Dynamics of IP traffic (original) (raw)
Related papers
Small-time scaling behavior of Internet backbone traffic
Computer Networks, 2005
We perform an extensive wavelet analysis of Internet backbone traffic traces to observe and understand the causes of small-time scaling phenomena present in them. We observe that for a majority of the traces, the second-order scaling exponents at small time scales (1-100 ms) 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 (%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 byte contribution and flow density. Our study points to the dense flows (i.e., flows with 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 influenced by traffic fluctuations at small time-scales, our observations and results have important implications for networking modeling, service provisioning and traffic engineering.
IEEE/ACM Transactions on Networking, 2017
In the mid-90's, it was shown that the statistics of aggregated time series from Internet traffic departed from those of traditional short range dependent models, and were instead characterized by asymptotic self-similarity. Following this seminal contribution, over the years, many studies have investigated the existence and form of scaling in Internet traffic. This contribution aims first at presenting a methodology, combining multiscale analysis (wavelet and wavelet leaders) and random projections (or sketches), permitting a precise, efficient and robust characterization of scaling which is capable of seeing through non-stationary anomalies. Second, we apply the methodology to a data set spanning an unusually long period: 14 years, from the MAWI traffic archive, thereby allowing an in-depth longitudinal analysis of the form, nature and evolutions of scaling in Internet traffic, as well as network mechanisms producing them. We also study a separate 3-day long trace to obtain complementary insight into intra-day behavior. We find that a biscaling (two ranges of independent scaling phenomena) regime is systematically observed: long-range dependence over the large scales, and multifractal-like scaling over the fine scales. We quantify the actual scaling ranges precisely, verify to high accuracy the expected relationship between the long range dependent parameter and the heavy tail parameter of the flow size distribution, and relate fine scale multifractal scaling to typical IP packet inter-arrival and to round-trip time distributions.
The Changing Nature of Network Traffic: Scaling Phenomena
1997
In this paper, we report on some preliminary results from an in-depth, wavelet-based analysis of a set of high-quality, packet-level tra c measurements, collected over the last 6-7 years from a number of di erent working wide-area networks (WANs). We rst validate and con rm an earlier nding, originally due to Paxson and Floyd 12], that actual WAN tra c is consistent with statistical self-similarity for su ciently large time scales. We then relate this large-time scaling phenomenon to the empirically observed characteristics of WAN tra c at the level of individual connections or applications. In particular, we present here original results about a detailed statistical analysis of Web-session characteristics, and report on an intriguing scaling property of measured WAN tra c at the transport layer (i.e., number of TCP connection arrivals per time unit). This scaling property of WAN tra c at the TCP layer was absent in the pre-Web period but has become ubiquitous in today's WWW-dominated WANs and is a direct consequence of the ever-increasing popularity of the Web (WWW) and its emergence as the major contributor to WAN tra c. Moreover, we show that this changing nature of WAN tra c can be naturally accounted for by self-similar tra c models, primarily because of their ability to provide physical explanations for empirically observed tra c phenomena in a networking context. Finally, we provide empirical evidence that actual WAN tra c traces also exhibit scaling properties over small time scales, but that the small-time scaling phenomenon is distinctly di erent from the observed large-time scaling property. We relate this newly observed characteristic of WAN tra c to the e ects that the dominant network protocols (e.g., TCP) and controls have on the ow of packets across the network and discuss the potential that multifractals have in this context for providing a structural modeling approach for WAN tra c and for capturing in a compact and parsimonious manner the observed scaling phenomena at large as well as small time scales.
A Longitudinal Study of Small-Time Scaling Behavior of Internet Traffic
Lecture Notes in Computer Science, 2010
We carry out a longitudinal study of evolution of small-time scaling behavior of Internet traffic on the MAWI dataset spanning 8 years. MAWI dataset contains a number of anomalies which interfere with the correct identification of scaling behavior, and hence to mitigate these effects, we use a sketch-based procedure for robust estimation of scaling exponent. We first show the importance of robust estimation procedure while studying small-time scaling behavior of Internet traffic. We further study the evolution of the following properties concerning the origins of small-time scaling behavior: (1) Scaling at IP level is independent of flow arrivals and (2) Dense flows are primary correlationcausing factor in small time scales. Traditionally these properties have been shown to hold by using a semi-experiments based methodology. We next show that due to network anomalies, semi-experiments can result in misleading inferences. Hence we propose and motivate the use of "robust semi-experiments" i.e., a semi-experiment coupled with the use of a robust estimation procedure for inferring scaling behavior. By making use of robust semi-experiments we find the above properties to be invariant across the entire MAWI dataset. Our other results consist in showing that dense flows form a larger fraction of aggregate traffic for recent traces and hence recent traces show larger short range correlations vis-a-vis earlier traces.
