Browsing by Author "Ribeiro, Vinay Joseph"

Now showing 1 - 20 of 25
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    Locating Available Bandwidth Bottlenecks
    (2004-09-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    The Spatio-temporal Available Bandwidth estimator (STAB), a new edge-based probing tool, locates thin links --- those links with less available bandwidth than all links preceeding them --- on end-to-end network paths. By localizing thin links, STAB facilitates network operations and troubleshooting, provides insight into what causes network congestion, and aids network-aware applications. The tool uses special chirp probing trains, featuring an exponential flight pattern of packets, which have the advantage of employing few packets while giving an accurate estimate of available bandwidth.
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    Multifractal Cross-Traffic Estimation
    (2000-09-01) Ribeiro, Vinay Joseph; Coates, Mark J.; Riedi, Rudolf H.; Sarvotham, Shriram; Hendricks, Brent; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/)
    In this paper we develop a novel model-based technique, the Delphi algorithm, for inferring the instantaneous volume of competing cross-traffic across an end-to-end path. By using only end-to-end measurements, Delphi avoids the need for data collection within the Internet. Unique to the algorithm is an efficient exponentially spaced probing packet train and a parsimonious multifractal parametric model for the cross-traffic that captures its multiscale statistical properties (including long-range dependence) and queuing behavior. The algorithm is adaptive; it requires no a priori traffic statistics and effectively tracks changes in network conditions. NS (network simulator) experiments reveal that Delphi gives accurate ross-traffic estimates for higher link utilization levels while at lower utilizations it over-estimates the cross-traffic. Also, when Delphi's single bottleneck assumption does not hold it over-estimates the cross-traffic.
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    Multifractal Signal Models with Application to Network Traffic
    (1998-08-01) Crouse, Matthew; Riedi, Rudolf H.; Ribeiro, Vinay Joseph; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, we develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics.
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    A Multifractal Wavelet Model for Positive Processes
    (1998-10-01) Crouse, Matthew; Riedi, Rudolf H.; Ribeiro, Vinay Joseph; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, we develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics.
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    A Multifractal Wavelet Model with Application to Network Traffic
    (1999-04-01) Riedi, Rudolf H.; Crouse, Matthew; Ribeiro, Vinay Joseph; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/)
    In this paper, we develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics.
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    Multiscale Queuing Analysis
    (2004-09-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We develop a new approach to queuing analysis for an infinite-length queue with constant service rate fed by an arbitrary traffic process. Our approach is particularly relevant to queues fed with long-range-dependent (LRD) traffic. We use traffic statistics at only a small fixed set of time scales and develop three approximations for the tail queue probability that are easy to implement in practice. These are non-asymptotic, that is they apply to any finite queue threshold. Simulations with LRD traffic models and Internet traces demonstrate their accuracy. Besides non-asymptotic error bounds and asymptotic decay rates for the approximations, we prove an optimality property of exponential time scales. Simulations reveal that the second-order correlation structure of traffic by itself does not determine queuing behavior and that the tails of traffic marginals at different time scales have a strong impact on queuing.
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    Multiscale Queuing Analysis
    (2006-10-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    This paper introduces a new multiscale framework for estimating the tail probability of a queue fed by an arbitrary traffic process. Using traffic statistics at a small number of time scales, our analysis extends the theoretical concept of the critical time scale and provides practical approximations for the tail queue probability. These approximations are non-asymptotic; that is they apply to any finite queue threshold. While our approach applies to any traffic process, it is particularly apt for long-range-dependent (LRD) traffic. For LRD fractional Brownian motion, we prove that a sparse exponential spacing of time scales yields optimal performance. Simulations with LRD traffic models and real Internet traces demonstrate the accuracy of the approach. Finally, simulations reveal that the marginals of traffic at multiple time scales have a strong influence on queuing that is not captured well by its global second-order correlation in non-Gaussian scenarios.
