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P robabilistic T rans- A lgorithmic S earch for Automated Network Management and Configuration

P robabilistic T rans- A lgorithmic S earch for Automated Network Management and Configuration. PTAS. Bilal Gonen, Murat Yuksel, Sushil Louis University of Nevada, Reno. Outline. Motivation & Problem definition: Intra-domain traffic engineering Black-box problem and search algorithms.

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P robabilistic T rans- A lgorithmic S earch for Automated Network Management and Configuration

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  1. Probabilistic Trans-Algorithmic Searchfor Automated Network Management and Configuration PTAS Bilal Gonen, Murat Yuksel, Sushil Louis University of Nevada, Reno

  2. Outline • Motivation & Problem definition: Intra-domain traffic engineering • Black-box problem and search algorithms. • PTAS: A hybrid search algorithm • Experimental results • Future work

  3. Motivation • Online configuration of large-scale systems such as networks require parameter optimization (e.g. setting link weights) to be done within a limited amount of time. • This time limit is even more pressing when configuration is needed as a recovery response to a failure (link failures) in the system.

  4. Configuration as a Black-box Optimization

  5. IGP Link Weight Setting • Routers flood information to learn topology • Determine “next hop” to reach other routers… • Compute shortest paths based on link weights • Link weights configured by network operator 5 5 5 5 5 5 5 5 5 5 J. Rexford et al., http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  6. IGP Link Weight Setting • How to set the weights? • Inversely proportional to link capacity? • Proportional to propagation delay? • Network-wide optimization based on traffic? 5 5 5 5 5 5 5 5 5 5 J. Rexford et al., http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  7. IGP Link Weight Setting • Empirical way: • Network administrator experience • Trial and error • error-prone, not scalable 5 5 5 5 5 5 5 5 5 5 J. Rexford et al., http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  8. Traffic Engineering: IGP Link Weight Setting Problem 10 10 5 10 5 5 20 5 5 congestion 5 5 5 5 5

  9. PTAS : Probabilistic Trans-Algorithmic Search

  10. No Free Lunch Theorem • Which algorithm is better? • NFL Theorem(Wolpert, 1997):No matter what perform metric is used, the average performance of any search algorithm will be the same over all possible problems. • General-purpose universal algorithm is impossible

  11. Stochastic Search Algorithm • Exploration: global phase, examine overall features, supply effectiveness • Exploitation: local phase, examine microscopic features, supply efficiency

  12. Common Search Techniques • Exploration techniques: • Random sampling • Random walk • Genetic Algorithm • Exploitation techniques: • Downhill simplex • Hillclimbing • Simulated Annealing • Hybrid • Recursive Random Search (RRS), T. Ye et al. ToN 2009

  13. Common Search Techniques • Exploration techniques: • Random sampling • Random walk • Genetic Algorithm • Exploitation techniques: • Downhill simplex • Hillclimbing • Simulated Annealing • Hybrid • Recursive Random Search (RRS), T. Ye et al. ToN 2009

  14. PTAS Design Principles • An algorithm may be good at one class of problems, but its performance will suffer in the other problems • Key Question: How to design an evolutionary hybrid search algorithm? • search for the best search • Roulette wheel: Punish the bad algorithms and reward the good ones • trans-algorithmic • Transfer the best-so-far among the algorithms

  15. PTAS : Probabilistic Trans-Algorithmic Search

  16. Roulette Wheel • The best known strategy to select among slot machines for investment! • Viewing each algorithm as a slot machine!

  17. Budget Allocation Mechanism Total Budget = 1500 300 300 300 300 300 Round budget = 300 Round-1 Round-3 Round-4 Round-2 Round-5 budget=100 budget=106 budget=120 budget=110 budget=110 Algorithm-3 Algorithm-1 Algorithm-3 Algorithm-2 Algorithm-1 Algorithm-2 Algorithm-3 Algorithm-1 Algorithm-1 Algorithm-2 Algorithm-2 Algorithm-3 Algorithm-3 Algorithm-2 Algorithm-1 Winner Winner Winner budget=100 budget=90 budget=92 budget=98 budget=102 Winner budget=100 budget=104 budget=88 budget=92 budget=88 Winner

  18. PTAS Results • We used several benchmark objective functions to model the black-box system: • Square Sum function • Rastrigin function • Griewangk’s Function • Axis parallel hyper-ellipsoid function • Rotated hyper-ellipsoid function • Ackley’s Path function. • PTAS outperforms all the other three algorithms for most of the objective functions

  19. PTAS Results Square Sum function: f1(x)=sum(x(i)^2), i=1:n

  20. PTAS Results Griewangk's function f(x)=sum(x(i)^2/4000)-prod(cos(x(i)/sqrt(i)))+1, i=1:n

  21. PTAS Results: Objective Function Changes PTAS’ benefits are more pronounced when objective function changes SquareSum -> Rastrigin -> SquareSum

  22. PTAS Results: Objective Function Changes PTAS’ benefits are more pronounced when objective function changes Griewangks -> Rastrigin -> Griewangks

  23. PTAS on IGP Link Weight Setting • Simulation Setup: • Ns-2 • Exodus topology from Rocketfuel • # of flows: 90 • # of nodes: 22 • # of links: 37 • Confidence interval: 80% (We repeated the optimization process 30 times to gain confidence.) • Optimization objective: minimize the overall packet drop rate. Thus, maximize aggregate network throughput

  24. PTAS on IGP Link Weight Setting PTAS Aggregated Througput Change link weights NS-2 simulator

  25. PTAS on IGP Link Weight Setting 6% 15% 33%

  26. Future Applications of PTAS • PTAS framework is applicable to the various network configuration problems, e.g.: • Random Early Detection (RED) queue management algorithm • BGP inter-domain traffic engineering

  27. Questions, Comments Thank you…

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