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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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Coefficient Calculation (Contd…)
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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Coefficient Calculation (Contd…)
Different Cases for Coefficient
Calculation Algorithm
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Coefficient Calculation (Contd…)
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Coefficient Calculation (Contd…)
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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Calculating L-LSR coefficient
(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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Calculating L-LSR coefficient
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