David Applegate Edith Cohen

1. Making Intra-Domain Routing Robust to Changing and Uncertain Traffic Demands:Understanding Fundamental Tradeoffs David Applegate Edith Cohen SIGCOMM 2003

2. 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

3. 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

4. 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

5. Possible solution: Robust routings 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. SIGCOMM 2003

6. 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

7. 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

8. Modeling and Metrics:Competitive Analysis Framework Relative rather than absolute metric: eCompare yourself only to the best possible. That is, eFor any applicable TM, compare your routing configuration performance to the best possible for that TM. SIGCOMM 2003

9. 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 ratioof 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. “Oblivious” Performance Ratio of Routing Configurations SIGCOMM 2003

17. 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

18. Demand Margins SIGCOMM 2003

19. Demand Margins SIGCOMM 2003

20. 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

21. 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

22. 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

23. Thank you! Non sequitur, Wednesday, August 27, 2003 The media quickly responds to SIGCOMM SIGCOMM 2003