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.

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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.

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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?

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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.

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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

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 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)

SIGCOMM 2003

Thank you!

Non sequitur, Wednesday, August 27, 2003

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