MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid, et. al. IEEE INFOCOM 2001.
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Transcript of MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid, et. al. IEEE INFOCOM 2001.
![Page 1: MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid, et. al. IEEE INFOCOM 2001.](https://reader036.fdocuments.in/reader036/viewer/2022082517/56649db65503460f94aa84f3/html5/thumbnails/1.jpg)
MATE: MPLS Adaptive Traffic Engineering
Anwar Elwalid, et. al.
IEEE INFOCOM 2001
![Page 2: MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid, et. al. IEEE INFOCOM 2001.](https://reader036.fdocuments.in/reader036/viewer/2022082517/56649db65503460f94aa84f3/html5/thumbnails/2.jpg)
Contents
• Introduction
• MATE Functions and Algorithms
• MATE Implementation Techniques
• Simulation Results
• Conclusions
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Introduction (1/3)
• Traffic engineering (TE) v.s. QoS routing– TE aims at maximizing operational network efficiency
while meeting certain constraints
– QoS routing meet certain QoS constraints for a given source-destination traffic flow
• Two categories of TE implementation– Extend current shortest path algorithm based routing pr
otocol, e.g. OSPF-TE
– MPLS based TE, e.g. RSVP-TE, CR-LDP
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Introduction (2/3)
• Limitations of extending SPF-based routing– Load sharing can not accomplished among paths of
different costs– Traffic/policy constraint are not taken into account– Modifications of link metrics to re-adjust traffic
mapping tend to have network-wide effects– Traffic demands must be predicable and known a priori
• The combination of MPLS technology and its TE capabilities are expected to overcome the above limitations.
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Introduction (3/3)
• MPLS TE mechanisms may be– Time-dependent mechanisms
• use historical information based on seasonal variations in traffic to pre-program LSP layout and traffic assignment
• do not attempt to adapt to unpredictable traffic variations or changing network conditions
– State-dependent mechanisms• Deal with adaptive traffic assignment to the established LSPs a
ccording to the current state of the network
– The focus of this paper is on load balancing short-term traffic fluctuations among multiple LSPs between an ingress node and an egress node
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MATE Functions & Algorithms (1/4)
• MATE functions in an ingress node
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MATE Functions & Algorithms (2/4)
• Filtering and Distribution function– Facilitate traffic shifting among LSPs in a way that reduc
es the possibilities of having packets out of order• Traffic Engineering function
– Decides on when and how to shift traffic among LSPs– Consists of two phases: monitoring phase and engineerin
g phase• Measurement and Analysis function
– Obtains one-way LSP statistics such as packet delay and packet loss, done by having ingress node transmit probe packet periodically to the egress node which returns them back to ingress node
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MATE Functions & Algorithms (3/4)
• Model– L: a set of unidirectional links, shared by– S: a set of ingress-egress(IE) node pairs, each pair s has– Ps: a set of LSPs– An IE pair s has total input traffic rate rs and route xsp a
mount of it on LSP p such that pPs xsp = rs, for all s
– xl: flow rate on link l L ,
– Cl(xl): cost function of link flow xl
– Objective:
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MATE Functions & Algorithms (4/4)
• Asynchronous algorithm– Gradient projection algorithm: iteratively adjusted in op
posite direction of the gradient and projected onto the feasible space. Each iteration takes the form
x(t+1) = [x(t) - C(t)]+ ,where >0 is a stepsize, should be chosen sufficiently small C(t) is a vector whose (s,p)th element is C/xsp
[z]+ is the projection of a vector z onto feasible space
– The algorithm terminates when there is no appreciable change, i.e.,||x(t+1)-x(t)|| <
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MATE Implementation Techniques
• Traffic filtering and distribution– Distribute traffic on a per-packet basis without filtering– Filter traffic on a per-flow basis and distribute the
flows to the bins such that the loads are similar– Filter the incoming packets by using a hash function
• Traffic measurement and analysis– Packet delay and packet loss probability are metrics
that can be estimated by a group of probe packets– Bootstrap technique is used to dynamically select the
required number of probe packet to send
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Experimental Methodology
• Two network topologies• Two types of traffic:
engineered traffic and cross traffic
• Two traffic models:– Short-term dependencies: Poisson– Large degree of dependencies: DAR
• Implementation of the algorithm– Random delay introduced before moving from the
monitoring phase to the traffic engineering phase– Coordination among ingress nodes
Network topology 1
Network topology 2
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Poisson traffic for network topology 1 DAR traffic for network topology 1
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With cross traffic and engineered Poisson traffic for network topology 2
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Poisson traffic with coordination DAR traffic with coordination
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Conclusions
• MATE algorithms are proposed– To apply adaptive TE to utilize network resource more
efficiently and minimize congestion
– Using minimal assumptions through a combination of techniques such as bootstrap probe packets
– With stability and optimality proved by analytical models
– To effectively remove traffic imbalances among multiple LSPs from simulation results