Load Sensitive Routing Protocol for Providing QoS in Best Effort Network

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Load Sensitive Routing Protocol for

Providing QoSin Best Effort

Network

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Motivation

Real time applications like audio and video conferencing, VoIP requires QoS from the Internet to have satisfactory performance.

Internet largely support best effort traffic and Open Shortest Path First (OSPF) is one of the most widely used routing protocols.

In OSPF, when a packet experiences congestion, the routing subsystem cannot send it through alternate path. Thus, it fails in providing Quality of Service. So there is a need to provide QoS routing in networks.

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Advantages of LSR algorithm

The Load Sensitive Routing algorithm implements QoS routing in a better way. It localizes the QoS routing

changes to the region where QoS has deteriorated no flooding Less overhead scalability.

It chooses loop free alternate paths for routing packet No separate loop detection Interoperate with OSPF routers

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Control Messages

Congestion notification Sent to all the neighbors when a link congestion is

detected When neighbors receive this congestion notification they

reroute packets through alternate next hop (three different ways of finding the alternate next hops are explained later)

Congestion over Sent to all the neighbors when a link congestion is over Neighbors revert back to routing packets through OSPF

next hops

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LSR (Contd)

LSR eligible neighbor Different nexthop used for alternate path Chosen based on OSPF property (which leads to loop free routing)

hop_count(ospf_nexthop, D) < hop_count(curr_node, D) ospf_cost(ospf_nexthop, D) < ospf_cost(curr_node, D)

If Node(Q) is neighbor of Node(P) for destination Node(D) and a’ * hop_count(Q, D) + b’ * ospf_cost(Q, D) < a’ * hop_count(P, D) + b’ *

ospf_cost(P, D) (from the above ospf property) => hop_count(Q, D) + b * ospf_cost(Q, D) < hop_count(P, D) + b *

ospf_cost(P, D)

Then Node (Q) will be LSR eligible neighbor for Node(P). b is called LSR Coefficient.

The task is then to determine LSR coefficient b

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LSR Contd…

Calculation of LSR coefficients b is global (same for all nodes) for a particular

destination b is local

b is global LSR: b is chosen such that the total number of alternate

paths (for a particular destination) is maximized. Check for each possible values of b and set it to the one that

gives maximum number of alternate paths E-LSR : Maximize total number of alternate paths subject to

the constraint that maximum number of nodes have at least one alternate path

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The Efficient Load Sensitive Routing

Algorithm (E-LSR) Objective of LSR Maximize total number of

alternate paths in network.

Objective of E-LSR Maximize total number of

alternate paths subject to

the constraint that maximum number of nodes have at least one alternate path.

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Proposed Algorithm for Coefficient Calculation

Less than and Greater than Constraints on b value.

Node(P) forwards packet to Node(Q) if HC(Q, D) + b * OC(Q, D) < HC(P, D) + b * OC(P, D)

If (HC (Q, D) < HC (P, D) and OC(Q, D) ≤ OC(P, D)) b ≥ 0

If (HC(Q, D) < HC(P, D) and OC(Q, D) > OC(P, D)) b < ((HC(P, D) – HC(Q, D) / (OC(Q, D) – OC(P, D))

If (HC(Q, D) ≥ HC(P, D) and OC(Q, D) < OC(P, D)) b > (HC(Q, D) - HC(P, D)) / (OC(P, D) - OC(Q, D))

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Coefficient Calculation (Contd…)

Necessary parameters gi: ith Greater than Constraint for

destination d. li: ith Less than Constraint of Node(i) for

destination d.

OC(A, C) = 6, OC(E, C) = 5, OC(F, C) = 8, OC(G, C) = 9HC(A, C) = 2, HC(E, C) = 3, HC(F, C) = 1, HC(G, C) = 1E – A: 3 + 5 * b < 2 + 6 * b => b > 1 (greater than constraint)F – A: 1 + 8 * b < 2 + 6 * b => b < 1/2 (less than constraint)G – A: 1 + 9 * b < 2 + 6 * b => b < 1/3 (less than constraint)

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Sort the greater than constraints such that g1 < g2 < g3 < … < gm

Sort the less than constraints such that l1 < l2 < l3 < … < ln

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Different Cases for Coefficient

Calculation Algorithm

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Objective Function Calculates two parameters

n: Number of nodes having at least one alternate path. m: Total number of alternate paths other than n.

