9335089
Long-term forecasting of Internet backbone traffic: observations and initial models
We introduce a methodology to predict when and where link additions/upgrades have to take place in an IP backbone network. Using SNMP statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent PoPs and look at its evolution at time scales larger than one hour. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis and linear time series models. Using wavelet multiresolution analysis, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12 hour time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12 hour time scale yields accurate estimates for at least six months in the future.
14141879
Improving VoIP quality through path switching
The current best-effort Internet cannot readily provide the service guarantees that VoIP applications often require. Path switching can potentially address this problem without requiring new network mechanisms, simply by leveraging the robustness to performance variations available from connectivity options such as multi-homing and overlays. In this paper, we evaluate the effectiveness and benefits of path switching in improving the quality of VoIP applications, and demonstrate its feasibility through the design and implementation of a prototype gateway. We argue for an application-driven path switching system that accounts for both network path characteristics and application-specific factors (e.g., codec algorithms, playout buffering schemes). We also develop an application path quality estimator based on the ITU-T E-model for voice quality assessment, and an application-driven path switching algorithm that dynamically adapts the time scales over which path switching decisions are made to maximize voice quality. Through network emulation and experiments over a wide-area multi-homed test bed, we show that, with sufficient path diversity, path switching can yield meaningful improvements in voice quality. Hence by exploiting the inherent path diversity of the Internet, application-driven path switching is a viable option in providing quality-of-service to applications.
3065647
A core stateless bandwidth broker architecture for scalable support of guaranteed services
We present a novel bandwidth broker architecture for scalable support of guaranteed services that decouples the QoS control plane from the packet forwarding plane. More specifically, under this architecture, core routers do not maintain any QoS reservation states, whether per-flow or aggregate. Instead, the QoS reservation states are stored at and managed by a bandwidth broker. There are several advantages of such a bandwidth broker architecture. Among others, it avoids the problem of inconsistent QoS states faced by the conventional hop-by-hop, distributed admission control approach. Furthermore, it allows us to design efficient admission control algorithms without incurring any overhead at core routers. The proposed bandwidth broker architecture is designed based on a core stateless virtual time reference system developed recently. This virtual time reference system provides a unifying framework to characterize, in terms of their abilities to support delay guarantees, both the per-hop behaviors of core routers and the end-to-end properties of their concatenation. We focus on the design of efficient admission control algorithms under the proposed bandwidth broker architecture. We consider both per-flow end-to-end guaranteed delay services and class-based guaranteed delay services with flow aggregation. Using our bandwidth broker architecture, we demonstrate how admission control can be done on a per domain basis instead of on a "hop-by-hop" basis. Such an approach may significantly reduce the complexity of the admission control algorithms. In designing class-based admission control algorithms, we investigate the problem of dynamic flow aggregation in providing guaranteed delay services and devise a new apparatus to effectively circumvent this problem. We conduct detailed analyses to provide theoretical underpinning for our schemes as well as to establish their correctness. Simulations are also performed to demonstrate the efficacy of our schemes.
33893889
Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing
Variable-bit-rate (VBR) compressed video can exhibit significant multiple-time-scale bit-rate variability. In this paper we consider the transmission of stored video from a server to a client across a network, and explore how the client buffer space can be used most effectively toward reducing the variability of the transmitted bit rate. Two basic results are presented. First, we show how to achieve the greatest possible reduction in rate variability when sending stored video to a client with given buffer size. We formally establish the optimality of our approach and illustrate its performance over a set of long MPEG-1 encoded video traces. Second, we evaluate the impact of optimal smoothing on the network resources needed for video transport, under two network service models: deterministic guaranteed service (Chang 1994; Wrege et al. 1996) and renegotiated constant-bit-rate (RCBR) service (Grossglauser et al. 1997). Under both models, the impact of optimal smoothing is dramatic.
14053219
Distributed Algorithm for Service Replication in Service Overlay Network
The service overlay network (SON) is an effective mean to deploy end-to-end QoS guaranteed content delivery services on the current Internet. We model the content delivery service on a SON as a service delivery tree (SDT). Previous studies have addressed the optimal distribution tree formulation issues. In this paper, we focus on the problem of maximizing the total operation profit of the SON. In [1], authors introduced a cost model for optimal bandwidth provisioning in SON. In this paper, we extended this concept and propose an alternative approach to maximize the total effective throughput of SDT as well as to minimize the QoS violation penalty of the SON by service replication. We present both centralized and distributed algorithms for the placement of replicated servers on the SDT. Experiments are carried to quantify the merit, effectiveness and the scalability of the proposed service replication algorithm. In particular, the performance gain is very close to the exhaustive search. The algorithm performs well when we scale up the service overlay networks. Finally, we show that one only needs to perform a small number of replications to attain the optimal gain.
