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Network Configuration and Management via Two-Phase Online Optimization

Network Configuration and Management via Two-Phase Online Optimization. Bilal Gonen University of Alaska Anchorage. Murat Yuksel University of Nevada, Reno. Motivation: Network Configuration. Many parameters to set in a network

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Network Configuration and Management via Two-Phase Online Optimization

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  1. Network Configuration and Management viaTwo-Phase Online Optimization Bilal Gonen University of Alaska Anchorage Murat Yuksel University of Nevada, Reno

  2. Motivation: Network Configuration • Many parameters to set in a network • Each may significantly change the overall network performance • Fast response to failures is necessary • Automated configuration and management is much needed in practice • Can be casted as an optimization problem..

  3. A Real-world Optimization Problem:Routing With “Static” Link Weights • 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 source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  4. A Real-world Optimization Problem(Traffic Engineering) S 5 5 2 2 2 5 D D 5 5 S 5 D S 5 5 2 congestion 5 5 2 2 5 5 2 5 2

  5. Setting the Link 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 source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  6. Setting the Link Weights • Empirical way: Network administrator experience • Problems: error-prone, not scalable 5 5 5 5 5 5 5 5 5 5 source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  7. Traffic Engineering Problem • Given a certain offered traffic load matrix, distribute the traffic over the network to achieve the optimal resource utilization. 5 5 5 5 5 5 5 5 5 5 source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

  8. Motivation: Problem Definition • Map the network to a black-box optimization framework and let the optimization algorithm search for the best configuration • Black-box optimization searches thru the response surface to find the optimum or near-optimum sample. • Key Question: How to accurately characterize the response surface with minimum # of experiments? Black-Box System Parameter 1 System Response Parameter 2 Parameter n

  9. Motivation: Problem Definition Black-Box System Parameter 1 System Response Parameter 2 Parameter n Can we try all possibilities? (Exhaustive search) Assume 1 ≤ Xi ≤ 10 , i=1:5 Step Size = 1 105 = 100,000 If one try = 1 sec then 100,000 sec ≈ 28 hours For 10 parameters ≈ 317 years

  10. Motivation: Big Picture Black-Box System Parameter 1 Problem System Response Parameter 2 Parameter n Metric Current BestSoFar Number of Experiments Algorithm#1 Comparator Budget Allocator PTAS Algorithm#2 Algorithm#3 BestSoFar Parameter Adjustments

  11. PTAS : Probabilistic Trans-Algorithmic Search Black-Box System Parameter 1 System Response Parameter 2 Parameter n Metric Current BestSoFar Number of Experiments Algorithm#1 Comparator Budget Allocator Algorithm#2 Algorithm#3 BestSoFar Parameter Adjustments

  12. PTAS Design Principles • An algorithm may be good at one class of problems, but its performance will suffer in the other problems • NFL Theorem: General-purpose universal algorithm is impossible • 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

  13. Some 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. Some 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

  15. Resource Allocation Mechanism

  16. PTAS : Probabilistic Trans-Algorithmic Search Black-Box System Parameter 1 System Response Parameter 2 Parameter n Metric Current BestSoFar Number of Experiments Algorithm#1 Comparator Budget Allocator Algorithm#2 Algorithm#3 BestSoFar Parameter Adjustments

  17. Resource 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. Resource Allocation Mechanism RRS is the winner in the 1st round. RRS is rewarded in the 2nd round. GA is the second in the 1st round. GA is punished in the 2nd round. SA is punished more in the 2nd round. SA is the third in the 1st round. SA is punished in the 2nd round but rewarded in the 3rd round.

  19. Experiment Setup • Network Simulator 2 (NS-2) • We converted our PTAS code into an NS-2 agent and integrate it into the NS-2. • Optimization objective: • minimize the overall packet drop rate • Thus, maximize aggregate network throughput

  20. Performance Evaluation on Exodus Topology • 22 nodes and 37 links exist. • We used 7 nodes as the edge nodes, and composed 6 × 7=42 TCP flows between those edge nodes. • Simulation metric: number of bytes received at sink nodes of the TCP flows. • We repeated the optimization process 30 times. • Average throughput achieved by each algorithm with 80% confidence intervals. IEEE GLOBECOM Workshops, 2011

  21. PTAS with No System Model Optimization using a separate model of the system Optimization using real-time running system • Assumption: system does not change frequently(backbone networks). • This former approach fails when the network system is dynamic with high failure rates or a variable demand profile. • It is not practical to model such highly variant networks by simulations.

  22. PTAS with No System Model:IGP Link Weights Optimization 11500 5000 0 6500 10000 13000 search phase search phase search interval search interval Simulation duration = 13,000 sec search interval = 5,000 sec A two-phase approach: search, no-search

  23. PTAS with No System Model • Key questions: • How frequent should we go into the “search” phase to achieve reasonable improvement by using in-situ trials on the real network? • How much disturbance is given to the system when the optimizer is searching for better configuration parameters?

  24. PTAS with No System Model:IGP Link Weights Optimization RRS (Avg throughput=7,698.24) SA (Avg throughput=7,322.22) PTAS (Avg throughput=7708.21) GA (Avg throughput=7,596.68)

  25. PTAS with No System Model:IGP Link Weights Optimization Although not always, PTAS outperforms on average. Comparison of PTAS with RRS, SA, and GA for using different search phase lengths and different number of rounds for PTAS

  26. Summary • Need for automated configuration and management of highly dynamic networks. • PTAS with no system model and PTAS with separate system model. • We explore some of the key tradeoffs: • How frequent the search should be done • How long should the search phase be • How worse the search phase can temporarily make the system performance due to its trials. • We apply PTAS and three other search algorithms on • Six well-known objective functions • A network problem on realistic ISP topologies • Wireless ad hoc network

  27. Questions, Comments

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