On Generalized Processor Sharing With Regulated Multimedia Traffic Flows

Multimedia traffic is becoming an increasing portion of today's Internet traffic due to the flourishing of multimedia applications such as music/video streaming, video teleconferencing, IP telephony, and distance learning. In this paper, we study the problem of supporting multimedia traffic using a generalized processor sharing (GPS) server. By examining the sample path behavior and exploring the inherent feasible ordering of the classes, we derive tight performance bounds on backlog and delay for regulated multimedia traffic classes in a GPS system. Our approach is quite general since we do not assume any arriving traffic model or any specific traffic regulator, other than that each traffic flow is deterministically regulated. Such deterministic regulators, as well as approximations of the GPS server, are widely implemented in commercial routers. In addition, our analysis is very accurate and achieves a high utilization of the server capacity, since we exploit the independence among the traffic flows for higher statistical multiplexing gains. Numerical examples and simulation results are presented to demonstrate the accuracy and merits of our approach, which is practical and well suited for supporting multimedia applications in the Internet     

Content-aware rate allocation for efficient video streaming via dynamic network utility maximization

Nowadays it is vital to design robust mechanisms to provide QoS for multimedia applications as an integral part of the network traffic. The main goal of this paper is to provide an efficient rate control scheme to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in congested networks. Towards this, we introduce a content-aware time-varying utility function, in which the quality impact of video content is incorporated into its mathematical expression. Moreover, we analytically model the buffer requirements of video sources in two ways: first as constraints of the optimization problem to guarantee a minimum rate demand for each source, and second as a penalty function embedded as part of the objective function attempting to achieve the highest possible rate for each source. Then, using the proposed analytical model, we formulate a dynamic network utility maximization problem, which aims to maximize the aggregate hybrid objective function of sources subject to capacity and buffer constraints. Finally, using primal–dual method, we solve DNUM problem and propose a distributed algorithm called CA-DNUM that optimally allocates the shared bandwidth to video streams. The experimental results demonstrate the efficacy and performance improvement of the proposed content-aware rate allocation algorithm for video sources in different scenarios.     

Self-similar cross-traffic analysis as a foundation for choosing among active available bandwidth measurement strategies

Among the many active, available bandwidth estimation tools and methods that have appeared recently in the literature, none has emerged that is accurate and robust enough to function in different networking environments. One of the main reasons for this problem is the highly variable statistical nature of network traffic. We present a novel theoretical method for active measurement strategy analysis through self-similar process sampling analysis. The results obtained show a relationship between the main parameters in the measurement procedure, such as the number of samples, sample length, and sample distance. The study suggests that accurate measurements require the use of many samples and thus long measurement procedures, sometimes on the order of several minutes, depending on the amount of cross traffic. Additionally, through the analysis of several existing Internet packet traces, we recommend that the minimum single sample probe stream length must be longer than the average cross traffic interarrival. Following these findings, we propose a new method for available bandwidth estimation that has shown significant accuracy under different network setups.     

Bandwidth Management for Supporting Differentiated Service Aware Traffic Engineering

This paper presents a bandwidth management framework for the support of differentiated-service-aware traffic engineering (DS-TE) in multiprotocol label switching (MPLS) networks. Our bandwidth management framework contains both bandwidth allocation and preemption mechanisms in which the link bandwidth is managed in two dimensions: class type (CT) and preemption priority. We put forward a Max-Min bandwidth constraint model in which we propose a novel "use it or lend it" strategy. The new model is able to guarantee a minimum bandwidth for each CT without causing resource fragmentation. Furthermore, we design three new bandwidth preemption algorithms for three bandwidth constraint models, respectively. An extensive simulation study is carried out to evaluate the effectiveness of the bandwidth constraint models and preemption algorithms. When compared with the existing constraint models and preemption rules, the proposed Max-Min constraint model and preemption algorithms improve not only bandwidth efficiency, but also robustness and fairness. They achieve significant performance improvement for the well-behaving traffic classes in terms of bandwidth utilization and bandwidth blocking and preemption probability. We also provide guidelines for selecting different DS-TE bandwidth management mechanisms.     

