2006

1. MATE:MPLS Adaptive Traffic EngineeringAnwar Elwalid Cheng Jin Steven Low Indra WidjajaBell Labs Michigan altech Fujitsu 2006

2. Talk Outline • MPLS Traffic Engineering • Overview of MATE • Theoretical Results • Simulation Results

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

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

5. Label Substitution • 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.

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

7. The Need for Traffic Engineering • No automatic load balancing among LSPs

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

9. Two-State Adaptive Traffic Engineering

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

11. Traffic Engineering Problem • For each Ingress-Egress pair s: • Input • Offered Load: as • Set of LSPs: Ps (an LSP p) • Output • Vector of traffic splits: lslsp= as

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

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

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

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

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

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

18. Experiment Setup

19. Aggregate Utilization on Shared Links

20. Packet Loss on Shared Links

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