Inter-autonomous system provisioning for end-to-end bandwidth guarantees

This paper addresses the issue of provisioning end-to-end bandwidth guarantees across multiple Autonomous Systems (ASes). We first review a cascaded model for negotiating and establishing service level agreements for end-to-end bandwidth guarantees between ASes. We then present a network dimensioning system that uses traffic engineering mechanisms for the provisioning of end-to-end bandwidth guarantees. The network dimensioning system solves two problems: (1) the economic problem of how to determine the optimum amount of bandwidth that needs to be purchased from adjacent downstream ASes at a minimum total cost; (2) given the available bandwidth resources within and beyond the AS as a result of (1), the engineering problem of how to assign bandwidth guaranteed routes to the predicted traffic while optimizing the network resource utilization. We formulate both as integer-programming problems and prove them to be NP-hard. An efficient genetic algorithm and an efficient greedy-penalty heuristic are, respectively, used to solve the two problems and we show that these perform significantly better than simple heuristic and random approaches.     

Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX

With fast proliferation of QoS-enabled wireless packet networks, need for effective QoS control is increasing. In this paper, we focus on QoS provisioning in Mobile WiMAX access service network (ASN). We investigate a dynamic bandwidth provisioning method that can help to increase resource utilization. Our approach consists of two stages: traffic forecasting, followed by bandwidth provisioning. For the first stage, we use auto-regressive integrated moving average (ARIMA) model to forecast traffic based on online measurement. For the second stage, we use a bandwidth provisioning scheme that allocates bandwidths depending on the traffic forecasting. We modeled our problem as a Fractional Knapsack Problem for which we used a greedy algorithm in order to find an approximate solution. Through simulation studies with real-world data sets, we found that our approach could increase the bandwidth for the real-time traffic class and guarantee adequate service quality for the nonreal-time traffic class as well, while maximizing resource utilization.     

Dynamic bandwidth allocation for Internet telephony

Internet telephony is promising for long-distance calls because of its low service charge and value-added functions. To provide Internet telephony to the general public, a service provider can operate a telephone gateway in each servicing city to bridge the local telephone network and the Internet, so that users can use telephones or fax machines to access this gateway for services. In this paper, we propose a dynamic bandwidth allocation scheme for two purposes: (1) each telephone gateway can fully utilize the available bandwidth to serve more telephone and fax sessions and (2) it can respond to the changing environments. We exploit three properties for dynamic bandwidth allocation. First, in a telephone session, each user usually alternates between speaking and listening. When a user is not speaking, she does not send any voice stream and hence the bandwidth can be dynamically released from this session for the other sessions. Second, voice traffic is elastic because it can be further compressed at the cost of a lower quality. Third, fax traffic is flexible because it can be temporarily delayed. We exploit these three properties to allocate bandwidth to telephone and fax sessions dynamically. When a telephone gateway adopts dynamic bandwidth allocation, it can serve more telephone and fax sessions while providing acceptably good quality-of-service (QoS), and it can give more stable QoS when the available bandwidth varies.     

一种实用化接入段全带宽动态分配方案

针对接入段多业务非均匀突发性特点,提出了一种实用的全带宽动态分配方案,该方案允许单业务最大可用带宽超出其公平共享范围。分析和实践结果表明,在保证带宽利用率、公平性和满足用户QoS指标的同时,该方案可使有需求链路在系统处于非饱和状态时占用全部剩余带宽(直至全带宽),大大提高系统的总带宽利用率。     

Autonomic resource provisioning for multilayer cloud applications with K-nearest neighbor resource scaling and priority-based resource allocation

Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto-scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale-up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE-K loop to auto-scale the resources for multilayered cloud applications. K-nearest neighbor (K-NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.     

一类分布参数动态系统的周期性分析

通过构造适当的Lyapunov泛函、利用M矩阵性质和不等式技巧, 在不要求神经网络激励函数的有界性、单调性和可微性弱保守条件下, 探讨了一类具有分布参数和分布时滞的Cohen-Grossberg动态神经网络周期解的存在性和指数稳定性问题, 提出了一系列充分性判据来确保这类同时具有分布参数和分布时滞神经网络周期解的存在性和指数稳定性, 并通过几个注解以及与其他文献结果进行比较说明了该方法的优越性. 最后, 给出了数值例子和计算机仿真来验证这一理论的有效性.     

DelPhi环境下基于ADO对象动态创建和连接加密的Access数据库

介绍了在用Delphi开发Access数据库应用程序过程中,会碰到当将应用程序安装到不同的机器上,由于数据环境改变导致数据库不能正常连接,致使程序无法运行的问题;以及如何动态的对数据库进行加密和连接等问题.结合实例详细阐述了ADO、ADOX接口的用法以及如何动态创建和连接加密的Access数据库,较好的解决了以上存在的问题.     

