Transcript MATE: MPLS Adaptive Traffic Engineering

MATE: MPLS Adaptive Traffic
Engineering
Anwar Elwalid, et. al.
IEEE INFOCOM 2001
Contents
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Introduction
MATE Functions and Algorithms
MATE Implementation Techniques
Simulation Results
Conclusions
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
protocol, e.g. OSPF-TE
– MPLS based TE, e.g. RSVP-TE, CR-LDP
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.
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
according 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
MATE Functions & Algorithms (1/4)
• MATE functions in an ingress node
MATE Functions & Algorithms (2/4)
• Filtering and Distribution function
– Facilitate traffic shifting among LSPs in a way that
reduces 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
engineering 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
MATE Functions & Algorithms (3/4)
• Model
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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
amount of it on LSP p such that pPs xsp = rs, for all s
– xl: flow rate on link l L ,
– Cl(xl): cost function of link flow xl
– Objective:
MATE Functions & Algorithms (4/4)
• Asynchronous algorithm
– Gradient projection algorithm: iteratively adjusted in
opposite 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)|| < 
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
Experimental Methodology
• Two network topologies
• Two types of traffic:
engineered traffic and cross traffic
• Two traffic models:
Network topology 1
– Short-term dependencies: Poisson
– Large degree of dependencies: DAR
• Implementation of the algorithm
Network topology 2
– Random delay introduced before moving from the
monitoring phase to the traffic engineering phase
– Coordination among ingress nodes
Poisson traffic for network topology 1
DAR traffic for network topology 1
 With cross traffic and
 engineered Poisson traffic
 for network topology 2
Poisson traffic with coordination
DAR traffic with coordination
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