Network Configuration and Management viaTwo-Phase Online

Optimization

Bilal GonenUniversity of Alaska

Anchorage

Murat YukselUniversity of Nevada,

Reno

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..

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

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source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

A Real-world Optimization Problem(Traffic Engineering)

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congestion

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Setting the Link Weights

Inversely proportional to link capacity

Proportional to propagation delayNetwork-wide optimization based on

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source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

Setting the Link Weights

Empirical way: Network administrator experience

Problems: error-prone, not scalable

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source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

Traffic Engineering Problem

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

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source: http://www.cs.princeton.edu/courses/archive/spr06/cos461/

Motivation: Problem Definition

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?

Motivation: Problem Definition

Black-Box System

Parameter 1

Parameter 2

Parameter n

System Response

Can we try all possibilities? (Exhaustive search)

Assume 1 ≤ Xi ≤ 10 , i=1:5Step Size = 1105 = 100,000

If one try = 1 secthen 100,000 sec ≈ 28 hours

For 10 parameters ≈ 317 years

Motivation: Big Picture

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

PTAS : Probabilistic Trans-Algorithmic Search

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 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

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

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

Resource Allocation Mechanism

PTAS : Probabilistic Trans-Algorithmic Search

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

Resource Allocation Mechanism

Total Budget = 1500

300 300 300 300 300

Round budget = 300

Algorithm-1

Round-1

budget=100

Algorithm-2

Algorithm-3

budget=100

budget=100

WinnerAlgorithm-1

Round-2

budget=110

Algorithm-2

Algorithm-3

budget=98

budget=92

Winner

Algorithm-1

Round-3

budget=106

Algorithm-2

Algorithm-3

budget=90

budget=104

Winner

Algorithm-1

Round-4

budget=120

Algorithm-2

Algorithm-3

budget=92

budget=88

Winner

Algorithm-1

Round-5

budget=110

Algorithm-2

Algorithm-3

budget=102

budget=88

Winner

RRS is rewarded in the 2nd round.

Resource Allocation Mechanism

budget From To budget From To budget From To RRS SA GA RRS SA GA RRS SA GA1 72 73440 92820 72 73440 74460 72 73440 76500 0.26 0.01 0.04 35.69 1.88 5.63 93.29 93.29 93.292 93 92820 92820 59 92820 100980 64 92820 92820 0.00 0.09 0.00 0.00 43.20 0.00 78.89 122.09 78.893 78 100980 102000 83 100980 100980 55 100980 100980 0.01 0.00 0.00 43.20 0.00 0.00 107.69 107.69 64.494 107 102000 102000 69 102000 102000 40 102000 102000 0.00 0.00 0.00 14.40 14.40 14.40 107.69 107.69 64.495 107 102000 102000 69 102000 102000 40 102000 103020 0.00 0.00 0.01 0.00 0.00 43.20 93.29 93.29 93.29

Next Round BudgetRound

RRS SA GAPercentage

ImprovementEarned from SoftBudget

RRS is the winner in the 1st round.

GA is the second in the 1st round.

SA is the third in the 1st round.

SA is punished more in the 2nd round.

GA is punished in the 2nd round.SA is punished in the 2nd round but

rewarded in the 3rd round.

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

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

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.

PTAS with No System Model:IGP Link Weights Optimization

130000 5000 6500 1150010000

search phase

search intervalsearch interval

search interval = 5,000 sec

Simulation duration = 13,000 sec

search phase

A two-phase approach: search, no-search

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?

PTAS with No System Model:IGP Link Weights Optimization

RRS (Avg throughput=7,698.24)

PTAS (Avg throughput=7708.21)GA (Avg throughput=7,596.68)

SA (Avg throughput=7,322.22)

PTAS with No System Model:IGP Link Weights Optimization

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.

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

Questions, Comments