Delphi环境下基于ADO对象动态创建和连接加密的Access数据库

介绍了在用Delphi开发Access数据库应用程序过程中,会碰到当将应用程序安装到不同的机器上,由于数据环境改变导致数据库不能正常连接,致使程序无法运行的问题;以及如何动态的对数据库进行加密和连接等问题。结合实例详细阐述了ADO、ADOX接口的用法以及如何动态创建和连接加密的Access数据库,较好的解决了以上存在的问题。     

Competitive online adaptive scheduling for sets of parallel jobs with fairness and efficiency

We study online adaptive scheduling for multiple sets of parallel jobs, where each set may contain one or more jobs with time-varying parallelism. This two-level scheduling scenario arises naturally when multiple parallel applications are submitted by different users or user groups in large parallel systems, where both user-level fairness and system-wide efficiency are of important concerns. To achieve fairness, we use the well-known equi-partitioning algorithm to distribute the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler that periodically adjusts the processor allocations within each set by consciously exploiting the jobs’ execution history. We show that our algorithm achieves asymptotically competitive performance with respect to the set response time, which incorporates two widely used performance metrics, namely, total response time and makespan, as special cases. Both theoretical analysis and simulation results demonstrate that our algorithm improves upon an existing scheduler that provides only fairness but lacks efficiency. Furthermore, we provide a generalized framework for analyzing a family of scheduling algorithms based on feedback-driven policies with provable efficiency. Finally, we consider an extended multi-level hierarchical scheduling model and present a fair and efficient solution that effectively reduces the problem to the two-level model.     

随机延迟网络控制系统中的分段时戳动态矩阵控制

针对网络控制系统中小于一个采样周期的随机延迟,引入了分段时戳动态矩阵控制算法.通过时戳方法测量网络延迟,在线校正系统的阶跃响应系数向量和控制系数向量,并采用分段算法来减少所需的在线计算量.给出了算法的推导过程和程序实现方法,并基于实时控制系统仿真平台TrueTime进行仿真研究.应用该算法对共享以太网中直流电机进行控制,取得了比标准动态矩阵控制算法更好的控制品质.     

具有磁滞输入非线性系统的鲁棒自适应控制

就一类具有磁滞输入的严反馈非线性系统, 提出了一种鲁棒自适应动态面控制方案. 该方案可克服传统反推控制带来的“微分爆炸”问题, 保证闭环系统的半全局稳定性, 且跟踪误差可收敛到任意小的残集内. 特别地, 通过引入动态面修正及初始化技巧, 可保证系统跟踪误差的L∞ 性能指标. 数值仿真验证了本文所提方法案的有效性.     

Dual segment rectangular dielectric resonator antenna with metamaterial for improvement of bandwidth and gain

The simulation and experimental studies of an aperture‐coupled wideband dual segment rectangular dielectric resonator antenna with metamaterial for C‐band applications are presented in this paper. The antenna consists of Alumina (Al₂O₃) ceramic as upper segment and Teflon as lower segment. The combination of circular‐shaped coplanar split‐ring resonator and conducting strip has been used as metamaterial superstrate. With the use of metamaterial superstrate, the bandwidth of the antenna is increased by 48% through simulation and 22% experimentally. The broadside radiation pattern of the antenna is converted into directive radiation pattern with reduced beamwidth when metamaterial superstrate is used. The peak gain of the antenna is also enhanced by 33% through simulation and 31% experimentally with the use of metamaterial superstrate. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:646–655, 2014.     

动态定价策略下的精确库存成本建模与优化

提出一种更接近实际的需求率公式,在式中同时考虑了价格和出厂时间对客户需求率的影响.基于新的需求率公式,建立了动态定价策略下的精确库存持有成本模型和库存商品的利润函数.注意到利润函数的复杂性,使用遗传算法分析了利润函数的性质,得出最优定价时间、定价价格和最大利润的关系,并分析了库存持有成本变化和消费者购买欲望变化对各定价参数的影响.     

A multiple parallel download scheme with server throughput and client bandwidth considerations for data grids

Though computer technology advances quickly, the computing speed and storage capacity of a single computer still cannot satisfy the requirements of many applications. As a result, grids have emerged to utilize the collective power of many computers. One of them, the data grid, provides a mechanism for handling a large amount of data. One of the characteristics of a data grid is to replicate files to many different computers such that a popular file would be more available. If a grid site does not have a file, it will have to download it from other grid sites. Thus, the parallel download method, which allows a user to download different parts of a file from various computers simultaneously, is used to decrease download time.However, multiple parallel downloads will affect one another. Thus if all jobs in the grid system use parallel download, the problem of resource competition and conflict will happen. In this paper, we propose a parallel download scheme considering the server output throughput limits and client input bandwidth constraints. Experimental results show that the proposed download scheme outperforms static and dynamic parallel download schemes.     

