QoS Routing Mechanisms and OSPF Extensions

Roch A. Guerin, Ariel Orda, and Douglas Williams

Propose extensions to OSPF to support QoS

They discuss path selection algorithms link advertisement mechanisms

Goal: improve likelihood of being routed on a

path capable of providing the desired QoS

As little impact on existing OSPF protocol as possible

trade off between simplicity and optimality

Framework

Network design: both best-effort and QoS packets all routers are QoS capable routers can advertise the amount of

resources they have left focus on unicast

flow specifies its desired QoSprotocol returns a path based on

requirementspath could be next-hop or full pathonce a path is identified the flow is

pinned to it

Simplifications

Focus on satisfying bandwidth requirements over minimum number of hops

does not take into account delay sensitive flows

Protocol metrics

Link available bandwidth - current amount of available bandwidth

Hop count - used as a measure of path cost, assume that a path with less hops would consume less network resources

Exact pre-computed paths

Based on Bellman-Ford shortest path algorithm

modify BF to compute maximum available bandwidth on link

At ith iteration of BF we can identify the path with the maximum available bandwidth among paths of at most i hops

On-demand Computationof Paths

More useful in instances where there are a limited number of requests for QoS requests

Based on how often you update the routing tables, on demand can yield better routes due to more updated information

no need to store a routing table

Uses Dijkstra’s algorithmSince Dijkstra is already used in OSPF

implementation would be easierAs algorithm runs only consider links

which can support the bandwidth requirement

Sort the list of next hops based on maximum available bandwidth

Approximate Pre-Computed Paths

Dijkstra-basedQuantize bandwidth valuesmay result in loss of accuracyThe range of possible bandwidth

values are mapped onto fixed number of quantized values (for example low, medium, and high)

Minimum hop count paths are computed for each of the quantized values

flows are then assigned to the path that can carry the smallest value equal to or greater than what it requests

While the algorithm runs, for each of the quantized values it removes from consideration all links which cannot support the required bandwidth

Complexity of Path Selection Algorithms

Worst case Dijkstra O(M log N)Worst case Bellman-Ford 0(H*M)

M: number of edges N: number of links H: upper bound on the number of hops

in a shortest path

H can be as large as N-1, but in practice is usually much smaller

can impose restriction on H making H<<N effectively making the complexity O(M)

Bellman-Ford also allows hop count as a secondary criterion without modifications such as quantizing

If we use approximate pre-computed path selection with Q quantized values its worst case complexity is O(Q*(M log N))

comparable to Bellman-Ford at the expense of sub-optimal solutions

Advertisement of Link State Information

Assumed that each router maintains updated database of the network topology, including the current available bandwidth of each link

Link-state information distribution is according to OSPF specification

The important question is when to distribute this information

An ideal situation would be one where all routers have the most up to date view of the network

This would call for very frequent updates, one every time the available bandwidth on a link changes

neither scalable nor practical

Could use periodic updates Major changes in available bandwidth

could go unnoticed for a full periodBetter to use a hybrid of the two

strategiesSend an update when the current

value of the link is above or below some threshold (say 2)

This implies that when a flow needs b units of bandwidth links with advertised bandwidths of 2b are “safe” and we can discard paths with b/2.

This leads to a new metric, certaintyThere are several ways to incorporate

this into the path selection process

Probabilistic Approach

The bandwidth value can be thought of as a random variable in the range (bl/2, 2*bl) where bl is the last advertised value

assuming uniform distribution you can compute for each bandwidth requirement b the success probability of the link and then run the Bellman Ford algorithm on it

This would mean that we would need to compute a different path for each value of b

Thus this approach is too complex in the case of pre-computed routes.

Simple Approach

Run the standard Bellman Ford algorithm outputting a N*H routing table

let 0.5 <= be the risk proneness of the decision maker, where lower values indicate a higher level of risk

let Hr be the number of hops the decision maker is willing to trade for safety

Then with a request for b units of bandwidth: from the table get hmin, the minimal number

of hops for a path with at least *b units from the table get hmax, the minimal number

of hops on a “safe” path if hmin + Hr >= hmax choose the safe path otherwise choose the path from the table with

maximal bandwidth having at most hmin + Hr

hops

Elaborating on Approach #2

A problem of approach #2 is that efficient paths may be disregarded

We can extend approach #2 by: erasing the links with values less than b/2 assigning a cost of 0 to safe links assigning a cost of 1 to links in the range

(b/2,2b) run the Bellman Ford algorithm with the new

cost function

This results in a huge table which identifies for each bandwidth value and for each number of hops, a path with no more than h hops that has a minimal number of non-safe links with respect to the bandwidth value

gives the decision maker more options

higher complexity in terms of running time and table size

since there are a maximum of M distinct bandwidth values the time and space complexity is O(M*H*M) and O(M*H*N)

Reducing the Complexity of Approach #3

Want to handle the problems of the simple approach without the complexity of the previous approach

Let bmax be the maximal value a link bandwidth can attain

Issue an update when a threshold is crossed

The update advertises the threshold that is closest to its current bandwidth value

This way the ratio between the actual and advertised bandwidth values are kept within the .5 … 2 range

The number of different advertised values is now O(log(bmax)) instead of O(M) as in the previous approach

time and space complexity are reduced to O(M*H*log(bmax)) and O(N*H*log(bmax))

Conclusions

The algorithms presented provide tradeoffs between complexity, accuracy, and ease of implementation

focused mainly on bandwidthfuture work:

relationship with call admission validating their claims with simulation