Traffic matrices for MPLS networks with LDP traffic statistics

Schnitter, Stefan; Horneffer, Martin

doi:10.1109/netwks.2004.240932

Cited by 11 publications

(4 citation statements)

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“…the (generalized) multiprotocol label switching ((G)MPLS) architecture [34], [39], tunnels can be implemented as label switched paths (LSPs) between label edge routers (LERs) and traffic can be easily measured per tunnel. Traffic matrices determined with help of the label distribution protocol (LDP) [40] provide the necessary traffic measurements per LSP.…”

Section: Application Of Ebac In a Network Scopementioning

confidence: 99%

Improving Experience-Based Admission Control through Traffic Type Awareness

Milbrandt¹,

Menth²,

Junker³

2007

JNW

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Experience-based admission control (EBAC) is a hybrid approach combining the classical parameter-based and measurement-based admission control. EBAC calculates an appropriate overbooking factor used to overbook link capacities with resource reservations in packet-switched networks. This overbooking factor correlates with the average peak-to-mean rate ratio of all admitted traffic flows on the link. So far, a single overbooking factor is calculated for the entire traffic aggregate. In this paper, we propose typespecific EBAC which provides a compound overbooking factor considering different types of traffic that subsume flows with similar peak-to-mean rate ratios. The concept can be well implemented since it does not require measurements of type-specific traffic aggregates. We give a proof of concept for this extension and compare it with the conventional EBAC approach. We show that EBAC with type-specific overbooking leads to better resource utilization under normal conditions and to faster response times for changing traffic mixes.

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Section: Application Of Ebac In a Network Scopementioning

confidence: 99%

Improving Experience-Based Admission Control through Traffic Type Awareness

Milbrandt¹,

Menth²,

Junker³

2007

JNW

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“…Nevertheless, some requirements for traffic management and control have often been neglected in pure IP networks. For a long time, link load statistics were the only standard source of data to observe the traffic development, in order to upgrade links when the load approaches a threshold and to estimate the traffic demand matrix [3,6,9,24].…”

Section: Introduction: Traffic Management In Ip Networkmentioning

confidence: 99%

The Efficiency of Traffic Engineering with Regard to Link Failure Resilience

2005

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IP networks have established as a global telecommunication platform with increasing user population and an extending spectrum of services. The traffic is also steadily increasing, recently driven by peer to peer networking in addition to client server based applications. Network planers and operators have to ensure the scalability of IP platforms in a permanent upgrade process for transmission capacities. At present, Deutsche Telekom and other telecommunication network providers are introducing traffic engineering methods to achieve an optimum resource utilization.In a first step, traffic engineering can be applied to a predefined network topology, but a comprehensive approach has to be coordinated with a process for upgrading the link capacities and has to prepare for relevant failure scenarios. We have evaluated the efficiency of traffic engineering together with simple link upgrade strategies in order to get a maximum throughput. Therefore a predefined traffic matrix T is taken into account. The optimization goal is to scale the traffic matrix by a maximum factor λ max such that the traffic demand λ max T can still be carried on the available network resources. The influence of the network topology on the evaluation results is shown in examples with regard to single link failures.

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“…If RSVP-TE [3] is used, a full mesh of tunnels can be deployed and counters for those tunnels exist to measure the traffic matrix. If LDP [4] is used to distribute the label information in a network, LDP statistics of the routers can be used to compute traffic matrices on a router level -see [5], [6]. The LDP method results in a very high measurement accuracy (for example, there is no sampling involved) while the measurement complexity is very low: The measurement is based on aggregated forwarding equivalence classes (FEC) that are introduced in MPLS/LDP and is not based on the IP flow level.…”

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confidence: 99%

Quality-of-service class specific traffic matrices in ip/mpls networks

Schnitter

Hartleb

Horneffer

2007

Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement

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In this paper we consider the problem of determining traffic matrices for end-to-end demands in an IP/MPLS network that supports multiple quality of service (QoS) classes. More precisely, we want to determine the set of traffic matrices T i for each QoS class i separately. T i contains average bandwidth levels for QoS class i for every pair of routers within the network. We propose a new method for obtaining QoS class specific traffic matrices that combines estimation and measurement methods: We take advantage of the fact that the total traffic matrix can be measured precisely in MPLS networks using either the LDP or RSVP-TE protocol. These measurements can then be used in a mathematical model to improve estimation methodsknown as network tomography -that estimate QoS class specific traffic matrices from QoS class specific link utilizations. In addition to the mathematical model, we present results of the proposed method from its application in Deutsche Telekom's global IP/MPLS backbone network and we show that the estimation accuracy (mean relative error) is improved by a factor of 2.5 compared to results from network tomogravity. We investigate the structure of the estimated traffic matrices for the different QoS classes and motivate in this paper why QoS class specific traffic matrices will be essential for efficient network planning and network engineering in the future.

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Traffic matrices for MPLS networks with LDP traffic statistics

Cited by 11 publications

References 0 publications

Improving Experience-Based Admission Control through Traffic Type Awareness

Improving Experience-Based Admission Control through Traffic Type Awareness

The Efficiency of Traffic Engineering with Regard to Link Failure Resilience

Quality-of-service class specific traffic matrices in ip/mpls networks

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