5876943
Adaptive packet sampling for flow volume measurement
Traffic measurement and monitoring are an important component of network management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement and monitoring are not only desirable, but also increasingly becoming a necessity. Since the utility of sampling depends on the accuracy and economy of measurement, it is important to control sampling error. In this paper we propose and analyze an adaptive, stratified random packet sampling technique for flow-level traffic measurement. In particular, we address the theoretical and practical issues involved. Through theoretical studies and experiments, we demonstrate that the proposed sampling technique provides unbiased estimation of flow size with controllable error bound, in terms of both packet and byte counts for elephant flows, while avoiding excessive oversampling.
5688571
Enhancing Downlink Performance in Wireless Networks by Simultaneous Multiple Packet Transmission
In this paper, we consider using simultaneous Multiple Packet Transmission (MPT) to improve the downlink performance of wireless networks. With MPT, the sender can send two compatible packets simultaneously to two distinct receivers and can double the throughput in the ideal case. We formalize the problem of finding a schedule to send out buffered packets in minimum time as finding a maximum matching problem in a graph. Since maximum matching algorithms are relatively complex and may not meet the timing requirements of real-time applications, we give a fast approximation algorithm that is capable of finding a matching at least 3/4 of the size of a maximum matching in O(|E|) time, where |E| is the number of edges in the graph. We also give analytical bounds for maximum allowable arrival rate, which measures the speedup of the downlink after enhanced with MPT, and our results show that the maximum arrival rate increases significantly even with a very small compatibility probability. We also use an approximate analytical model and simulations to study the average packet delay, and our results show that packet delay can be greatly reduced even with a very small compatibility probability.
26146425
An On-Line Universal Lossy Data Compression Algorithm via Continuous Codebook Refinement - Part III: Redundancy Analysis
For pt.II see ibid., vol.42, p.822-36 (1996). The Gold-washing data compression algorithm is an adaptive vector quantization algorithm with vector dimension n. In this paper, a redundancy problem of the Gold-washing data compression algorithm is considered. It is demonstrated that for any memoryless source with finite alphabet A and generic distribution p and for any R>0, the redundancy of the Gold-washing data compression algorithm with dimension n (defined as the difference between the average performance of the algorithm and the distortion-rate function D(p,R) of p) is upper-bounded by |/sub /spl delta/R///sup /spl delta//D(p,R)|((|A|+2/spl xi/+4 log n)/2n)+/spl sigma/(logn/n) where /sub /spl delta/R///sup /spl delta//D(p,R) is the partial derivative of D(p,R) with respect to R, |A| is the cardinality of A, and /spl xi/>0 is a parameter used to control the threshold in the Gold-washing algorithm. In connection with the results of Zhang, Yang, and Wei (see ibid., vol.43, no.1, p.71-91, 1997) on the redundancy of lossy source coding, this shows that the Gold-washing algorithm has the optimal convergence rate among all adaptive finite-state vector quantizers.
17171833
What can be determined from a full and a weak perspective image?
This paper presents a first investigation on the structure from motion problem from the combination of full and weak perspective images. This problem arises in multiresolution object modeling where multiple zoomed-in or close-up views are combined with wider or distant reference views. The narrow field-of-view (FOV) images from the zoomed-in or closeup views can be approximated as weak perspective projection. Using a full perspective projection model for the narrow FOV images, although more accurate, actually leads to instabilities during the estimation process due to the non-linearities in the imaging model. The weak perspective approximation leads to more stable estimation algorithms, although at the cost of a small amount of modeling inaccuracy. Previous work in structure from motion focused either on two (or more) perspective images or on a set of weak perspective (more generally, affine) images. The main contribution of this paper is the study of the SFM problem for the much neglected case of one perspective and one (or more) weak perspective image. We show that in contrast to the case of a pair of weak perspective images, there is adequate information to recover Euclidean structure from a single perspective and a single weak perspective image. The epipolar geometry is simpler than with two perspective images leading to simpler and more stable estimation algorithms. Computer simulation shows that more stable results can be obtained with the technique presented in this paper than if two images are both considered to be full perspective.
18081212
A novel analytical model for electronic and optical switches with shared buffer
Switches with shared buffer have lower packet loss probabilities than other types of switches when the sizes of the buffers are the same. In the past, the performance analysis for electronic shared buffer switches has been carried out extensively. However, due to the strong dependencies of the output queues in the buffer, it is very difficult to find a good analytical model. Existing models are either accurate but have exponential complexities or not very accurate. In this paper, we propose a novel analytical model called the aggregation model for switches with shared buffer. This model can be used for analyzing both electronic and optical switches, and has perfect accuracies under all tested conditions and has polynomial time complexity. It is based on the idea of induction: first find the behavior of 2 queues, then aggregate them into one block; then find the behavior of 3 queues while regarding 2 of the queues as one block, then aggregate the 3 queues into one block; then aggregate 4 queues and so on. When a sufficient number of queues have been aggregated, the behavior of the entire switch is found. We believe that the new model represents the best analytical model for shared buffer switches so far.
8318108
Weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.