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    Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic
    (2000-03-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Crouse, Matthew; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/)
    Many studies have indicated the importance of capturing scaling properties when modeling traffic loads; however, the influence of long-range dependence (LRD) and marginal statistics still remains on unsure footing. In this paper, we study these two issues by introducing a multiscale traffic model and a novel multiscale approach to queuing analysis. The multifractal wavelet model (MWM) is a multiplicative, wavelet-based model that captures the positivity, LRD, and "spikiness" of non-Gaussian traffic. Using a binary tree, the model synthesizes an N-point data set with only O(N)computations. Leveraging the tree structure of the model, we derive a multiscale queuing analysis that provides a simple closed form approximation to the tail queue probability, valid for any given buffer size. The analysis is applicable not only to the MWM but to tree-based models in general, including fractional Gaussian noise. Simulated queuing experiments demonstrate the accuracy of the MWM for matching real data traces and the precision of our theoretical queuing formula. Thus, the MWM is useful not only for fast synthesis of data for simulation purposes but also for applications requiring accurate queuing formulas such as call admission control. Our results clearly indicate that the marginal distribution of traffic at different time-resolutions affects queuing and that a Gaussian assumption can lead to over-optimistic predictions of tail queue probability even when taking LRD into account.
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    Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic
    (2001-02-20) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Crouse, Matthew; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    This paper develops a novel approach to queuing analysis tailor-made for multiscale long-range-dependent (LRD) traffic models. We review two such traffic models, the wavelet-domain independent Gaussian model (WIG) and the multifractal wavelet model (MWM). The WIG model is a recent generalization of the ubiquitous fractional Brownian motion process. Both models are based on a multiscale binary tree structure that captures the correlation structure of traffic and hence its LRD. Due to its additive nature, the WIG is inherently Gaussian, while the multiplicative MWM is non-Gaussian. The MWM is set within the framework of multifractals, which provide natural tools to measure the multiscale statistical properties of traffic loads, in particular their burstiness. Our queuing analysis leverages the tree structure of the models and provides a simple closed-form approximation to the tail queue probability for any given queue size. This makes the WIG and MWM suitable for numerous practical applications, including congestion control, admission control, and cross-traffic estimation. The queuing analysis reveals that the marginal distribution and, in particular, the large values of traffic at different time scales strongly affect queuing. This implies that merely modeling the traffic variance at multiple time scales, or equivalently, the second-order correlation structure, can be insufficient for capturing the queuing behavior of real traffic. We confirm these analytical findings by comparing the queuing behavior of WIG and MWM traffic through simulations.
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    Network Traffic Modeling using a Multifractal Wavelet Model
    (1999-02-01) Riedi, Rudolf H.; Crouse, Matthew; Ribeiro, Vinay Joseph; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, we describe a new multiscale model for characterizing positive-valued and long-range dependent data. The model uses the Haar wavelet transform and puts a constraint on the wavelet coefficients to guarantee positivity, which results in a swift O(N) algorithm to synthesize N-point data sets. We elucidate our model's ability to capture the covariance structure of real data, study its multifractal properties, and derive a scheme for matching it to real data observations. We demonstrate the model's utility by applying it to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close match to the real data statistics (variance-time plots) and queuing behavior.
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    Network Traffic Modeling using a Multifractal Wavelet Model
    (2000-07-01) Riedi, Rudolf H.; Ribeiro, Vinay Joseph; Crouse, Matthew; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, we develop a simple and powerful multiscale model for syntheizing nonFaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have generally been restricted by a Gaussianity assumption that can be un-realistic for traffic. Using a multiplicative superstructure on top of the Haar wavelet transform, we exploit the decorrelating properties of wavelets while simultaneously capturing the positivity and "spikiness" of nonGaussian traffic. This leads to a swift O(N) algorithm for fitting and synthesizing N-point data sets. The resulting model belongs to the class of multifractal cascades, a set of processes with rich statistical properties. We elucidate our model's ability to capture the covariance structure of real data and then fit it to real traffic traces. Queueing experiments demonstrate the accuracy of the model for matching real data.
