Making Intra-Domain Routing
Robust to Changing and Uncertain
Traffic Demands:
Understanding Fundamental Tradeoffs
David Applegate
Edith Cohen
SIGCOMM 2003
Some ISP Challenges
• Utilize network capacity efficiently
• QoS
Intra-Domain Traffic Engineering is
increasingly deployed.
Components:
• Understanding traffic demands
• Configuring routing protocols so that
traffic is routed efficiently
SIGCOMM 2003
Financial reports
(and traffic demands)
• Past results are not a guarantee of
future performance.
• Past results are not even a guarantee
of past performance.
SIGCOMM 2003
Traffic Demands
• Measurement of traffic data is
inexact.
– Inference from link loads  estimation
errors
– Sampled flows  sampling errors
– Missing data
• Traffic demands are dynamic and
change on multiple time scales.
SIGCOMM 2003
Routing configuration
• Knowing exact demands values allows for
very efficient routings.
• But..., we don’t have accurate values.
• Moreover, even if we did…
• Demands are dynamic.
• But..., modifications to the routings
cause disruptions and reduce QoS.
Possible solution: Robust routings
SIGCOMM 2003
Robust routings
• A fixed routing configuration that works
well (as well as possible) for a wide
range (or all) traffic matrices (TMs).
• Built-in robustness to changing/unknown
conditions is a natural objective of good
engineering.
SIGCOMM 2003
Challenges
• Modeling: How to measure robustness?
• Algorithmic: Given no or some constraints
on TMs, how to efficiently compute an
optimal robust routing ?
• Understanding the tradeoff: Quantify the
“generality cost”: A fixed routing that is
optimized for many TMs may be suboptimal
for a particular TM. What to expect?
SIGCOMM 2003
Modeling and Metrics:
Competitive Analysis Framework
Relative rather than absolute metric:
e Compare yourself only to the best
possible. That is,
e For any applicable TM, compare your
routing configuration performance to
the best possible for that TM.
SIGCOMM 2003
Metrics... details
• Given a routing configuration f and a TM D,
we look at the Maximum Link Utilization
(MLU) when routing D using f.
• Performance ratio of f on D: ratio of MLU of
f on D to the MLU of the optimal routing
configuration for D.
• Performance ratio of f on a set of TMs is the
max performance ratio over TMs in the set.
SIGCOMM 2003
Challenges
• Modeling: How to measure robustness?
• Algorithmic: Given no or some constraints
on TMs, how to efficiently compute an
optimal robust routing ?
• Understanding the tradeoff: Quantify the
“generality cost”: A fixed routing that is
optimized for many TMs may be suboptimal
for a particular TM. What to expect?
SIGCOMM 2003
Algorithms for optimal robust
(“demand oblivious”) routing
• Known: [ACFKR:STOC 03] Polynomial time
algorithm through an exponential LP
formulation using the Ellipsoid algorithm
(separation)
• Our contribution (theoretical and practical):
•
•
•
Compact polynomial-size LP formulation.
Efficient implementation.
Extensions to demand ranges constraints.
SIGCOMM 2003
Challenges
• Modeling: How to measure robustness?
• Algorithmic: Given no or some constraints
on TMs, how to efficiently compute an
optimal robust routing ?
• Understanding the tradeoff: Quantify the
“generality cost”: A fixed routing that is
optimized for many TMs may be suboptimal
for a particular TM. What to expect?
SIGCOMM 2003
Understanding the Tradeoffs
• How well can we do with no knowledge
of demands (what is the optimal
“oblivious” performance ratio) ?
• What if we have some knowledge on
applicable demands, say, using a “base”
TM within some error margins ?
SIGCOMM 2003
Data
• Topologies: Six PoP to PoP ISP topologies
from Rocketfuel, aggregated to cities; one
topology from [MTSBD 02]
14—57 nodes ; 25—88 links
• Capacities: heuristic
• TMs: heuristic, bimodal and gravity
• TM-sets: All TMs; base bimodal/gravity
TM with margins (error bars)
SIGCOMM 2003
Routing Configurations
• Optimal Robust routing for the
applicable set of TMs (MPLS-style)
(computed using our algorithms)
• OSPF routing (derived) (supplied with
Rocketfuel data)
• For demand margins: optimal routing
for the base TM (MPLS-style)
(computed via a mcf LP)
SIGCOMM 2003
“Oblivious” Performance Ratio
of Routing Configurations
ASN
1221 Telstra
1755 Ebone
6461 Abovenet
3967 Exodus
3257 Tiscali
1239 Sprintlink
N-14 [MTSBD02]
PoPs
57
23
22
22
50
44
14
links
59
38
42
37
88
83
25
SIGCOMM 2003
Optimal OSPF
1.43
4.2
1.78
16.6
1.91
13.4
1.62
49.2
1.80
51.2
1.90
234.0
1.97
7.7
Scalability
• [Räcke 02] poly-logarithmic upper bound
for symmetric networks; [HHR 03]
O(log^2 n log log n)
• We observe < 2 for ISP networks.
• Supported by analysis showing that cycles
and cliques of any size have <2 ratio.
• 1.4-1.9 is surprisingly low but probably not
good enough to be practical
SIGCOMM 2003
Demand Margins
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Demand Margins
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Conclusions from experimental
evaluation
• Can do reasonably well with no knowledge
of TM (for all TMs), link utilization +40%+90%
• Can do even better for error margins (x4
bars with +25% utilization).
• Routing designed to be optimal for a
somewhat-off TM estimate can be much
worse than an optimal demand-oblivious
routing.
SIGCOMM 2003
Summary of Contributions
• New analytical framework and algorithms
for computing and evaluating robust
routing configurations.
• Experiments showing that optimal robust
routings perform well on (Rocketfuel) ISP
topologies, and significantly outperform
naïve methods (optimize without margins,
naïve OSPF)
SIGCOMM 2003
Future
• Robust restoration routing
• Optimal OSPF-style rather that MPLSstyle robust routings
• Robust routing under varying demand
constraints (link load data)
• More efficient computation
• Better measure (relative metric places too
much emphasis on “easy” TMs)
SIGCOMM 2003
Thank you!
Non sequitur, Wednesday, August 27, 2003
The media quickly responds to SIGCOMM
SIGCOMM 2003