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Making Intra-Domain Routing Robust to Changing and Uncertain Traffic Demands: Understanding Fundamental Tradeoffs. David Applegate Edith Cohen. Some ISP Challenges. Utilize network capacity efficiently QoS. Intra-Domain Traffic Engineering is increasingly deployed. Components: - PowerPoint PPT Presentation
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SIGCOMM 2003
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: Compare yourself only to the best
possible. That is, 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 PoPs
links
Optimal
OSPF
1221 Telstra 57 59 1.43 4.2
1755 Ebone 23 38 1.78 16.6
6461 Abovenet 22 42 1.91 13.4
3967 Exodus 22 37 1.62 49.2
3257 Tiscali 50 88 1.80 51.2
1239 Sprintlink 44 83 1.90 234.0
N-14 [MTSBD02] 14 25 1.97 7.7
SIGCOMM 2003
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
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 MPLS-
style 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)