An ISP-friendly inter-overlay coordination framework for multiple coexisting P2P systems

As Peer-to-Peer (P2P) systems are widely deployed in the Internet, P2P traffic control becomes a challenge for Internet Service Providers (ISPs) and P2P system vendors. Some recent works consider the interaction between ISPs and P2P systems, and propose ISP-friendly P2P traffic control mechanisms for reducing cross-ISP P2P traffic. In this paper, we consider another fundamental problem: the performance optimization and the interaction among multiple coexisting P2P systems. Specifically, we propose an ISP-friendly inter-overlay coordination framework (COOD) for controlling P2P traffic. The COOD framework consists of three important components: network traffic optimization, P2P service differentiation, and ISP policy enforcement. Our packet-level simulation results show that, compared with current P2P traffic control mechanisms, the COOD framework can provide better overall performance to multiple coexisting P2P systems, achieve service differentiation, and implement flexible mechanisms to effectively control cross-ISP P2P traffic.     

宽带卫星网络中基于跨层设计的带宽分配研究

传统的带宽分配策略按照分层结构进行设计,集中在链路层解决问题,没有考虑动态变化的信道状态和所承载的数据流的QoS性能。基于跨层设计的思想,针对支持话音业务和Internet数据业务的卫星网络提出了一种基于跨层设计的带宽分配算法。该策略将应用层的业务特性和数据链路层的带宽分配策略以及物理层的信道状态进行跨层优化。主控站通过建立代价函数的方法将所有相关参数综合考虑,利用动态规划算法得到了最佳的带宽分配方案。数值结果表明：跨层设计方式可以适应变化的信道状态,并且同传统带宽分配算法比较,提高了Internet数据的有效吞吐量并且保持了话音业务的QoS要求。     

基于广义细胞自动机的网络带宽优化

Nowadays,bandwidth allocation schemes in a TCP/IP or ATM network are congestion avoidance oriented.Few scheme has taken global optimization into account,for global optimization problem can not be easily solved by conventional mathematical method due to the complexity and large-scale of massive information system,such as Internet.We present a novel bandwidth allocation scheme based on generalized cellular automaton (GCA).Firstly we introduce how to map network topology into GCA model,then we propose how cells and macro cells interact in our solution.Our simulation results show the scheme leads to global optimization rapidly.     

Large margin hidden Markov models for speech recognition

In this paper, motivated by large margin classifiers in machine learning, we propose a novel method to estimate continuous-density hidden Markov model (CDHMM) for speech recognition according to the principle of maximizing the minimum multiclass separation margin. The approach is named large margin HMM. First, we show this type of large margin HMM estimation problem can be formulated as a constrained minimax optimization problem. Second, we propose to solve this constrained minimax optimization problem by using a penalized gradient descent algorithm, where the original objective function, i.e., minimum margin, is approximated by a differentiable function and the constraints are cast as penalty terms in the objective function. The new training method is evaluated in the speaker-independent isolated E-set recognition and the TIDIGITS connected digit string recognition tasks. Experimental results clearly show that the large margin HMMs consistently outperform the conventional HMM training methods. It has been consistently observed that the large margin training method yields significant recognition error rate reduction even on top of some popular discriminative training methods.     

基于分布感知的Web服务组合方法研究

提出了一种分布感知的Web 服务组合方法。移动Agent代理Web 服务,构成一个自治的感知单元,移动Agent通过语义的协商确认匹配关系,Web 服务组合可以通过移动Agent的演化完成。常见的遗传组合算法存在着复杂路径表示和罚函数难于确定的局限性,采用感知基础上的服务关系矩阵和服务相对质量的矩阵,最优服务组合的建立转化为带约束的最优化问题,并给出不带参数的罚函数动态演化算法解决方案,仿真表明这种方法可以提高适应度和性能。     

Traffic prediction for dynamic traffic engineering

Traffic engineering with traffic prediction is a promising approach to accommodate time-varying traffic without frequent route changes. In this approach, the routes are decided so as to avoid congestion on the basis of the predicted traffic. However, if the range of variation including temporal traffic changes within the next control interval is not appropriately decided, the route cannot accommodate the shorter-term variation and congestion still occurs. To solve this problem, we propose a prediction procedure to consider the short-term and longer-term future traffic demands. Our method predicts the longer-term traffic variation from the monitored traffic data. We then take account of the short-term traffic variation in order to accommodate prediction uncertainty incurred by temporal traffic changes and prediction errors. We use the standard deviation to estimate the range of short-term fluctuation. Through the simulation using actual traffic traces on a backbone network of Internet2, we show that traffic engineering using the traffic information predicted by our method can set up routes that accommodate traffic variation for several or more hours with efficient load balancing. As a result, we can reduce the required bandwidth by 18.9% using SARIMA with trend component compared with that of the existing traffic engineering methods.     

