S. Suri, M, Waldvogel, P. Warkhede CS University of Washington Profile-Based Routing: A New Framework for MPLS Traffic Engineering.

S. Suri, M, Waldvogel, P. WarkhedeCS

University of Washington

Profile-Based Routing: A New Framework for

MPLS Traffic Engineering

Overview

• Dynamic routing of bandwidth guaranteed flows

• Online• Goal –minimize number of rejections–maximize network utilization

Assumptions• ingress-egress nodes are known• traffic profile between pairs of ingress-

egress nodes are known– aggregate expected traffic between ie pairs– inferred from SLA or measured– ex: avg bw requirement over a certain time

period– profile is a good predictor

• MPLS networks

Routing Requirements

• No splitting: A flow should be routed on a single path

• Online routing: No knowledge of future requests

• Must be fast and scalable• Should be able to handle additional

policy constraints• Traffic Profile

Motivation and Review

Current routing schemes

• Shortest path: – simple – may create bottlenecks– may lead low network utilization

• Widest Shortest Path: choose shortest path with largest residual capacity

• Minimum Interference Routing (MIRA) (by Kodialam & Lakshman):

Idea: avoid routing a flow along paths that can reduce max-flow value between some other ie pair

– no true admission control– may cause high # of rejections, low utilization– computationally very expensive

Minimum Inference Routing (MIRA)

INPUT: G(N,L) with residual capacities Request ((a,b),D)

OUTPUT: A route between (a,b)

ALGORITHM: – for each ie pair\(a,b)

•compute maxflow, critical links and weights– eliminate links with residual capacity<D– use Dijkstra to compute shortest path– update residual capacities along the path – route the demand

Example I

• Online LSP requests arrives in order (S0,D0), (S1,D1), (S2,D2),..,(Sn,Dn) with bandwidth requirement of 1.

Example II

• Online LSP requests arrives in order (S0,D), (S1,D), (S2,D),..,(Sn,D) with bandwidth requirement of n,1,1,..,1.

Example III

• Online LSP requests arrives in order (S0,D),..,(S0,D), (n of them, with bw=1) & (S1,D), (S2,D),..,(Sn,D) with bw=1

Profile-Based Routing

Given a set of LSP requests, what is themax. number of requests that can berouted? NP-Complete.

Problem: Unsplittability

Two phases:• Offline (Preprocessing) phase: use multi-

commodity flow framework on traffic profiles

Offline phase: (cont.)

Goal: route as much commodity as possible

Linear Programming: • update G to have feasible solution

always

Offline phase: (cont.)

– xi(e) amount of commodity i routed through edge e

–Solve for G’

with appropriate constraints.

Output: xi(e)

To maximize network utilization, e’s capacity is preallocated for each class.

• Online phase:

– (on each edge, residual capacities for each traffic class is kept)

– route each LSP request as they arrive

–update appropriate residuals

RESULTS

• Worst-Case Results

RESULTS (cont.)• Simulation bandwidth [1,..,4]

RESULTS (cont.)• Effect of increasing maximum

bandwidth requested [1,..,48]

RESULTS (cont.)

• Bandwidth Fragmentation– causes deviation from upper bound– how bad is it?

RESULTS (cont.)

Amount of bw wasted increases with larger

requests, but small (4% of link capacity at worst)

• What if expected flows aren’t requested?

To measure, look at the snapshots:– what fraction of incoming requests

accepted – if PBR is aggressively rejecting at the

beginning, performance will be lower at the beginning

RESULTS (cont.)

Conclusion and Extensions

• accepts more flows• computationally more efficient• preprocessing phase can be

extended by using different cost functions to provide – minimum service level– fairness

Conclusion and Extensions

• what if profiles are not accurate, how to track it

• if a request does not arrive for a long time, can we make resources available to others in bw guaranteed environment