The Experts below are selected from a list of 74676 Experts worldwide ranked by ideXlab platform
Fionn Murtagh - One of the best experts on this subject based on the ideXlab platform.
-
web Traffic Demand forecasting using wavelet based multiscale decomposition
International Journal of Intelligent Systems, 2001Co-Authors: Alexandre Aussem, Fionn MurtaghAbstract:In this paper we propose an experimental forecasting strategy taking into account the long-range dependence of aggregate network Traffic, and we apply it to provide one-minute-ahead World-Wide Web (Web) Traffic Demand forecasts in terms of average number of bytes transferred. Recently, statistical examination of Web traces have shown evidence that Web Traffic arising from file transfers exhibits a behavior that is consistent with the notion of self-similarity. Essentially, self-similarity indicates that significant burstiness is present on a wide range of time scales (i.e., the process is long-range dependent). Hence the idea of exploiting this multiscale property with a view towards discovering and capturing regularities underlying the time series which may prove useful for short-term Traffic load forecasting. We carry out a wavelet transform decomposition of the original series to decompose the Traffic time series into varying scales of temporal resolution, with the aim of making the underlying temporal structures more tractable. In a second step, each individual wavelet series—supposed to capture some features of the series—is fitted with a dynamical recurrent neural network (DRNN) model to output the wavelet forecast. The latter are afterwards recombined to form the next-minute Web Traffic Demand. The method is applied on a large set of HTTP logs and is shown to yield good results. © 2001 John Wiley & Sons, Inc.
Ravi Sundaram - One of the best experts on this subject based on the ideXlab platform.
-
a methodology for estimating interdomain web Traffic Demand
Internet Measurement Conference, 2004Co-Authors: Anja Feldmann, Nils Kammenhuber, Olaf Maennel, Bruce M Maggs, Roberto De Prisco, Ravi SundaramAbstract:This paper introduces a methodology for estimating interdomain Web Traffic lows between all clients worldwide and the ervers belonging to over one housand content providers. The idea is to use the server logs from a large ontent Delivery Network (CDN) to identify client downloads of content provider (i.e., publisher) Web pages. For each of these Web pages, a client typically downloads some objects from the content provider, some from the CDN, and perhaps some from third parties such as banner advertisement agencies. The sizes and sources of the non-CDN downloads associated with each CDN download are estimated separately by examining Web accesses in packet traces collected at several universities. The methodology produces a (time-varying) interdomain HTTP Traffic Demand matrix pairing several hundred thousand blocks of client IP addresses with over ten thousand individual Web servers. When combined with geographical databases and routing tables, the matrix can be used to provide (partial) answers to questions such as "How do Web access patterns vary by country?", "Which autonomous systems host the most Web content?", and "How stable are Web Traffic flows over time?".
Alexandre Aussem - One of the best experts on this subject based on the ideXlab platform.
-
web Traffic Demand forecasting using wavelet based multiscale decomposition
International Journal of Intelligent Systems, 2001Co-Authors: Alexandre Aussem, Fionn MurtaghAbstract:In this paper we propose an experimental forecasting strategy taking into account the long-range dependence of aggregate network Traffic, and we apply it to provide one-minute-ahead World-Wide Web (Web) Traffic Demand forecasts in terms of average number of bytes transferred. Recently, statistical examination of Web traces have shown evidence that Web Traffic arising from file transfers exhibits a behavior that is consistent with the notion of self-similarity. Essentially, self-similarity indicates that significant burstiness is present on a wide range of time scales (i.e., the process is long-range dependent). Hence the idea of exploiting this multiscale property with a view towards discovering and capturing regularities underlying the time series which may prove useful for short-term Traffic load forecasting. We carry out a wavelet transform decomposition of the original series to decompose the Traffic time series into varying scales of temporal resolution, with the aim of making the underlying temporal structures more tractable. In a second step, each individual wavelet series—supposed to capture some features of the series—is fitted with a dynamical recurrent neural network (DRNN) model to output the wavelet forecast. The latter are afterwards recombined to form the next-minute Web Traffic Demand. The method is applied on a large set of HTTP logs and is shown to yield good results. © 2001 John Wiley & Sons, Inc.
Mateusz Zotkiewicz - One of the best experts on this subject based on the ideXlab platform.
-
Robust routing and optimal partitioning of a Traffic Demand polytope
International Transactions in Operational Research, 2011Co-Authors: Walid Ben-ameur, Mateusz ZotkiewiczAbstract:In this paper we consider the problem of optimal partitioning of a Traffic Demand polytope using a hyperplane. In our model all possible Demand matrices belong to a polytope. The polytope can be divided into parts, and different routing schemes can be applied while dealing with Traffic matrices from different parts of the polytope. We consider three basic models: Robust-Routing, No-Sharing and Dynamic-Routing. We apply two different partitioning strategies depending on whether the reservation vectors on opposite sides of the hyperplane are required to be identical, or allowed to differ. We provide efficient algorithms that solve these problems. Moreover, we prove polynomiality of some of the considered cases. Finally, we present numerical results proving the applicability of the introduced algorithms and showing differences between the routing strategies.
-
Polynomial Traffic Demand polytope partitioning
Electronic Notes in Discrete Mathematics, 2010Co-Authors: Walid Ben-ameur, Mateusz ZotkiewiczAbstract:We consider the problem of partitioning of a Traffic Demand polytope using a hyper-plane. The polytope is divided into parts, and different routing schemes are applied while dealing with Traffic matrices from different parts of the polytope. Different Demands cannot share resources, and reservation vectors on opposite sides of the hyperplane have to be identical. We provide a polynomial time algorithm solving the presented problem when a Traffic Demand polytope is defined as a convex hull of a known set of Traffic Demand matrices.
Garrett Burchett - One of the best experts on this subject based on the ideXlab platform.
-
whether weather matters to Traffic Demand Traffic safety and Traffic operations and flow
Transportation Research Record, 2006Co-Authors: Thomas H Maze, Manish Agarwal, Garrett BurchettAbstract:Weather affects many aspects of transportation, but three dimensions of weather impact on highway Traffic are predominant and measureable. Inclement weather affects Traffic Demand, Traffic safety, and Traffic flow relationships. Understanding these relationships will help highway agencies select better management strategies and create more efficient operating policies. For example, it was found that severe winter storms bring a higher risk of being involved in a crash by as much as 25 times—much higher than the increased risk brought by behaviors that state governments already have placed sanctions against, such as speeding or drunk driving. Given the heightened risk of drivers' involvement in a crash, highway agencies might wish to manage better and restrict use of highways during times of extreme weather, to reduce safety costs and costs associated with rescuing stranded and injured motorists in the worst weather conditions. However, the first step in managing the transportation systems to minimize the ...
