Implications of Interdomain Traffic Characteristics on Traffic Engineering

Uhlig, Steve; Bonaventure, Olivier

doi:10.1002/ett.4460130104

Cited by 27 publications

(21 citation statements)

References 16 publications

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“…If data is consistent with a Zipf-like distribution, it is in principle sufficient to concentrate on the popular events when engineering the network in order to optimize the overall performance. This is the common idea, i.e., used for measurement [18], traffic engineering [12], [19], [13], [20], including load adaptive routing [21], scheduling [22], Web caching [10], [11], [23], etc.. All of the above examples assume and rely on the assumption that if an event is popular it will stay popular. On the other hand it is well-known that traffic is variable and traffic fluctuations can induce significant changes in popularity.…”

Section: Introductionmentioning

confidence: 99%

Capturing the Variability of Internet Flows Across Time

Wallerich¹,

Feldmann²

2006

Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications

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Abstract-More and more traffic management techniques, including accounting and load adaptive routing, try to take advantage of the fact that traffic demands are consistent with Zipf's law. By treating a few large volume demands differently they try to capture most of the traffic. This relies on the implicit assumption that traffic demands are persistent in volume over time; meaning that their volume does not change drastically over time.As this assumption has been shown to be incorrect we in this paper focus on how Internet flows behave over time. Accordingly, this paper examines the characteristics of volatility in a qualitative way by characterizing the components that are responsible for changes in the cast of heavy hitters over time.

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Section: Introductionmentioning

confidence: 99%

Capturing the Variability of Internet Flows Across Time

Wallerich¹,

Feldmann²

2006

Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications

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“…Previous work has shown that traffic observed by an AS has a tree-like structure rooted at the observing AS and whose leafs are the destination ASs [21,22], and on average edges farther away from the root see less traffic. We thus expect that different edges observed different traffic dynamics.…”

Section: Amount Of Traffic Vs Lifetimementioning

confidence: 99%

Capturing Internet Traffic Dynamics through Graph Distances

Uhlig

Jamakovic³

2009

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Studies of the Internet have typically focused either on the routing system, i.e. the paths chosen to reach a given destination, or on the evolution of traffic on a physical link. In this paper, we combine routing and traffic, and study for the first time the evolution of the traffic on the Internet topology. We rely on the traffic and routing data of a large transit provider, spanning almost a month. We compute distances between the traffic graph over small and large timescales. We find that the global traffic distribution on the AS graph largely differs from traffic observed at small timescales. However, variations between consecutive time periods are relatively limited, i.e. the topology spanned by the traffic from one time period to the next is small. This difference between local and global traffic distribution is found in the timescales at which traffic dynamics occurs on AS-level links. Small timescales, i.e. less than a few hours, do not account for a significant fraction of the traffic dynamics. Most of the traffic variability is concentrated at timescales of days. Models of Internet traffic on its topology should thus focus on capturing the long-term changes in the global traffic pattern.

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“…The feasibility of interdomain TE by a stub AS was examined in [6] and more recently in [7]. A simulation-based study of the effectiveness of AS-Path prepending for INITE has been published in [8].…”

Section: Related Workmentioning

confidence: 99%

Interdomain Ingress Traffic Engineering Through Optimized AS-Path Prepending

Gao

Dovrolis

Zegura

2005

NETWORKING 2005. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile A

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Abstract. In INterdomain Ingress Traffic Engineering (INITE), a "target" Autonomous System (AS) aims to control the ingress link at which the traffic of one or more upstream source networks enters that AS. In practice, ISPs often manipulate, mostly in a trial-and-error manner, the length of the AS-Path attribute of upstream routes through a simple technique known as prepending (or padding). In this paper, we focus on prepending and propose a polynomial-time algorithm (referred to as OPV) that determines the optimal padding for an advertised route at each ingress link of the target network. Specifically, given a set of "elephant" source networks and some maximum load constraints on the ingress links of the target AS, OPV determines the minimum padding at each ingress link so that the load constraints are met, when it is feasible to do so. OPV requires as input an AS-Path length estimate from each source network to each ingress link. We describe how to estimate this matrix, leveraging the BGP Looking Glass Servers. To deal with unavoidable inaccuracies in the AS-Path length estimates, and also to compensate for the generally unknown BGP tie-breaking process in upstream networks, we also develop a robust variation (RPV) of the OPV algorithm.

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Implications of Interdomain Traffic Characteristics on Traffic Engineering

Cited by 27 publications

References 16 publications

Capturing the Variability of Internet Flows Across Time

Capturing the Variability of Internet Flows Across Time

Capturing Internet Traffic Dynamics through Graph Distances

Interdomain Ingress Traffic Engineering Through Optimized AS-Path Prepending

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