S. Suri, M, Waldvogel, P. Warkhede CS University of Washington Profile-Based Routing: A New...
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Transcript of S. Suri, M, Waldvogel, P. Warkhede CS University of Washington Profile-Based Routing: A New...
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