User-level performance of elastic traffic in a differentiated-services environment

We consider a system with two service classes, one of which supports elastic traffic. The traffic characteristics of the other class can be completely general, allowing streaming applications as an important special case. The link capacity is shared between the two traffic classes in accordance with the generalized processor sharing (GPS) discipline. GPS-based scheduling algorithms, such as weighted fair queuing, provide a flexible mechanism for service differentiation and prioritization.

We examine the user-level performance of the elastic traffic. The elastic traffic users randomly initiate file transfers with a heavy-tailed distribution. Within the elastic traffic class, the active flows share the available bandwidth in an ordinary processor-sharing (PS) fashion. The PS discipline has emerged as a natural paradigm for evaluating the user-perceived performance of bandwidth sharing algorithms like TCP. For a certain parameter range, we establish that the transfer delay incurred by the elastic traffic flows is asymptotically equivalent to that in an isolated PS system with constant service rate. This service rate is only affected by the streaming traffic through its average rate. Specifically, the elastic traffic is largely immune from possible adverse traffic characteristics or performance degradation due to prioritization of the streaming traffic. This confirms that GPS-based multiplexing mechanisms achieve significantly better performance for both traffic classes than a static bandwidth partitioning approach.     

自适应惩罚策略及其在交通信号优化中的应用

针对约束优化问题的求解,设计了一种处理约束条件的自适应惩罚策略,用于将具有不等式约束和等式约束的优化问题转变为仅包含决策变量上、下限约束的优化问题。该策略通过引入约束可行测度、可行度的概念来描述决策变量服从于不等式约束和等式约束的程度,并以此构造处理约束条件的自适应惩罚函数,惩罚值随着约束可行度的变化而动态自适应地改变。为了检验该惩罚策略的有效性,针对单路口交通信号优化问题进行了应用研究,并用三种不同算法进行了大量的仿真计算,结果表明所设计的自适应策略在具有高度约束条件的城市交通信号优化问题中具有良好的效果。     

Optimizing bandwidth allocation for heterogeneous traffic in IoT

In Internet of Things (IoT), there generally exist two typical types of heterogeneous flows with different requirements: inelastic media flow (e.g., the monitoring flow) and elastic data flow (e.g., the environment info from measurement). These heterogeneous flows often coexist for sharing the limited bandwidth. Despite substantial works, a simple yet efficient approach to economically allocate bandwidth for these two types of flows is still not available. In this paper, we propose two methods to optimize the bandwidth allocation of these flows: the network-utility-maximization-based (NUM) method and the asymptotic analysis method. The NUM method provides a general solution to the optimization problem, but requires a certain computational complexity. The asymptotic analysis method delves into the inherent property of the network and explicitly expresses the solution in terms of protocol parameters and traffic requirements. Extensive simulations verify that the two methods are very accurate and can well achieve the desired objectives.     

Neural network approach for allocation with capacity

In this paper we discuss neural network approach for allocation with capacity constraints problem. This problem can be formulated as zero-one integer programming problem. We transform this zero-one integer programming problem into an equivalent nonlinear programming problem by replacing zero-one constraints with quadratic concave equality constraints. We propose two kinds of neural network structures based on penalty function method and augmented Lagrangian multiplier method, and compare them by theoretical analysis and numerical simulation. We show that penalty function based neural network approach is not good to combinatorial optimization problem because it falls in the dilemma whether terminating at an infeasible solution or sticking at any feasible solution, and augmented Lagrangian multiplier method based neural network can alleviate this suffering in some degree.     

Harmonic proportional bandwidth allocation and scheduling for service differentiation on streaming servers

To provide ubiquitous access to the proliferating rich media on the Internet, scalable streaming servers must be able to provide differentiated services to various client requests. Recent advances of transcoding technology make network-I/O bandwidth usages at the server communication ports controllable by request schedulers on the fly. In this article, we propose a transcoding-enabled bandwidth allocation scheme for service differentiation on streaming servers. It aims to deliver high bit rate streams to high priority request classes without overcompromising low priority request classes. We investigate the problem of providing differentiated streaming services at application level in two aspects: stream bandwidth allocation and request scheduling. We formulate the bandwidth allocation problem as an optimization of a harmonic utility function of the stream quality factors and derive the optimal streaming bit rates for requests of different classes under various server load conditions. We prove that the optimal allocation, referred to as harmonic proportional allocation, not only maximizes the system utility function, but also guarantees proportional fair sharing between classes with different prespecified differentiation weights. We evaluate the allocation scheme, in combination with two popular request scheduling approaches, via extensive simulations and compare it with an absolute differentiation strategy and a proportional-share strategy tailored from relative differentiation in networking. Simulation results show that the harmonic proportional allocation scheme can meet the objective of relative differentiation in both short and long timescales and greatly enhance the service availability and maintain low queueing delay when the streaming system is highly loaded.     

