EuroFGI Workshop on IP QoS and Traffic Control An Adaptive Multi-Temporal Approach for Robust Routing Pedro CASAS & Sandrine VATON ENST Bretagne EuroFGI Workshop on IP QoS and Traffic Control Lisbon, Portugal, December 6-7 2007 P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Traffic Engineering for Routing Optimization under Traffic Uncertainty Robust TE Techniques to tackle the problem ⇒ Robust Routing Stable (static) routing to avoid potential instabilities but..... P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Traffic Engineering for Routing Optimization under Traffic Uncertainty Robust TE Techniques to tackle the problem ⇒ Robust Routing Stable (static) routing to avoid potential instabilities but..... NOTHING COMES FOR FREE ! ! P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Outline Traffic Engineering (TE) under traffic uncertainty 1 Robust and proactive TE : the Stable Robust Routing 2 A time-varying approach : the Multi-Temporal Robust Routing 3 Conclusions and Perspectives 4 P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Outline Traffic Engineering (TE) under traffic uncertainty 1 Robust and proactive TE : the Stable Robust Routing 2 A time-varying approach : the Multi-Temporal Robust Routing 3 Conclusions and Perspectives 4 P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control TE in current scenario : a challenging task Current traffic demands are highly variable and uncertain Sources of Demands Variation Unexpected Daily Periodic Events Usage Patterns Equipment Failures External Network Flash Routing Attacks Crowds Changes Spontaneous Services (P2P) P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Examples of Variations in Real Data (I) 10000 Link 47 Correlated volume changes Link 58 9000 Link 104 Link 126 8000 Unidentifiable variations Link Load (unknown unit) 7000 6000 5000 4000 3000 2000 1000 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Time (min) (a) Traffic patterns in a large Tier-2 network. P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Examples of Variations in Real Data (II) 3 2.5 x 10 External routing changes 2 Link Load (Mbps) 1.5 1 0.5 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Time (min) (b) Traffic patterns in the Abilene network. P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Large heterogeneity in current traffic demands 9000 1200 2500 1000 8000 2000 800 7000 6000 1500 600 5000 400 1000 4000 200 0 3000 500 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 7000 1200 700 6000 600 1000 5000 500 800 4000 400 3000 300 600 2000 200 400 1000 100 200 0 0 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 1200 600 2600 1000 500 2400 800 400 2200 600 300 2000 400 200 1800 200 100 1600 0 0 1400 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control The traditional approach for TE Stable Prediction-Based Routing - Static Routing Based on Traffic Matrix (TM) estimation and prediction Relies on single estimated TM or group of expected TMs to optimize routing Adaptive Routing - Dynamic Routing Load balancing in real-time Responds to instantaneous traffic demands, based on measurements. P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Multipath routing optimization Consider the following network scenario : Network topology : n nodes. L = { 1 , . . ., r } links with capacities in C = ( c 1 , c 2 , . . . , c r ). N = { OD 1 , .., OD m = n ( n − 1) } Origin-Destination traffic flows. Routing matrix R = { r l , k → l =1 .. r , k =1 .. m } , 0 � r l , k � 1. P ( k ) = { set of paths p for OD k } , k = 1 .. m . Traffic OD flows d = { d i , j → i , j =1 .. n } = ⇒ d = { d k , k =1 .. m } Links traffic (aggregated ODs traffic) y = { y l , l =1 .. r } y ( t ) = R × d ( t ) ∀ t . P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Multipath routing optimization Problem formulation Given d , C , R and P ( k ), TE seeks to optimally balance d in P ( k ) to minimize some performance criterion : P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Multipath routing optimization Problem formulation Given d , C , R and P ( k ), TE seeks to optimally balance d in P ( k ) to minimize some performance criterion : r l , k · d k y l � u max ( C , d , R ) = max = max c l c l l ∈{ 1 ... r } l ∈{ 1 ... r } k P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
EuroFGI Workshop on IP QoS and Traffic Control Multipath routing optimization Problem formulation Given d , C , R and P ( k ), TE seeks to optimally balance d in P ( k ) to minimize some performance criterion : r l , k · d k y l � u max ( C , d , R ) = max = max c l c l l ∈{ 1 ... r } l ∈{ 1 ... r } k x k p , 0 � x k p � 1, fraction of d k in p ∈ P ( k ) x k l , 0 � x k l � 1, fraction of d k in l ∈ p P. CASAS - S. VATON Computer Science Department ENST BRETAGNE
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