Small-time scaling behaviors of Internet backbone traffic: An empirical study
2003
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 smalltime scalings of Internet backbone traffic via empirical data analysis. We analyze the traffic composition of the traces along two dimensions -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 influencing the small-time scalings of aggregate traffic.
The multiscale nature of network traffic: Discovery, analysis, and modelling
IEEE Signal …, 2002
The complexity and richness of telecommunications traffic is such that one may despair to find any regularity or explanatory principles. Nonetheless, the discovery of scaling behavior in tele-traffic has provided hope that parsimonious models can be found. The statistics of scaling behavior present many challenges, especially in non-stationary environments. In this paper, we overview the state of the art in this area, focusing on the capabilities of the wavelet transform as a key tool for unravelling the mysteries of traffic statistics and dynamics.
Looking behind and beyond self-similarity: On scaling phenomena in measured WAN traffic
1997
In this paper, we report on some preliminary results from an in-depth, wavelet-based analysis of a set of high-quality, packet-level tra c measurements, collected over the last 6-7 years from a number of di erent working wide-area networks (WANs). We rst validate and con rm an earlier nding, originally due to Paxson and Floyd 10], that actual WAN tra c is consistent with statistical self-similarity for su ciently large time scales. Second, we illustrate that TCP tra c characteristics have undergone major changes within the last 3-4 years, because of the increasing popularity of the Web (WWW) and its emergence as the major contributor to WAN tra c. Finally, we provide empirical evidence that actual WAN tra c traces also exhibit scaling properties over small time scales, but that the small-time scaling phenomenon is distinctly di erent from the observed large-time scaling property. We relate this newly observed characteristic of WAN tra c to the e ects that the dominant network protocols (e.g., TCP) and controls have on the ow of packets across the network, and discuss the potential that multifractals have for providing a structural modeling approach for WAN tra c that captures in a compact and parsimonious manner the observed scaling phenomena at large as well as small time scales.
Non-stationarity and high-order scaling in TCP flow arrivals
ACM SIGCOMM Computer Communication Review, 2004
The last decade has been a very fruitful period in important discoveries in network traffic modeling, uncovering various scaling behaviors. Self-similarity, long-range dependence, multifractal behavior and finally cascades have been studied and convincingly matched to real traffic. The first purpose of this paper is to provide a methodology to go beyond the naive analysis of the second-order wavelet-based estimators of scaling, by performing non-stationarity checks and relying on the information contained in the high-order properties of the wavelet coefficients. Then, we apply this methodology to study the scaling properties of the TCP flow arrivals based on several traffic traces spanning the years from 1993 to early 2002. Our study reveals that the second-order scaling properties of this process describe its dynamics quite well. However, our analysis also provides evidence that high-order scaling in this process appears due to pathological behaviors like rate limitation and nonstationarity.
The changing nature of network traffic
ACM SIGCOMM Computer Communication Review, 1998
In this paper, we report on some preliminary results from an in-depth, wavelet-based analysis of a set of high-quality, packet-level tra c measurements, collected over the last 6-7 years from a number of di erent working wide-area networks (WANs). We rst validate and con rm an earlier nding, originally due to Paxson and Floyd 12], that actual WAN tra c is consistent with statistical self-similarity for su ciently large time scales. We then relate this large-time scaling phenomenon to the empirically observed characteristics of WAN tra c at the level of individual connections or applications. In particular, we present here original results about a detailed statistical analysis of Web-session characteristics, and report on an intriguing scaling property of measured WAN tra c at the transport layer (i.e., number of TCP connection arrivals per time unit). This scaling property of WAN tra c at the TCP layer was absent in the pre-Web period but has become ubiquitous in today's WWW-dominated WANs and is a direct consequence of the ever-increasing popularity of the Web (WWW) and its emergence as the major contributor to WAN tra c. Moreover, we show that this changing nature of WAN tra c can be naturally accounted for by self-similar tra c models, primarily because of their ability to provide physical explanations for empirically observed tra c phenomena in a networking context. Finally, we provide empirical evidence that actual WAN tra c traces also exhibit scaling properties over small time scales, but that the small-time scaling phenomenon is distinctly di erent from the observed large-time scaling property. We relate this newly observed characteristic of WAN tra c to the e ects that the dominant network protocols (e.g., TCP) and controls have on the ow of packets across the network and discuss the potential that multifractals have in this context for providing a structural modeling approach for WAN tra c and for capturing in a compact and parsimonious manner the observed scaling phenomena at large as well as small time scales.