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    On the impact of variability on the buffer dynamics in IP networks
    (1999-09-20) Joo, Youngmi; Ribeiro, Vinay Joseph; Feldmann, Anja; Gilbert, Anna; Willinger, Walter; Center for Multimedia Communications (http://cmc.rice.edu/)
    The main objective of this paper is to demonstrate in the context of a simple TCP/IP-based network that depending on the underlying assumptions about the inherent nature of the variability of network traffic, very different conclusions can be derived for a number of well-studied and apparently well-understood problems in the areas of traffic engineering and management. For example, by either fully ignoring or explicitly accounting for the empirically observed variability of network traffic at the source level, we provide detailed ns-2-based simulation results for two commonly-used traffic workload scenarios that can give rise to fundamentally different buffer dynamics in IP routers. We also discuss a set of ns-2 simulation experiments to illustrate that the queuing dynamics within IP routers can be qualitatively very different depending on whether the observed variability of measured network traffic over small time scales is assumed to be in part endogenous in nature (i.e., due to TCP's feedback flow control mechanism, which is "closed loop") or is exogenously determined, resulting in an "open loop" characterization of network traffic arriving at the routers.
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    Optimal Sampling Strategies for Multiscale Models with Application to Network Traffic Estimation
    (2003-10-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    This paper considers the problem of determining which set of 2p leaf nodes on a binary multiscale tree model of depth N (N>p) gives the best linear minimum mean-squared estimator of the tree root. We find that the best-case and worst-case sampling choices depend on the correlation structure of the tree. This problem arises in Internet traffic estimation, where the goal is to estimate the average traffic rate on a network path based on a limited number of traffic samples.
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    Optimal Sampling Strategies for Multiscale Stochastic Processes
    (2004-12-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    This paper studies multiscale stochastic processes which are random processes organized on the nodes of a tree. The random variables at different levels on the tree represent time series of samples of a stochastic process at different temporal or spatial cales. We focus on classes of multiscale processes with additional statistical structure connecting scales and seek an optimal linear estimator of coarse scale nodes using an incomplete set of nodes at a finer time scale. We prove that the optimal solution for any tree with so-called independent innovations is readily given by a polynomial-time algorithm which we term the water-filling algorithm. The optimal solutions vary dramatically with the correlation structure of the multiscale process. For so-called scale-invariant trees and processes with positive correlation progression through scales, uniformly spaced leaves are optimal and clustered leaves are the worst possible. For processes with negative correlation progression, uniformly spaced leaves are the worst possible. Our results have implications for network traffic estimation, sensor network design, and environmental monitoring.
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    Optimal Sampling Strategies for Multiscale Stochastic Processes
    (2006-01-15) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper, we determine which non-random sampling of fixed size gives the best linear predictor of the sum of a finite spatial population. We employ different multiscale superpopulation models and use the minimum mean-squared error as our optimality criterion. In a multiscale superpopulation tree models, the leaves represent the units of the population, interior nodes represent partial sums of the population, and the root node represents the total sum of the population. We prove that the optimal sampling pattern varies dramatically with the correlation structure of the tree nodes. While uniform sampling is optimal for trees with "positive correlation progression," it provides the worst possible sampling with "negative correlation progression." As an analysis tool, we introduce and study a class of independent innovations trees that are of interest in their own right. We derive a fast water-filling algorithm to determine the optimal sampling of the leaves to estimate the root of an independent innovations tree.
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    PathChirp: Efficient Available Bandwidth Estimation for Network Paths
    (2003-04-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.; Navratil, Jiri; Cottrell, Les; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    This paper presents PathChirp, a new active probing tool for estimating the available bandwidth on a communication network path. Based on the concept of "self-induced congestion," PathChirp features an exponential flight pattern of probes we call a chirp. Packet chips offer several significant advantages over current probing schemes based on packet pairs or packet trains. By rapidly increasing the probing rate within each chirp, PathChirp obtains a rich set of information from which to dynamically estimate the available bandwidth. Since it uses only packet interarrival times for estimation, PathChirp does not require synchronous nor highly stable clocks at the sender and receiver. We test PathChirp with simulations and Internet experiments and find that it provides good estimates of the available bandwidth while using up to an order-of-magnitude fewer bytes than current state-of-the-art techniques.
