Bilal Gonen University of Alaska Anchorage Murat Yuksel University of Nevada, Reno

 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..

 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 source:

congestion D S D D S S

 Inversely proportional to link capacity  Proportional to propagation delay  Network-wide optimization based on traffic source:

 Empirical way: Network administrator experience  Problems: error-prone, not scalable source:

 Given a certain offered traffic load matrix, distribute the traffic over the network to achieve the optimal resource utilization source:

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

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

Black-Box System Parameter 1 Parameter 2 Parameter n System Response Parameter Adjustments Algorithm#3 Algorithm#2 Algorithm#1 Budget Allocator Comparator Current BestSoFar BestSoFar Metric Number of Experiments

 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

 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

 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

Black-Box System Parameter 1 Parameter 2 Parameter n System Response Parameter Adjustments Algorithm#3 Algorithm#2 Algorithm#1 Budget Allocator Comparator Current BestSoFar BestSoFar Metric Number of Experiments

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

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

 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

 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

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.

search phase search interval search interval = 5,000 sec Simulation duration = 13,000 sec search phase A two-phase approach: search, no-search

 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?

RRS (Avg throughput=7,698.24) PTAS (Avg throughput= ) GA (Avg throughput=7,596.68) SA (Avg throughput=7,322.22)

Comparison of PTAS with RRS, SA, and GA for using different search phase lengths and different number of rounds for PTAS Although not always, PTAS outperforms on average.

 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