Returns N * N * n + m

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Local Coefficient Based LSR (L-LSR) b is local

For a particular destination, each node can choose its own local L-LSR coeffiecient denoted as b(vi,D)

but L-LSR coefficient is assigned such a way that the loop-free property is still maintained Calculation of b is more complex We use a graph theoretic approach

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Building QoS graph

Edges along the ospf path have a weight of infinity For all other edges (called cross-edge)

weight is assigned as per the “out-degree” of the node But while calculating out-degree of a node do not include

any ospf edges weights are assigned to the cross edge according to the

out-degree cross-edges are added to the sink-tree only for nodes

along the QoS paths

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Example Topology

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Example sink tree

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Example QoS graph

QoS path:A-B-C-D

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Building Acyclic QoS graph

Addition of cross-edges could introduce loops We use minimum feedback arc set (FAS)

algorithm to break the cycles in the graph we actually remove edges with maximum weight

(in the cycle) while breaking cycle we want to target a node which has more alternate path

(more weight) this acyclic graph represents the alternate paths through

which nodes can send packets during congestion

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Calculating L-LSR coefficient

Calculated from the acyclic QoS graph If Node(vi) can choose Node(vj) as its L-LSR

next hop then

HC(vj, D) + b(vj, D) * OC(vj,D) <

HC(vi, D) + b(vi, D) * OC(vi,D) b’(vj, D) – b’(vi, D) < weight(vj, vi) (1)where

weight(vj, vi) = HC(vi, D) - HC(vj, D) (2)

b’(vi, D) = b(vi, D) * OC(vi,D)

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(1) can be represented as a constraint graph there is a directed edge from Node(vj) to Node(vi)

with weight weigth(vj, vi) constrained graph can be obtained by reversing

the direction of edges of acyclic QoS graph and assigning weights according to (2)

Finally, the L-LSR coefficients are calculated using (1)

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Constraint Graph of example topology

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for assigning b traversal starts from D similar to BFS. But a node is visited only

when all its incoming edges are visited From D we first visit Y and Z

(cannot visit X from D) and compute b for Y and Z (such that (1) is satisfied) In the next round we visit Y and then we can visit X and determine its b

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Simulation Setup Simulation Parameters

Congestion Threshold 90% Congestion detection Interval

1 sec Cost of link is assigned as

Cost = 1000000 / bandwidth in bps Traffic Scenarios

Scenario A Voice Traffic :

CBR with bandwidth 64kbps( packet size :160bytes, Interval: 0.02 sec)

Scenario B Data Traffic :

Exponential ON / OFF ( packet size : 576 bytes, mean ON period : 50 msec and mean OFF period :50msec, average rate : 128 kbps)

Cross Traffic Randomly selected Source and Destination exchange Traffic which follows

Poisson traffic with average rate of 32kbps (sent in both the scenario A and B)

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Simulation Topology Two QoS paths:

0-1-2-3-4-5

10-9-8-7-6-5

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Delay Scenario-A Path(10,5)

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Delay Scenario-B Path(10,5)

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PPD Scenario-A Path(10,5)

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PPD Scenario-B Path(10,5)

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Delay Scenario-A Path(0,5)

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Delay Scenario-B Path(0,5)

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PPD Scenario-A Path(0,5)

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PPD Scenario-B Path(0,5)

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Conclusion

We presented an OSPF-based Load Sensitive Routing protocol Three different methods of selecting alternate paths

based on loop free property of OSPF, hence does not need separate loop detection can interoperate with OSPF routers

provides QoS in terms of delay and packet drop L-LSR performs the best among the LSR family of

protocols much better performance than OSPF

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References

1. A. Sahoo, “An OSPF Based Load-Sensitive QoS Routing Algorithm using Alternate Paths,” in IEEE International Conference on Computer Communication Networks, October 2002.

2. G. Apostolopoulos, R. Guerin, S. Kamat, A. Orda, A. Przygienda, and D.Williams. “QoS routing mechanisms and OSPF extensions”. Internet Request for Comments (RFC2676), April 1999.

3. A. Segall, P. Bhagwat, and A. Krishna. “QoS Routing Using Alternate Paths”. Journal of High Speed Networks, 7(2): 141–158, 1998.

4. Z. Wang and J. Crowcroft. “Shortest path first with emergency exits”. ACM SIGCOMM 90, pages 166–176, Sept 1990.

5. Andrew S. Tanenbaum. Computer Networks. Prentice-Hall India, Fourth edition, 2003.

6. Camil Demetrescu and Irene Finocchi. Combinatorial algorithms for feedback problems in directed graphs.Inf. Process Lett. 86(3) :129-136 ,2003

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Load Sensitive Routing Protocol for Providing QoS in Best Effort Network