6.8.2002 Thesis Seminar on Networking Technology

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Riikka Susitaival Supervisor: Professor Jorma Virtamo

Instructors: Ph.D. Pirkko Kuusela Ph.D. Samuli Aalto

Networking Laboratory

6.8.2002 Thesis Seminar on Networking Technology

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• Background• Objectives• Load balancing algorithms• Routing algorithms to achieve differentiated

services• Results• Conclusion

6.8.2002 Thesis Seminar on Networking Technology

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• Current IP routing is topology driven– Routers make forwarding decisions

independently– 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

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6.8.2002 Thesis Seminar on Networking Technology

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– The most significant application is traffic engineering

– 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

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• To study MPLS architecture– Technical aspect– Traffic engineering over MPLS

• To study load balancing algorithms– Approximations– Granularity

• To develop flow allocation methods that provide differentiated services

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• 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 is ingress 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

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1. Minimum-delay routing

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6.8.2002 Thesis Seminar on Networking Technology

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2. LP-NLP optimization• 1st phase min-max optimization:

• 2nd phase: Allocate traffic to the paths obtained from 1st phase solution so that the mean delay is minimized.

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6.8.2002 Thesis Seminar on Networking Technology

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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 each link.

6.8.2002 Thesis Seminar on Networking Technology

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Routing algorithms to achieve differentiated service

• The goal is to differentiate the mean delay of different 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

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1. The weights in optimization function

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6.8.2002 Thesis Seminar on Networking Technology

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2. Optimization so that the ratio of mean delay is fixed to a parameter q.

3. Heuristic approach: The class that should achieve smaller mean delay, is routed first using the heuristic algorithm described above. Allocated traffic of first class is multiplied by a factor of (1+∆) and second class is routed.

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6.8.2002 Thesis Seminar on Networking Technology

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• Optimization using WFQ-weights1. The weights included to the optimization function

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6.8.2002 Thesis Seminar on Networking Technology

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• Alternative ways to optimize:– Straightforward optimization– Two-level procedure: traffic is first allocated without

WFQ-weights and then WFQ-weights are defined using optimization function above.

– Two-level procedure so that paths are first defined using LP-formulation and then then WFQ-parameters are defined using optimization function above.

2. The ratio of the delays at each link is fixed to a parameter q and WFQ-weights defined as function of q.

6.8.2002 Thesis Seminar on Networking Technology

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• 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

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6.8.2002 Thesis Seminar on Networking Technology

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6.8.2002 Thesis Seminar on Networking Technology

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6.8.2002 Thesis Seminar on Networking Technology

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• The use of load balancing improves performance significantly, LP-NLP algorithm reduces the computation time

• The weights used in optimization with WFQ-weights are smaller than in optimization without WFQ-weights

• Increase in mean delay is greater in optimization with WFQ-weights

• The algorithm that allocates first traffic and then determines WFQ-weights, is closest to optimal

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• 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 by

WFQ-scheduling