El Alpe d’Huez: una gesta ciclista en primera línea

> D etection by the child node. If tire child node carmot cope with tire volum e o f packets com in g from its parent and the input buffer starts to increase even after information flow control.

> D etection by the parent node. If, after the application o f information flow control, the parent’s output buffer starts to build up because one o f its children carmot cope with the volu m e o f packets being sent.

Like the previous case, the decision-m aking node should be informed o f the problem by both the congested child and its parent. I f the congested child is a parent node, the decision-m aking node can ask one or more o f its children to recormect to the parent o f the congested child directly or it can introduce a new node to share the load (see Section 4.3.4). If the cliild node is a local exchange, the only option is to share the load with another lightly loaded local exchan ge or w ith a new local exchange introduced for this purpose.

This section represents discussions and proposals on the subject o f dynam ic re-configuration according to small, unexpected variations in signalling traffic in order to avoid the creation o f bottlenecks. The testing and refinement o f the suggested m echanism s are left as proposals for the project’s future work.

5.2 O p tim ization o f the N etw ork T op ology

The m atching o f the hierarchy o f the network topology to the usage pattern can be regarded as an

optim ization problem. The objective is to find the optimum placement o f databases in a signalling tree so as to m inim ize the transactions average end-to-end delay, given the location change and call request rates exchanged between local exchanges {i.e. usage pattern) and subject to database

capacity constraint. A similar problem can be formulated in w hich the objective function to be m inim ized is the total rate o f database updates and accesses. D ynam ic programming can be used to solve this problem [121][122].

D ynam ic progranuning applies primarily to a situation such as the one presented, in w hich many decisions have to be made to m axim ize the overall performance o f the system. The system has to

be one in w hich distinct stages may be recognized and decisions at tlie later stages do not affect tlie performance o f the earlier ones. Hence the problem can be tackled starting at the leaves o f the tree

building up tlie network topology towards the root node.

R eferences [123] and [124] apply dynamic programming to the optim ization problem fonnulated

above. In [123] the objective function to be m inim ized is the total rate o f database operations, w hilst [124] formulates the problem so as to m inim ize the requests average end-to-end delay. The approaches are similar because as the average number o f database operations per transaction is m inim ized, the average requests end-to-end delay should also be reduced, and vice-versa. The approach o f [123] does not take into account in its cost function the amount o f signalhng across tlie network, hence, if the capacity o f the database is sufficiently large, then the solution is always a centralized schem e in w hich a single database is placed at the root node. If com m unication costs are introduced, even for an arbitrarily large database capacity, the centralized schem e m ight not give the optim um solution. [124] does take database operation delay and com m unication costs into account, presenting a more general solution.

The optim ization problem, as formulated in [123] and [124], is to choose the subset A o f nodes

containing databases so as to niinim ize a certain parameter, given the usage pattern and subject to the database capacity constraint. The parameter to be m inim ized can be, for exam ple, the rate o f database operations (updates and accesses) or the average transaction end-to-end delay. The dynam ic programming approach is then used by specifying functions for the constraint accum ulation and the resource accumulation. The objective is to find the minimum resource accum ulation for a given constraint accumulation. The algoritlim starts from the leaves o f the tree and su ccessively glues the solutions together until the root is reached.

This com putation can be performed for sub-domains or for the w hole network w henever changes to the usage pattern are sufiiciently significant to justify multiple re-configurations. Therefore, this method should be applied when the system is expanded, re-dim ensioned or when there is a significant change to the signalling traffic distribution across the system. The method described in the previous section should cater for sm all variations to the signalling traffic, representing small adjustments o f the network topology to alleviate transient points o f congestion.

5.3 Su m m ary

This chapter discusses how the system can be given the ability to monitor its own signalling load in order to decide w hen and how to reconfigure so that the network topology is optim ized

according to the current usage pattern. The objective is to introduce monitoring and d ecision ­ making m echanism s able to m odify the network topology dynam ically in response to changes in signalling traffic. Possible approaches to the monitoring and decision-m aking m echanism s are

presented in Section 5.1. They represent real-time adjustments to tlie network topology to avoid tlie creation o f bottlenecks due to unexpected variations o f the traffic flow.

Tliis chapter also discusses methods for finding the optimum placement o f databases in a signalling tree so as to m inim ize the transactions average end-to-end delay, given the usage pattern and subject to database capacity constraint. This is discussed in Section 5.2, tlie presented method for m atching tlie network topology to the usage pattern can also be used only to sub-dom ains o f the network. U nless restricted to sub-domains, this method can represent a large-scale re-configuration

o f the network, optim izing the topology to serve the current request distribution pattern, and hence it should be used only when changes to the usage pattern are sufficiently significant to Justify m ultiple re-configurations. The procedures described in Section 5.1 should be used to adjust the network topology dynam ically to small variations on the usage pattern. During low traffic periods

(e.g. at night), the information on usage pattern gathered over the previous intense traffic period, if

significant traffic variations have occurred, is used as input data to optim ize the topology by using the m ethod described in Section 5.2.

The m echanism s described in this chapter introduce self-m anagem ent capability and make the system capable o f adapting to an evolving environment. These m echanism s rely on the recovery and re-configuration procedures described in Chapter 4 that are the building blocks for producing such a flexible, scaleable and robust distributed system. The next chapter presents the system m odel used in the simulation and the evaluation o f the system ’s performance.

Chapter 6