bilal gonen university of alaska anchorage murat yuksel university of nevada, reno
TRANSCRIPT
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