information. We then formulate a joint subcarrier and power allocation issue and find that it has high complexity. Hence, we first distribute subcarriers under the assumption of equal power and develop a proportional allocation strategy to achieve a tradeoff between fairness and QoS. Next, an iterative water-filling power allocation is proposed to fully utilize the limited power. To further decrease complexity, a power iterative scheme is introduced. Simulation results show that the proposed scheme significantly improves system performance while reducing 68% of the feedback overhead. In addition, the power iterative strategy is suitable in practice due to low complexity.     

Automatic modeling algorithm of stochastic error for inertial sensors

This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.     

ROPAS: Cross-Layer Cognitive Architecture for Mobile UWB Networks

The allocation of bandwidth to unlicensed users, without significantly increasing the interference on the existing licensed users, is a challenge for Ultra Wideband （UWB） networks. Our research work presents a novel Rake Optimization and Power Aware Scheduling （ROPAS） architecture for UWB networks. Since UWB communication is rich in multipath effects, a Rake receiver is used for path diversity. Our idea of developing an optimized Rake receiver in our ROPAS architecture stems from the intention of reducing the computation complexity in terms of the number of multiplications and additions needed for the weight derivation attached to each finger of the Rake receiver. Our proposed work uses the Cognitive Radio （CR） for dynamic channel allocation among the requesting users while limiting the average power transmitted in each sub-band. In our proposed novel ROPAS architecture, dynamic channel allocation is achieved by a CR-based cross-layer design between the PHY and Medium Access Control （MAC） layers. Additionally, the maximum number of parallel transmissions within a frame interval is formulated as an optimization problem. This optimal decision is based on the distance parameter between a transmitter-receiver pair, bit error rate and frequency of request by a particular application. Moreover, the optimization problem improvises a differentiation technique among the requesting applications by incorporating priority levels among user applications. This provides fairness and higher throughput among services with varying power constraint and data rates required for a UWB network.     

IETF softwire unicast and multicast framework for IPv6 transition

IPv6 protocol plays an important role in the next generation of Internet （NGI）. It is expected that the elegant coexistence of IPv4 and IPv6 is the key point of IPv6 transition. To solve the transition problem, we propose a mesh unicast framework and a multicast framework in this paper. We describe two reference models for the mesh unicast framework, and put forward two potential solutions for the multicast framework. A Linux-based prototype is implemented for IPv4 over IPv6 scenario and a test bed is deployed with 8 nodes on CERNET2. The deployment demon- strates the advantages of the framework.     

基于AIMD算法的分层多播拥塞控制

提出了一种基于AIMD算法的分层多播拥塞控制算法．算法借助AIMD算法具有的良好TCP兼容性和稳定性，采用慢增慢减的速率调节原则来防止TCP中速率减半策略所带来的速率振荡．为避免反馈处理带来的复杂性和可扩缩性问题，提出了无须反馈的收方至发方间往返时延估计方法．算法采用类似TCP的慢启动算法来提高链路的利用率和收敛速度．通过仿真评估得出，算法对TCP流、不同多播流均表现出理想的公平性，并有很高的带宽利用率和良好的稳定性．     

On the Integration of Unicast and Multicast Cell Scheduling in Buffered Crossbar Switches

Internet traffic is a mixture of unicast and multicast flows. Integrated schedulers capable of dealing with both traffic types have been designed mainly for Input Queued (IQ) buffer-less crossbar switches. Combined Input and crossbar queued (CICQ) switches, on the other hand, are known to have better performance than their buffer-less predecessors due to their potential in simplifying the scheduling and improving the switching performance. The design of integrated schedulers in CICQ switches has thus far been neglected. In this paper, we propose a novel CICQ architecture that supports both unicast and multicast traffic along with its appropriate scheduling. In particular, we propose an integrated round-robin-based scheduler that efficiently services both unicast and multicast traffic simultaneously. Our scheme, named multicast and unicast round robin scheduling (MURS), has been shown to outperform all existing schemes under various traffic patterns. Simulation results suggested that we can trade the size of the internal buffers for the number of input multicast queues. We further propose a hardware implementation of our algorithm for a 16 times 16 buffered crossbar switch. The implementation results suggest that MURS can run at 20 Gbps line rate and a clock cycle time of 2.8 ns, reaching an aggregate switching bandwidth of 320 Gbps.     

