**2.2.2 On-demand signalling**

Compared to unsolicited signalling, on-demand signalling works the other way around. When a node (called the requesting node) requires state information, it queries the other nodes (called the serving nodes) for this information. Thus, on-demand signalling yields the state information as recent as possible, with expected benefit for the routing decisions. Furthermore, the type of state information which is queried (e.g. capacity or buffer occupancy) may vary according to the type of route that must be computed. On the other hand, since the signalling procedure is triggered for each route computation, the amount of traffic generated by on-demand signalling is likely to be higher than with unsolicited signalling. Additionally, the requesting node has to gather complete information before initiating the route computation. On-demand signalling is more convenient for connection oriented networks, where the source node requests the network state information from other nodes before setting up a connection and then the route to destination node is computed. As the number of packets during a signalling session is high, additional mechanisms (caching, snooping) have to be devised, in order to limit the number of signalling packets (Franck & Maral, 2002a).

### **2.3 Computing routes**

In the case of per-hop packet-switched routing routes cannot be computed on demand. Instead, routing tables are pre-computed for all nodes periodically or in response to a significant change in link costs, thus defining routing update intervals. Link-cost metrics for the delay sensitive traffic are typical additive metrics, and thus the shortest routes are typically calculated using the Dijkstra algorithm. The main feature of an additive metric is that the total cost for any path is a sum of costs of individual links.

On the other hand, the link cost for the throughput sensitive traffic is a concave metric. Thus, the total cost for any path equals the one on the link with minimum cost. A typical optimization criterion for the throughput sensitive traffic is to find the paths within minimum hop count with the maximum available bandwidth. Minimum hop count is an additional constraint, which is used to minimize the use of resources. The Bellman-Ford shortest path algorithm is well suited to compute paths of the maximum available bandwidths within a minimum hop count. It is a property of the Bellman-Ford algorithm that, at its *hth* iteration, it identifies the optimal path (in our context the path with the maximum available bandwidth) between the source and each destination not more than *h* hops away. In other words, because the Bellman-Ford algorithm progresses by increasing the hop count, it provides the hop count of a path as a side result, which can be used as a second optimization criterion.

Regardless of the type of traffic the second shortest path with disjoint first link can be calculated by eliminating the first link on the shortest route (i.e. *LC*l is set to infinity for delay sensitive traffic and to 0 in the case of throughput sensitive traffic) and using Dijkstra and Bellman Ford algorithm on such modified network. The alternative paths are used in the case of adaptive forwarding.
