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MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid Cheng Jin Steven Low Indra Widjaja Bell Labs Michigan altech Fujitsu. 2006. Talk Outline. MPLS Traffic Engineering Overview of MATE Theoretical Results Simulation Results. Best of Both Worlds.
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MATE:MPLS Adaptive Traffic EngineeringAnwar Elwalid Cheng Jin Steven Low Indra WidjajaBell Labs Michigan altech Fujitsu 2006
Talk Outline • MPLS Traffic Engineering • Overview of MATE • Theoretical Results • Simulation Results
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!
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.
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.
MPLS Explicit Routing • Multiple Label-Switched Paths (LSPs) between an ingress-egress pair can be efficiently established
The Need for Traffic Engineering • No automatic load balancing among LSPs
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
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
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
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
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
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
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
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
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)
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