Topology aggregation and Multi-constraint QoS

routing

Presented by Almas Ansari

Flow of the presentation

• The Scalability Problem

• Need for Topology Aggregation

• Topology Aggregation Schemes

• Assigning Values to Logical Links

• Multi-Constrained QoS Routing

• Conclusions

The Scalability Problem

• Routing consists of 2 basic tasks:

- collecting network state information

- finding a feasible path for a connection

based on this information

• Topology is usually obtained from a link state protocol like OSPF.

• As the network grows larger, it is impossible to broadcast topology to every node because it takes too much space, time and bandwidth.

• Ways to deal with this problem:- reducing the no. of topology updates (Goal : deliver as infrequently as possible without affecting routing performance.)

- topology aggregation (Goal: reduce the size of the messages without affecting routing)

- combining both

Need for Topology Aggregation

• TA: very important technique to achieve scalability.

• Reduces routing information and thereby routing table sizes by very large magnitude.

• Achieved by dividing networks into smaller, manageable routing domains.

• Internal details of a domain topology is aggregated before broadcasting.

• Inside the domain : complete view

• Outside the domain: aggregated view

• Aggregated view is used by outside nodes to make routing decisions.

• Hence aggregated topologies must be as accurate as possible.

• An efficient TA scheme must provide an adequate balance between compaction and accuracy.

Topology Aggregation Schemes

• Full Mesh• Single Node • Star• Spanning Tree• ?

All schemes suffer from varying degrees of distortion.

Full Mesh• All border nodes connected by logical links.

• A logical link has QoS parameters like a physical link.

• How to come up with these parameters?

• This is still a huge matrix of b(b-1)/2 links.

• Does not scale well.

Single Node

• One node will represent each routing domain.

• It has QoS parameters.

• Parameters may be the best, worst or average of all links.

• Sometimes the values of the diameter of the graph is used.

Star

• Here border nodes are connected via logical links to a virtual nucleus.

• Bypasses may be allowed.

Spanning Tree

• A spanning tree of all border nodes is created.

• To make the representation more accurate, start by including crucial links.

QoS Parameters – How to assign them to logical links?

• 2 types of QoS parameters:- link constraint (e.g. bandwidth)

- path constraint (e.g. delay)

• Additive or Restrictive

• Other e.g. : delay jitter, cost etc.

• A fundamental step in TA is assigning the QoS parameters to logical link.

• Choosing these values correctly is crucial because improper values may lead to rejection of supported calls (under-estimation) or crankback i.e. failure to support an accepted call (over-estimation).

• Assigning values to logical links is easier to do when one metric is under consideration

• Take best, worst or average values.

• It is very difficult to do aggregation with bounded distortion when 2 or more parameters are under consideration.

• If a link has 2 parameters b and d, we can find separate optimal paths for each b and d. If we can find a path that maximizes b as well as minimizes d, then a jointly optimal path is found.

• A jointly optimal path i.e. that provides better values for all metrics may not exist.

• In such cases, other ways to assign values are used.

Conventional approaches to assign values for multi metrics:

Single Path Parameters Approach- Decide on the most important parameter

- How to decide upon the most important parameter?- Find the best path according to this parameter

- Assign values of this path to the logical link

Multiple Path Parameters Best Case Approach- find best path between 2 border for each metric- assign the logical link the best values- aggressive method: since high possibility of crankback

Multiple Path Parameters Worst Case Approach- find worst path between 2 border for each metric- assign the logical link the worst values- under estimation method: since high possibility

of supported calls not being admitted

QoS Routing• The notion of QoS has been proposed to capture

the qualitatively or quantitatively defined performance contract between the service provider and the user application.

• QoS routing selects network routes with sufficient resources for the requested QoS parameters.

• Goal: satisfying the QoS requirement for every admitted connection.

• A QoS routing algorithm may fail to find a feasible path for a connection because:

- such a path does not exist

- the searching space of a heuristic approach

does not cover any existing a feasible path

• When this happens the system can either reject the connection or negotiate with the application for a looser QoS constraint.

Multi-Constraint QoS Routing

• Multi-Constrained QoS routing deals with finding routes that satisfy multiple independent QoS constraints.

• Is NP-Hard

• The basic QoS routing problems can be:

• Link optimization routing

e.g. b optimization routing

finding widest route from src to dst.

• Link constrained routing

e.g. b constrained routing

finding a path from src to dst such that b is not less than a certain value on all links.

Link optimization problem can be reduced to link constrained problem and then solved by a slightly modified DA or BFA.

• Path optimization routinge.g. d optimization routingleast delay path

• Path constrained routinge.g. d constrained routingd of path below a certain value

• These problems can be solved by directly by DA or BFA.

• Now consider a link has 2 parameters b and d.

• Of this we can have several combinations of routing problems.

• E.g. link-constrained path-optimizationi.e. To find the least delay path that has a bandwidth constraint

• Can be solved by a shortest path algorithm which works on a graph whose links that violate the bandwidth requirement have been pruned.

• Other four problem classes are:

- link-constrained link-optimization

- multi-link constrained

- link-constrained path-constrained

- path-constrained link-optimization

• These are solvable in polynomial time by a modified shortest path algorithm.

• Other difficult to solve problem classes are: - path-constrained path-optimization e.g. delay-constrained least-cost routing finding the least cost path with bounded delay.

- multi-path constrained e.g. delay-delay jitter constrained finding a path with bounded delay as well as bounded jitter.

• If all metrics except one take bounded integer values then the problems are solvable in polynomial time by running the EBFA.

• EBFA finds all optimal paths at each node.

• Very high space and time complexity.

• Limited granularity and Limited path heuristics can be used.

• Limited granularity heuristic uses bounded finite range to approximate QoS metrics. Problem can now be solved in polynomial time.

• Limited path heuristic limits the no. of optimal paths stored at each node, thereby reducing space complexity.

Conclusions

• TA is very important to achieve scalability.

• All TA schemes suffer from some distortion.

• Multi-Constrained QoS routing is difficult.