The basis of a QoS-based routing algorithm is a dynamic network dependent cost function that is used to find the optimal or at least a feasible route across the network. However, all QoS-based routing algorithms suffer from a major drawback. The cost function at the core of the algorithms identifies segments of the network where resources are ample and exploits them to the benefit of connections that would otherwise cross a congested portion of the network. Thus, the algorithms consume more resources than Minimum Hop routing would do when the network traffic is non-stationary and heavy. QoS-based routing, thus, wastes resources and performs poorly compared with Minimum Hop routing in the event of congestion. The crux of the discussion is that whatever is gained at low or medium network loads, is offset at high network loads. What is required is a resilient algorithm that either allows the migration of a QoS-based routing algorithm to a Minimum Hop algorithm at high loads or an algorithm that merges Minimum Hop and QoS characteristics. The study opts for the latter approach and proposes and exhibits a hop constrained QoS routing algorithm that outperforms traditional QoS routing algorithms during simulation. This routing technique is based on an approximation algorithm that solves the hop constrained routing problem. The algorithm is derived from a dynamic programming FPAS scheme and finds the shortest walk for a single source destination pair in a graph with restricted number of hops when all the edge costs are non-negative. Simulated results demonstrate that routing technique based on the algorithm is robust to changes in the traffic pattern and consistently outperforms other QoS based routing techniques under heavy load conditions.
QOS aware applications have propelled the development of two complementary technologies, Multicasting and Differentiated Services. To provide the required QOS on the Internet, either the bandwidth needs to be increased (Multicasting) or limited bandwidth prioritized among users (DiffServ). Although, the bandwidth on the Internet is continually increasing, the backbone is still insufficient to support QOS without resource allocations. Hence, there is a need to map multicasting in a DiffServ Environment to conserve network bandwidth and to provision this bandwidth in an appropriate fashion. In this regard, two issues have to be addressed. One, the key difference between multicast and DiffServe routing is the structure of the multicast tree. This tree is maintained in multicast aware routers whereas in DiffServe, the core routers maintain no state information regarding the flows. Second, the task of restructuring the multicast tree when members join/leave. Currently, the first issue is addressed by embedding the multicast information within the packet itself as an additional header field. In this paper, we propose a neural network based heuristic approach to address the second problem of routing in a dynamic DiffServe Multicast environment.
Many dynamic multicast routing algorithms have been proposed. The greedy algorithm creates a near optimal tree when a node is added but requires many query/reply messages. The PSPT algorithm cannot construct a cost optimal tree. The VTDM algorithm requires the estimated number of nodes that will join and is not flexible. The problem of building an optimal tree to satisfy QOS requirements at minimum cost and taking minimum network resources is NP- complete and none of the above solutions give an optimal solution.
We have modeled this combinatorial optimization as a nonlinear programming problem and trained an artificial neural network to solve the problem. The problem is tractable only when the QOS parameters are combined into DiffServe classes because of the flows are short-liv