1,721,055 research outputs found
The CoMo white paper
No full textCoMo (Continuous Monitoring) is a passive moni-toring system. CoMo has been
designed to be the basic building block of an open network monitoring infrastructure that
would allow researchers and network operators to easily process and share network traffic
statistics over multiple sites. This paper identifies the challenges that lie ahead in the
deployment of such an open infrastructure. These main challenges are:(1) the system must
allow any generic metric to be computed on the incoming traffic stream,(2) it must provide privacy and security guarantees to the owner of the monitored link, the network users and the CoMo users, and (3) it must be robust in the face of anomalous traffic patterns. We describe the high-level architecture of CoMo and, in greater detail, the resource management, query processing and security aspects
Small-time scaling behaviors of Internet backbone traffic: an empirical study
No full textWe 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 ISE 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 - 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.This work was conducted while the first two authors were visiting
Sprint ATL. Zhi-Li Zhang was on leave from University of Minnesota,
and Vinay Ribeiro was a graduate intern. Zhi-Li Zhang was supported
in part by NSF grants ITR-0085824 and CAREER Award NCR-
9734428, and by the University of Minnesota McKnight Land-grant
professorship
On the Correlation between Route Dynamics and Routing Loops
Full text linkRouting loops are caused by inconsistencies in the routing state of the network. Although undesirable from this aspect, they can provide insight into the routing dynamics that caused them. In this work we present a methodology that utilizes a priori knowledge of loops to study the correlation between routing loops and routing events that could have caused them. We apply our technique to associate route changes with packet loops detected in actual traffic traces collected from the Sprint Backbone. Our study shows that a strong correlation exists between loops and changes in the BGP routing state while the link state protocols ISIS is seldom responsible for such events. Our analysis also identifies factors that influence the distribution of loop path lengths as well as the effectiveness of our detection techniques
Analysis of Measured Single-Hop Delay from an Operational Backbone Network
No full textWe measure and analyze the single-hop packet delay through operational routers in a backbone IP network. First we present our delay measurements through a single router. Then we identify step-by-step the factors contributing to single-hop delay. In addition to packet processing, transmission, and queueing delays, we identify the presence of very large delays due to non-work-conserving router behavior. We use a simple output queue model to separate those delay components. Our step-by-step methodology used to ohtain the pure queueing delay is easily applicable to any single-hop delay measurements. After obtaining the queueing delay, we analyze the tail of its distribution, and find that it is long tailed and fits a Weihull distrihution with the scale parameter, a = 0.5, and the shape parameter, b = 0.58 to 0.6. The measured average queueing delay is larger than predicted by M/M/l, M/G/l, and FBM models when the link utilization is below 70%, but its absolute value is quite small
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