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems: Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises, multiple missing measurements as well as the dynamic event-triggered transmission scheme. The multiple missing measurements are characterized through random variables that obey some given probability distributions, and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable. Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense. To this end, the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers, thus the original design issue is reformulated as that of the augmented system. Subsequently, we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters. With the aid of two well-defined matrix difference equations, we not only obtain upper bounds on filtering error covariances, but also minimize those bounds via carefully designing gain parameters. Finally, an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

     

结合SVD_ TLS及EKF算法的动态自组织模糊神经网络在动态系统中的应用

针对如何优化模糊神经网络的规则及如何合理地调整非线性参数及线性参数等问题,提出了将奇异值分解_总体最小二乘法(SVD_TLS)及扩展卡尔曼滤波(EKF)相结合的动态自组织模糊神经网络(STD_DSFNN)。首先给出了STD DSFNN的结构及各层的含义;其次,用EKF算法学习非线性参数,SVD_TLS算法学习线性参数的同时提取重要模糊规则;最后,通过典型的Machey-Ulass时间序列预测实例验证SVD_TLS及EKF相结合的动态自组织模糊神经网络(STE_DSFNN),同时与DFNN, ANFIS及UKF_DFNN相对比,结果表明STE DSFNN网络结构更紧凑,具有更好的泛化能力。     

Adaptive Dynamic Surface Control with Guaranteed L∞ Tracking Performance for a Class of Uncertain Nonlinear Systems with Unknown Time Delays

This paper is aimed at exploring dynamic surface control (DSC) for a class of uncertain nonlinear systems in strict‐feedback form with time delays. Combining the Finite Covering Lemma (Heine‐Borel Theorem) with neural networks, a novel method is proposed to approximate time delay terms, which leads to the abandonment of traditional Lyapunov‐Krasovskii functionals. Then, a surface error modification and an initialization technique are proposed to guarantee the tracking performance. Moreover, by applying a newly‐developed neural network based adaptive control technique, it is shown that the update law for the proposed DSC scheme is needed only at the last design step with only one parameter being estimated online, which significantly reduces the computational burden, compared with current DSC schemes. Simulation results are presented to illustrate the efficiency of the proposed scheme.     

Return Link Systems for Wireless Mobile Camera Using 42 GHz-Band with Frequency and Space Diversity Techniques

In this paper, return link systems for wireless mobile camera using 42 GHz-band in multipath fading environments are proposed. The return link systems for wireless mobile camera require wireless transmission with high reliability as same as the conventional wired camera systems. The proposed return link systems achieve transmission with high reliability by taking into consideration frequency and space diversity techniques. The proposed systems can be classified as 3 three types of return link systems according to diversity techniques： FD （frequency diversity） systems, SD （space diversity） systems and FD＋SD system （frequency and space diversity）. Computer simulations are carried out in order to confirm the effectiveness of the proposed return link systems. It is confirmed that the proposed three types of return link systems have an advantage about BER performances and effectively exploit frequency and space resources.     

组合概率信息的复杂动态系统层次故障检诊方法

基于对系统故障的功能性特征的考虑和对子系统模态的精确定义,提出一种构造系统诊断用的层次模型的方法,并由此提出一种在此模型中层次地推进检诊过程的方法,基本诊断手段采用de kleer的GDE通用诊断推理机.层次的诊断方式极大地提高了诊断的效率,子系统模态的适当定义保证了层次间界限分明,消除了诊断算法在不同层次间可能的重复,产生了简炼的层次间概率传递公式.     

A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting

A new Neuro-Fuzzy Inference System with Dynamic Neurons or NFIS-DN is presented here for discrete time dynamic system identification and time series forecasting problems. The proposed dynamic system based neuron, referred to as Dynamic Neuron (DN) is realized by a discrete-time nonlinear state-space model. The DN is designed such way, that the output considers only the effect of finite past instances, enabling the system with finite memory. The NFIS-DN model has five layers, and DNs are employed only in the layers handling crisp values. The antecedent and the consequent parameters of NFIS-DN are updated using a self-regulated backpropagation through time learning algorithm. The performance evaluation of NFIS-DN has been carried-out using benchmark problems in the areas of nonlinear system identification and time series forecasting. The results are compared with the state-of-the-art method on the neural fuzzy networks. The obtained results clearly suggest that the NFIS-DN performs significantly better while using a smaller or similar number of fuzzy rules. Finally the practical application of the NFIS-DN has been demonstrated using two real-world problems.