An augmented Lagrangian decomposition method for quasi-separable problems in MDO

Several decomposition methods have been proposed for the distributed optimal design of quasi-separable problems encountered in Multidisciplinary Design Optimization (MDO). Some of these methods are known to have numerical convergence difficulties that can be explained theoretically. We propose a new decomposition algorithm for quasi-separable MDO problems. In particular, we propose a decomposed problem formulation based on the augmented Lagrangian penalty function and the block coordinate descent algorithm. The proposed solution algorithm consists of inner and outer loops. In the outer loop, the augmented Lagrangian penalty parameters are updated. In the inner loop, our method alternates between solving an optimization master problem and solving disciplinary optimization subproblems. The coordinating master problem can be solved analytically; the disciplinary subproblems can be solved using commonly available gradient-based optimization algorithms. The augmented Lagrangian decomposition method is derived such that existing proofs can be used to show convergence of the decomposition algorithm to Karush–Kuhn–Tucker points of the original problem under mild assumptions. We investigate the numerical performance of the proposed method on two example problems.     

Optimal routing in multiple I/O data network using neural network with perturbed energy function

An optimal routing problem in multiple I/O data network is one of the most important problems related on the performance of the network basically, and is formulated as a constrained nonlinear optimization problem. When solving the problem considered using neural networks, we may obtain local minima, rather than global minimum, because the problem has multimodals. In this paper, we introduce a perturbed energy function into the neural network based on a penalty method to solve the multimodal nonlinear optimization problem.     

Asymptotic buffer overflow probabilities in multiclassmultiplexers: an optimal control approach

We consider a multiclass multiplexer with support for multiple service classes and dedicated buffers for each service class. Under specific scheduling policies for sharing bandwidth among these classes, we seek the asymptotic (as the buffer size goes to infinity) tail of the buffer overflow probability for each dedicated buffer. We assume dependent arrival and service processes as is usually the case in models of bursty traffic. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We introduce a novel optimal control approach to address these problems. In particular, we relate the lower bound derivation to a deterministic optimal control problem, which we explicitly solve. Optimal state trajectories of the control problem correspond to typical congestion scenarios. We explicitly and in detail characterize the most likely modes of overflow. We specialize our results to the generalized processor sharing policy (GPS) and the generalized longest queue first policy (GLQF). The performance of strict priority policies is obtained as a corollary. We compare the GPS and GLQF policies and conclude that GLQF achieves smaller overflow probabilities than GPS for all arrival and service processes for which our analysis holds. Our results have important implications for traffic management of high-speed networks and can be used as a basis for an admission control mechanism which guarantees a different loss probability for each class     

Methodologies for bandwidth allocation,transmission scheduling,and congestion avoidance in broadband ATM networks

The call types supported in high-speed packet networks vary widely in their bandwidth requirements and tolerance to message delay and loss. In this paper, we classify various traffic sources which are likely to be integrated in broadband ATM networks, and suggest schemes for bandwidth allocation and transmission scheduling to meet the quality and performance objectives. We propose ATM cell-multiplexing using a Dynamic Time-Slice (DTS) scheme which guarantees a required bandwidth for each traffic class and/or virtual circuit (VC), and is dynamic in that it allows the different traffic classes or VCs to share the bandwidth with a soft boundary. Any bandwidth momentarily unused by a class or a VC is made available to the other traffic present in the multiplexer. The scheme guarantees a desired bandwidth to connections which require a fixed wide bandwidth. Thus, it facilitates setting up circuit-like connections in a network using the ATM protocol for transport. The DTS scheme is an efficient way of combining constant bit-rate (CBR) services with variable bit-rate (VBR) stastically multiplexed services. We also described methodologies to schedule delivery of delay-tolerant data traffic within the framework of the DTS scheme. Important issues such as buffer allocations, guarantee of service quality, and ease of implementation are also discussed.     

并行量子遗传算法在QoS组播路由中的应用

随着网络通信技术的发展和Internet的普及,性能出色的组播路由越来越重要。著名的组播路由Steiner树问题是NP完全问题,应采用启发式方法求解。文中在常规量子遗传算法中引入并行进化模型,提出了一种解决多约束QoS组播路由优化问题的算法。在满足带宽、时延约束条件下寻找代价最小的组播树,并合理安排节点负荷,减少通信开销。仿真实验结果表明本算法搜索速度快、全局寻优能力强,性能和效率优于常规量子遗传算法。