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Failure Resilient Routing Simple Failure Recovery with Load Balancing Martin Suchara in collaboration with: D. Xu, R. Doverspike, D. Johnson and J. Rexford

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2 Acknowledgement Special thanks to Olivier Bonaventure, Quynh Nguyen, Kostas Oikonomou, Rakesh Sinha, Robert Tarjan, Kobus van der Merwe, and Jennifer Yates. We gratefully acknowledge the support of the DARPA CORONET Program, Contract N00173-08-C-2011. The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.

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3 Failure Recovery and Traffic Engineering in IP Networks Uninterrupted data delivery when links or routers fail Major goal: re-balance the network load after failure Failure recovery essential for Backbone network operators Large datacenters Local enterprise networks

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4 Overview I.Failure recovery: the challenges II.Architecture: goals and proposed design III.Optimizations: of routing and load balancing IV.Evaluation: using synthetic and realistic topologies V.Conclusion

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5 Challenges of Failure Recovery Existing solutions reroute traffic to avoid failures Can use, e.g., MPLS local or global protection Prompt failure detection Global path protection is slow Balance the traffic after rerouting Challenging with local path protection primary tunnel backup tunnel primary tunnel backup tunnel local global

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6 Overview I.Failure recovery: the challenges II.Architecture: goals and proposed design III.Optimizations: of routing and load balancing IV.Evaluation: using synthetic and realistic topologies V.Conclusion

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7 Architectural Goals 3.Detect and respond to failures quickly 1.Simplify the network Allow use of minimalist cheap routers Simplify network management 2.Balance the load Before, during, and after each failure

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8 The Architecture – Components Management system Knows topology, approximate traffic demands, potential failures Sets up multiple paths and calculates load splitting ratios Minimal functionality in routers Path-level failure notification Static configuration No coordination with other routers

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9 The Architecture topology design list of shared risks traffic demands t s fixed paths splitting ratios 0.25 0.5

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10 The Architecture t s link cut fixed paths splitting ratios 0.25 0.5

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11 The Architecture t s link cut fixed paths splitting ratios 0.25 0.5 path probing

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12 The Architecture t s link cut path probing fixed paths splitting ratios 0.5 0

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13 The Architecture: Summary 1.Offline optimizations 2.Load balancing on end-to-end paths 3.Path-level failure detection How to calculate the paths and splitting ratios?

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14 Overview I.Failure recovery: the challenges II.Architecture: goals and proposed design III.Optimizations: of routing and load balancing IV.Evaluation: using synthetic and realistic topologies V.Conclusion

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15 Goal I: Find Paths Resilient to Failures A working path needed for each allowed failure state (shared risk link group) Example of failure states: S = {e 1 }, { e 2 }, { e 3 }, { e 4 }, { e 5 }, {e 1, e 2 }, {e 1, e 5 } e1e1 e3e3 e2e2 e4e4 e5e5 R1 R2

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16 Goal II: Minimize Link Loads minimize s w s e Φ(u e s ) while routing all traffic link utilization u e s cost Φ(u e s ) aggregate congestion cost weighted for all failures: links indexed by e u e s =1 Cost function is a penalty for approaching capacity failure state weight failure states indexed by s

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17 Our Three Solutions capabilities of routers congestion Suboptimal solution Solution not scalable Good performance and practical? Too simple solutions do not do well Diminishing returns when adding functionality

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18 1. Optimal Solution: State Per Network Failure Edge router must learn which links failed Custom splitting ratios for each failure 0.4 0.2 FailureSplitting Ratios -0.4, 0.4, 0.2 e4e4 0.7, 0, 0.3 e 1 & e 2 0, 0.6, 0.4 …… configuration: 0.7 0.3 e4e4 e3e3 e1e1 e2e2 e5e5 e6e6 one entry per failure

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19 1. Optimal Solution: State Per Network Failure Solve a classical multicommodity flow for each failure case s: min load balancing objective s.t. flow conservation demand satisfaction edge flow non-negativity Decompose edge flow into paths and splitting ratios Does not scale with number of potential failure states

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20 2. State-Dependent Splitting: Per Observable Failure Edge router observes which paths failed Custom splitting ratios for each observed combination of failed paths 0.4 0.2 FailureSplitting Ratios -0.4, 0.4, 0.2 p20.6, 0, 0.4 …… configuration: 0.6 0.4 p1p1 p2p2 p3p3 NP-hard unless paths are fixed at most 2 #paths entries

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21 2. State-Dependent Splitting: Per Observable Failure If paths fixed, can find optimal splitting ratios: Heuristic: use the same paths as the optimal solution min load balancing objective s.t. flow conservation demand satisfaction path flow non-negativity

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22 3. State-Independent Splitting: Across All Failure Scenarios Edge router observes which paths failed Fixed splitting ratios for all observable failures 0.4 0.2 p1, p2, p3: 0.4, 0.4, 0.2 configuration: 0.667 0.333 Non-convex optimization even with fixed paths p1p1 p2p2 p3p3

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23 3. State-Independent Splitting: Across All Failure Scenarios Heuristic to compute splitting ratios Use averages of the optimal solution weighted by all failure case weights Heuristic to compute paths The same paths as the optimal solution r i = s w s r i s fraction of traffic on the i-th path

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24 Our Three Solutions 1.Optimal solution 2.State-dependent splitting 3.State-independent splitting How well do they work in practice?

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25 Overview I.Failure recovery: the challenges II.Architecture: goals and proposed design III.Optimizations: of routing and load balancing IV.Evaluation: using synthetic and realistic topologies V.Conclusion

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26 Simulations on a Range of Topologies Single link failures Shared risk failures for the tier-1 topology 954 failures, up to 20 links simultaneously

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27 Congestion Cost – Tier-1 IP Backbone with SRLG Failures increasing load Additional router capabilities improve performance up to a point objective value network traffic State-dependent splitting indistinguishable from optimum State-independent splitting not optimal but simple How do we compare to OSPF? Use optimized OSPF link weights [Fortz, Thorup 02].

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28 Congestion Cost – Tier-1 IP Backbone with SRLG Failures increasing load OSPF uses equal splitting on shortest paths. This restriction makes the performance worse. objective value network traffic OSPF with optimized link weights can be suboptimal

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29 Average Traffic Propagation Delay in Tier-1 Backbone Service Level Agreements guarantee 37 ms mean traffic propagation delay Need to ensure mean delay doesnt increase much

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30 Number of Paths– Tier-1 IP Backbone with SRLG Failures Number of paths almost independent of the load number of paths cdfnumber of paths For higher traffic load slightly more paths

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31 Number of Paths – Various Topologies More paths for larger and more diverse topologies number of paths cdf Greatest number of paths in the tier-1 backbone

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32 Overview I.Failure recovery: the challenges II.Architecture: goals and proposed design III.Optimizations: of routing and load balancing IV.Evaluation: using synthetic and realistic topologies V.Conclusion

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33 Conclusion Simple mechanism combining path protection and traffic engineering Favorable properties of state-dependent splitting algorithm: Path-level failure information is just as good as complete failure information

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34 Thank You!

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