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    Queuing analysis of long-range-dependent traffic
    (1998-04-20) Ribeiro, Vinay Joseph; Digital Signal Processing (http://dsp.rice.edu/)
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    Simulation of Non-Gaussian Long-Range-Dependent Traffic using Wavelets
    (1999-05-01) Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Crouse, Matthew; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/)
    In this paper, we develop a simple and powerful multiscale model for the synthesis of non-Gaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have generally been restricted by a Gaussianity assumption that can be unrealistic for traffic. Using a ultiplicative superstructure on top of the Haar wavelet transform, we exploit the decorrelating properties of wavelets while simultaneously capturing the positivity and "spikiness" of nonGaussian traffic. This leads to a swift O(N) algorithm for fitting and synthesizing N-point data sets. The resulting model belongs to the class of multifractal cascades, a set of processes with rich statistical properties. We elucidate our model's ability to capture the covariance structure of real data and then fit it to real traffic traces. Queueing experiments demonstrate the accuracy of the model for matching real data. Our results indicate that the nonGaussian nature of traffic has a significant effect on queuing.
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    Small-Time Scaling Behavior of Internet Backbone Traffic
    (Elsevier, 2005-06-01) Ribeiro, Vinay Joseph; Zhang, Zhi-Li; Moon, Sue; Diot, Christophe; Digital Signal Processing (http://dsp.rice.edu/)
    We perform an extensive wavelet analysis of Internet backbone traffic signals to observe and understand the causes of small-time (sub-seconds) scaling phenomena present in them. We observe that for a majority of the traffic traces, the (second-order) scaling exponents at small time scales (1ms - 100ms) are fairly close to 0.5, indicating that traffic fluctuations at these time scales are (nearly) uncorrelated. Some traces, however, do exhibit moderately large scaling exponents (approximately 0.7) at small time scales. In addition, the traces manifest mostly monofractal behaviors at small time scales. To identify the network causes of the observed scaling behavior, we analyze the flow composition of the traffic along two dimensions -- flow size and flow density. Our study points to the dense flows (i.e., flows with bursts of densely clustered packets) as the correlation-causing factor in small time scales, and reveals that the traffic composition in terms of proportions of dense vs. sparse flows plays a major role in influencing the small-time scalings of aggregate traffic. Since queuing inside routers is strongly influenced by traffic fluctuations at small time-scales, our observations and results have significant implications in networking modeling, service provisioning and traffic engineering.
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    Small-time scaling behaviors of Internet backbone traffic: An empirical study
    (2003-04-20) Zhang, Zhi-Li; Ribeiro, Vinay Joseph; Moon, Sue; Diot, Christophe; Center for Multimedia Communications (http://cmc.rice.edu/); Digital Signal Processing (http://dsp.rice.edu/)
    We study the small-time (sub-seconds) scaling behaviors of Internet backbone traffic, based on traces collected from OC3/12/48 links in a tier-1 ISP. We observe that for a majority of these traces, the (second-order) scaling exponents at small time scales (1ms - 100ms) are fairly close to 0.5, indicating that traffic fluctuations at these time scales are (nearly) uncorrelated. In addition, the traces manifest mostly monofractal behaviors at small time scales. The objective of the paper is to understand the potential causes or factors that influence the small-time scalings of Internet backbone traffic via empirical data analysis. We analyze the traffic composition of the traces along two dimensions รข flow size and flow density. Our study uncovers dense flows (i.e., flows with bursts of densely clustered packets) as the correlation-causing factor in small time scales, and reveals that the traffic composition in terms of proportions of dense vs. sparse flows plays a major role in influecing the small-time scalings of aggregate traffic.