MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid Cheng Jin Steven Low Indra Widjaja Bell Labs Michigan altech Fujitsu 2006

Talk Outline n MPLS Traffic Engineering n Overview of MATE n Theoretical Results n Simulation Results

Best of Both Worlds n MPLS + IP form a middle ground that combines the best of IP and the best of virtual circuit switching technologies n ATM and Frame Relay cannot easily come to the middle so IP has!

Label Encapsulation n MPLS – between L2 and L3 n MPLS Encapsulation is specified over various media types. Top labels may use existing format, lower label(s) use a new “shim” label format.

Label Substitution n Have a friend go to B ahead of you using one of the two routing techniques (hop-hop, source). At every road they reserve a lane just for you. At every intersection they post a big sign that says for a given lane which way to turn and what new lane to take.

MPLS Explicit Routing n Multiple Label-Switched Paths (LSPs) between an ingress-egress pair can be efficiently established

The Need for Traffic Engineering n No automatic load balancing among LSPs

Design Goals n Distributed load-balancing algorithm n Need no extra network support n Minimal packet reordering required n General framework for traffic engineering n Internet Draft: draft-widjaja-mpls-mate-02.txt

Two-State Adaptive Traffic Engineering

Functional Units in Ingress LSRs n Probe packets are sent to estimate the relative one- way mean packet delay and packet loss rate along the LSP

Traffic Engineering Problem n For each Ingress-Egress pair s: n Input u Offered Load: a s u Set of LSPs: P s (an LSP p) n Output  Vector of traffic splits: s sp = a s

Problem Formulation n Define a cost C p, for an LSP p, as a function of link utilization l sp n Each ingress-egress pair minimizes the sum of the cost function of each LSP subject to a feasible traffic split Min C( s ) = C p ( sp ) Min C( s ) = C p ( sp ) s.t. sp = a s, sp > 0 s.t. sp = a s, sp > 0

Understanding the Cost Function n Not necessarily a perfect cost function n Help steer network toward desirable operating point n Allows systematic derivation and refinement of practical traffic engineering schemes

Solution Approach n Optimality Criterion  Optimal if paths with positive flow have minimum (and equal) cost derivatives n Gradient Projection Algorithm u Shift traffic from paths with highest derivatives to paths with lowest derivatives by a small amount each iteration

Asynchronous Environment n Feedback delays (probe measurements): u non-negligible u different delays for LSPs u time-varying n Many ingress-egress routers shift traffic u independently u at different times u likely with different frequencies

Convergence under Asynchronous Conditions n The algorithm will converge provided the cost function satisfies certain requirements Starting from any initial rate vector (0), the limit point of the sequence { (t)} is optimal, provided the step size is sufficiently small Starting from any initial rate vector (0), the limit point of the sequence { (t)} is optimal, provided the step size is sufficiently small n Bound on step size estimates the effect of asynchronism

Packet-level Discrete Event Simulator n Entities: Packets, Routers, Queues, and Links n Simulated Functional Units u Measurement and Analysis u Traffic Engineering u Assume traffic already filtered into bins n Both Poisson and Long-range dependent traffic (DAR)

Experiment Setup

Aggregate Utilization on Shared Links

Packet Loss on Shared Links

Conclusion n MPLS Adaptive Traffic Engineering u an end-to-end solution without network support u distributed load-balancing u steer networks toward “optimal” operating point under asynchronous network conditions u validated in simulation