3234740
Stochastic Ordering for Internet Congestion Control and its Applications
Window growth function for congestion control is a strong determinant of protocol behaviors, especially its second and higher-order behaviors associated with the distribution of transmission rates, its variances, and protocol stability. This paper presents a new stochastic tool, called convex ordering, that provides an ordering of any convex function of transmission rates of two protocols and valuable insights into high order behaviors of protocols. As the ordering determined by this tool is consistent with any convex function of rates, it can be applied to any unknown metric for protocol performance that consists of some high-order moments of transmission rates, as well as those already known such as rate variance. Using the tool, it is analyzed that a protocol with a growth function that starts off with a concave function and then switches to a convex function (e.g., an odd order function such as x3 and x5) around the maximum window size in the previous loss epoch, gives the smallest rate variation under a variety of network conditions. Among existing protocols, BIC and CUBIC have this window growth function. Experimental and simulation results confirm the analytical findings.
303543
Limitations of Equation-Based Congestion Control
We study limitations of an equation-based congestion control protocol, called TCP-friendly rate control (TFRC). It examines how the three main factors that determine TFRC throughput, namely, the TCP-friendly equation, loss event rate estimation, and delay estimation, can influence the long-term throughput imbalance between TFRC and TCP. Especially, we show that different sending rates of competing flows cause these flows to experience different loss event rates. There are several fundamental reasons why TFRC and TCP flows have different average sending rates, from the first place. Earlier work shows that the convexity of the TCP-friendly equation used in TFRC causes the sending rate difference. We report two additional reasons in this paper: 1) the convexity of where is a loss event period and 2) different retransmission timeout period (RTO) estimations of TCP and TFRC. These factors can be the reasons for TCP and TFRC to experience initially different sending rates. But we find that the loss event rate difference due to the differing sending rates greatly amplifies the initial throughput difference; in some extreme cases, TFRC uses around 20 times more, or sometimes 10 times less, bandwidth than TCP. Despite these factors influencing the throughput difference, we also find that simple heuristics can greatly mitigate the problem.
8691766
Media Streaming via TFRC: An Analytical Study of the Impact of TFRC on User-Perceived Media Quality
TCP-Friendly Rate Control (TFRC) is being adopted in Internet standards for congestion control of various streaming media applications. In this paper, we consider the transmission of pre-recorded media from a server to a client by using TFRC, and analytically study the impact of TFRC on user-perceived media quality, which is roughly measured by calculating the rebuffering probability. A rebuffering probability is defined to be the probability that the total duration of all rebuffering events experienced by a user is longer than a certain threshold. Two approaches are presented to help an application determine an appropriate initial buffering delay and media playback rate in order to achieve a certain rebuffering probability under a given network condition. First, we derive a closed- form expression to approximate the average TFRC sending rate, which could be used as the maximum allowed playback rate of a media stream. Second, we develop a queueing model for a TFRC client buffer with the traffic described by a Markov-Renewal- Modulated Deterministic Process (MRMDP), and present an iterative method to calculate the rebuffering probability.
18082864
Performance analysis of an ingress switch in a JumpStart optical burst switching network
We consider an ingress optical burst switching (OBS) node employing the JumpStart signaling protocol. The switch serves a number of users, each connected to the switch with a fiber link that supports multiple wavelengths. Each wavelength is associated with a 3-state Markovian burst arrival process which permits short and long bursts to be modeled. We model the ingress switch as a closed multi-class non-product-form queueing network, which we analyze approximately by decomposition. Specifically, we develop new techniques to analyze the queueing network, first assuming a single class of customers, and subsequently multiple classes of customers. These analytical techniques have applications to general queueing networks beyond the one studied in this paper. We also develop computationally efficient approximate algorithms to analyze an ingress switch in the limiting case where the number of wavelengths is large. The algorithms have a good accuracy, and they provide insight into the effect of various system parameters on the performance of an ingress OBS switch.
6958967
Dual multivariate auto-regressive modeling in state space for temporal signal separation
Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regressive (AR) process whereby an underlying multivariate AR process in observation space is obtained. In this dual AR modeling, the mixing process from temporal sources to observations is the same as the mixture from the nontemporal residuals of the source AR (SAR) process to that of the observation AR (OAR) process. We can therefore avoid the source temporal effects in performing ICA by learning the demixing system on the independently distributed OAR residuals rather than the time-correlated observations. Particularly, we implement this approach by modeling each source signal as a finite mixture of generalized autoregressive conditional heteroskedastic (GARCH) process. The adaptive algorithms are proposed to extract the OAR residuals appropriately online, together with learning the demixing system via a nontemporal ICA algorithm. The experiments have shown its superior performance on temporal source separation.
14770227
Data smoothing regularization, multi-sets-learning, and problem solving strategies
First, we briefly introduce the basic idea of data smoothing regularization, which was firstly proposed by Xu [Brain-like computing and intelligent information systems (1997) 241] for parameter learning in a way similar to Tikhonov regularization but with an easy solution to the difficulty of determining an appropriate hyper-parameter. Also, the roles of this regularization are demonstrated on Gaussian-mixture via smoothed versions of the EM algorithm, the BYY model selection criterion, adaptive harmony algorithm as well as its related Rival penalized competitive learning. Second, these studies are extended to a mixture of reconstruction errors of Gaussian types, which provides a new probabilistic formulation for the multi-sets learning approach [Proc. IEEE ICNN94 I (1994) 315] that learns multiple objects in typical geometrical structures such as points, lines, hyperplanes, circles, ellipses, and templates of given shapes. Finally, insights are provided on three problem solving strategies, namely the competition-penalty adaptation based learning, the global evidence accumulation based selection, and the guess-test based decision, with a general problem solving paradigm suggested.
2120795
Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor Model for Financial Forecast and Investment
We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.
205119948
On radial basis function nets and kernel regression: Statistical consistency, convergence rates, and receptive field size
Abstract Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) are established. By using existing theoretical results obtained for KRE as tools, we obtain a number of interesting theoretical results for RBF nets. Upper bounds are presented for convergence rates of the approximation error with respect to the number of hidden units. The existence of a consistent estimator for RBF nets is proven constructively. Upper bounds are also provided for the pointwise and L2 convergence rates of the best consistent estimator for RBF nets as the numbers of both the samples and the hidden units tend to infinity. Moreover, the problem of selecting the appropriate size of the receptive field of the radial basis function is theoretically investigated and the way this selection is influenced by various factors is elaborated. In addition, some results are also given for the convergence of the empirical error obtained by the least squares estimator for RBF nets.
8227025
A Browser Compatibility Testing Method Based on Combinatorial Testing
For ensuring the displaying effects of Web applications, it is important to perform compatibility testing for browsers under the different configurations and it is a hard task to test all. So this paper focused on the improvements for browser compatibility testing. Firstly, we introduced the related work. Then we analysed the functional speciality of the current popular browsers, and provided the preconditions to simplify problems. Next we gave an instance and brought forward the single factor covering method and the pair-wise covering design to gain testing suits. Finally, we introduced the assistant tools we have developed and the future work.