基于集成FEC和层次传输的可靠组播的流控技术

端系统能力和网络带宽的异构性给大规模组播的流控带来很大困难。本文将前向纠错（ＦＥＣ）技术与层次传输相结合,较好地解决了异构环境下可靠组播的流控问题。我们给出了传输调度和信道速率分配的算法,讨论了差错控制问题。性能分析和模拟表明,该方法对大规模、异构组播组可显著减少平均传输时间并且有效地利用网络带宽。只需较少数目的组播组就能得到性能的很大提高。软件ＦＥＣ编码器的速度能够匹配当前的网络条件,算法易于实     

一种非TCP友好流惩罚算法

为了改善Internet中各流量带宽分配的不公平性,提出一种适于缓存管理的非TCP友好流的惩罚算法（NFTFP算法）。通过在缓冲区记录每个连接所占用的带宽,对于非TCP友好流量进行识别和惩罚,从而提高不同数据流享用带宽的公平性。仿真实验表明在标准TCP流量和非TCP友好流共存的环境下,NFTFP算法能提高对非TCP友好流的惩罚力度,在保持较高的链路利用率的同时,获得比RED算法更好的公平性。     

Improving the multicast state scalability in internet routers by integrating hash algorithm with recursive unicast

In this article, a new protocol called REHASH has been devised that gracefully integrates the idea of recursive unicast with hash algorithm to achieve scalable multicast for improved performance.In this model, data packets have unicast destination addresses. Therefore, REHASH supports pure unicast routers transparently.The key idea of the proposed protocol is to simplify address allocation and implements multicast distribution using recursive unicast hash trees. The branching nodes recursively create packet copies to implement the distribution. REHASH adopts the source-specific channel abstraction to tackle the address allocation and the sender access control problems. Consequently, it provides best routes and is suitable for including QoS and authentication parameters inside hash tree construction algorithm. Additionally, REHASH tree management provides enhanced tree stability in the presence of group dynamics.     

分层组播中MAX-MIN公平速率分配算法的运用

以分层多速率组播解决方案为研究目标,针对多速率组播与TCP单播共存网络的公平性问题,通过在分层组播中应用max min公平速率分配算法来得到各虚会话的公平速率,并采用二分法对速率集合进行不同粒度上的整合,给出了具体的分层解决策略,从而为异构网络环境中分层多速率组播在公平性前提下的有效实施提供了保障。     

高速路由器中一种有效的组播交换排队机制

许多Internet新业务产生的网络流量都属于组播类型．高速路由器中组播交换队列设计是解决组播数据在复杂网络中有效传输的关键问题之一．理想情况下,为保证服务的公平性及有效性,不应该在排队和调度时对组播和单播人为地进行区分．在二者共存的情况下,如何进行公平地排队、交换是亟待解决的问题．基于共享存储交换结构,提出了一种有效的组播交换队列设计方案,同时提供了相应的与组播比例和平均扇出相关的缓存管理机制和队列调度算法．仿真结果表明,与在Cisco高端路由器中广泛应用的ESLIP设计方案相比,在输入负载大于80％的重负载环境中,该系统能够获得更低的平均时延和更小的系统丢包率．     

高吞吐量的核心无状态公平队列算法

提出了一种优化的核心无状态公平队列凋度算法(xCSFQ),在CSFQ的基础上,根据缓冲区占用率和数据流到达速率决定丢包概率,缓存管理上采用基于CHOKC原理的机制进行缓存管理,解决了CSFQ链路利用率低的问题,提高了带宽在UDP流和TCP流之间分配的公平性,最后对算法进行了仿真分析。     

CSFQ算法分析与改进

核心无状态公平队列调度（CSVQ）算法提供了如同有状态网那样好的公平带宽分配，但它的丢包算法不适用于TCP流。针对TCP流的特点，对CSFQ算法进行如下改进：将缓存队列长度与丢包概率关联起来，用一种类似于RED（random early drop）缓存管理方法解决了缓存频繁溢出导致的一些问题；对TCP流的丢包率进行修正，使用多余带宽来转发TCP包，解决TCP流与UDP流的带宽分配公平性。仿真试验表明，新算法NEW-CSFQ更好地提供数据流公平的频宽共享，对突发流响应较原算法有所提高，且算法复杂度简单，容易在高速核心路由器上实现。     

无线局域网组播优化分组算法

为了提高WLAN中组播组吞吐量,节约网络带宽,保证组播接收端整体公平性效用,提出一种组播优化分组算法。将数据获取能力和信号质量相近的终端分到一组,研究不同分组数目下公平性效用值,并分析组播组吞吐量和网络带宽占用情况。实验结果表明在最佳分组范围内,整体公平性效用值增长率变大,吞吐量和带宽占用情况比RMM （ Receivers Merging Method）算法优,性能在组播转单播和直接组播传输之间达到很好的折中。