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6.8.2002 Thesis Seminar on Networking Technology 1
Load balancing by MPLS indifferentiated services networks
Riikka SusitaivalSupervisor: Professor Jorma Virtamo
Instructors:Ph.D. Pirkko KuuselaPh.D. Samuli Aalto
Networking Laboratory
6.8.2002 Thesis Seminar on Networking Technology 2
Contents
• Background• Objectives• Load balancing algorithms• Routing algorithms to achieve differentiated
services• Results• Conclusion
6.8.2002 Thesis Seminar on Networking Technology 3
Background (1/3)• Current IP routing is topology driven
– Routers make forwarding decisionsindependently
– Paths selected using shortest path algorithms
• Multi Protocol Label Switching (MPLS)– combines datagram and virtual circuit
approaches– based on short labels that are used to make
forwarding decision
6.8.2002 Thesis Seminar on Networking Technology 4
Background (2/3)
6.8.2002 Thesis Seminar on Networking Technology 5
Background (3/3)
– The most significant application is trafficengineering
– Provide capabilities to split traffic
• Load balancing methods– MPLS provides tools for load balancing– Objective to
• minimize the maximum link load• minimize the mean delay
– Granularity
6.8.2002 Thesis Seminar on Networking Technology 6
Objectives of thesis
• To study MPLS architecture– Technical aspect– Traffic engineering over MPLS
• To study load balancing algorithms– Approximations– Granularity
• To develop flow allocation methods thatprovide differentiated services
6.8.2002 Thesis Seminar on Networking Technology 7
Load balancing algorithms• The objective is to minimize the mean delay• Based on the delay of M/M/1-queue• 3 different methods implemented and compared• Notation:
– Directed link (m,n) with bandwidth b(m,n)
– Traffic demand d(i,j),, where i is ingress node and jegress node
– R(i,j),k=d(i,j) , if k is egress node, R(i,j),k=-d(i,j) , if k isingress node, otherwise R(i,j),k =0
– x(i,j),(m,n) traffic of ingress-egress pair (i,j) on link (m,n)
6.8.2002 Thesis Seminar on Networking Technology 8
1. Minimum-delay routing
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6.8.2002 Thesis Seminar on Networking Technology 9
2. LP-NLP optimization• 1st phase min-max optimization:
• 2nd phase: Allocate traffic to the paths obtainedfrom 1st phase solution so that the mean delay isminimized.
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6.8.2002 Thesis Seminar on Networking Technology 10
3. Heuristics:• Divide traffic into streams according to the level
of granularity.• Route each stream to the network using Dijkstra’s
algorithm in(i) ascending order in terms of traffic intensity(ii) descending order in terms of traffic intensity(iii) descending order in terms of the mean delay.
• Use the delay of M/M/1/queue as cost of eachlink.
6.8.2002 Thesis Seminar on Networking Technology 11
Routing algorithms to achievedifferentiated service
• The goal is to differentiate the mean delay ofdifferent classes (gold and silver), two approaches:– Optimization that relies on routing only– Optimization that uses WFQ-weights to achieve
difference• Weighted Fair Queueing (WFQ) provides desired
portion of bandwidth to each service class
• Approximations
6.8.2002 Thesis Seminar on Networking Technology 12
– The weights in optimization function
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6.8.2002 Thesis Seminar on Networking Technology 13
2. Optimization so that the ratio of mean delay isfixed to a parameter q.
3. Heuristic approach: The class that shouldachieve smaller mean delay, is routed firstusing the heuristic algorithm described above.Allocated traffic of first class is multiplied bya factor of (1+∆) and second class is routed.
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6.8.2002 Thesis Seminar on Networking Technology 14
• Optimization using WFQ-weights1. The weights included to the optimization function
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6.8.2002 Thesis Seminar on Networking Technology 15
• Alternative ways to optimize:– Straightforward optimization– Two-level procedure: traffic is first allocated without
WFQ-weights and then WFQ-weights are defined usingoptimization function above.
– Two-level procedure so that paths are first defined usingLP-formulation and then then WFQ-parameters aredefined using optimization function above.
2. The ratio of the delays at each link is fixed toa parameter q and WFQ-weights defined asfunction of q.
6.8.2002 Thesis Seminar on Networking Technology 16
Results (1/5)
• Test-network:– 10 nodes– 52 links– 72 ingress-egress pairs
• 2 classes, gold and silver– Equal traffic matrices
6.8.2002 Thesis Seminar on Networking Technology 17
Results (2/5)
0 5 10 15 20 25 30Granularity
0.000125
0.00013
0.000135
0.00014
0.000145
ehTnaem
yaled
The mean delay as the function of granularity
iii
ii
i
6.8.2002 Thesis Seminar on Networking Technology 18
Results (3/5)
60 70 80 90 100Load as % of maximum
0.0002
0.0005
0.001
0.002
0.005
0.01
ehTnaem
yaled
0.0002
0.0005
0.001
0.002
0.005
0.01
The mean delay as the function of the percentage of maximum load
Heuristics
LP-NLP
Minimum-delay
6.8.2002 Thesis Seminar on Networking Technology 19
Results (4/5)
1 1.2 1.4 1.6 1.8 2The ratio of mean delay
0.00013
0.00014
0.00015
0.00016
0.00017
ehTnaem
yaled
The mean delayas the functionof the ratioof mean delay
Heuristics
Fixed
Weights
6.8.2002 Thesis Seminar on Networking Technology 20
Results (5/5)
1 1.5 2 2.5 3 3.5 4 4.5The ratio of mean delay
0.00025
0.0003
0.00035
0.0004
ehTnaem
yaled
The mean delay as the function of the ratio of mean delay
Fixed delays, vers. 2Fixed delays, vers. 1
LP- NLPTwo- step, iterative
Two- step, version 2Two- step, version 1
Straightforward
6.8.2002 Thesis Seminar on Networking Technology 21
Conclusions
• The use of load balancing improves performancesignificantly, LP-NLP algorithm reduces thecomputation time
• The weights used in optimization with WFQ-weights are smaller than in optimization withoutWFQ-weights
• Increase in mean delay is greater in optimizationwith WFQ-weights
• The algorithm that allocates first traffic and thendetermines WFQ-weights, is closest to optimal
6.8.2002 Thesis Seminar on Networking Technology 22
Further work
• More optimization, variations of– Topology and size of network– The number of traffic classes– Unequal traffic demand matrices between
classes
• Modeling the actual bandwidth provided byWFQ-scheduling