Analysis of Quality of Service Routing Algorithms

2011 ◽  
pp. 159-169
Author(s):  
E.George Dharma Prakash Raj ◽  
Sinthu Janita Prakash ◽  
S.V.Kasmir Raja
Keyword(s):  
Multicast Routing ◽  
Source Node ◽  
Destination Node ◽  
Path Delay ◽  
Quality Of Service Routing ◽  
Routing Problem ◽  
Unicast Routing ◽  
Service Routing ◽  
Feasible Path

The routing problems can be divided into two major classes. They are 1) Unicast routing and 2) Multicast routing. The Unicast routing problem is as follows. Given a source node sr, a destination node dn, a set of QoS constraints qc and an optimization goal (optional), find the best feasible path from sr to dn, which satisfies qc. The Multicast routing problem is as follows. Given a source node sr, a set st of destination nodes, a set of constraints cts and an optimization goal (optional), find the best feasible path covering sr and all nodes in st, which satisfies cts. This article presents two such Unicast QoS based algorithms called as Source Routing and the proposed Heuristic Routing. A Client Server based model has been generated to study the performance of the two algorithms with respect to the message overhead, response time and path delay. The Experiments and the results are analyzed.

An Optimal Quality of Service Routing Model for Multicast Network

2011 ◽  
Vol 211-212 ◽  
pp. 988-992
Author(s):  
Shao Wei Feng ◽  
Jing Zhang ◽  
Rui Wang
Keyword(s):  
Optimal Solution ◽  
Total Revenue ◽  
Quality Of Service Routing ◽  
Primal Problem ◽  
Path Constraints ◽  
Service Routing ◽  
Feasible Path ◽  
Feasible Solutions ◽  
Optimal Quality

The QoS requirement of a connection is given as a set of constraints, which can be link constraints, path constraints, or tree constraints. A feasible path (tree) is one that has sufficient residual resources to satisfy the QoS constraints of a connection. In this solution some complicated constraint are relaxed and the primal problem is decomposed into several sub problems. Primal feasible solutions are obtained by some heuristics and a link set adjusting method is proposed by the aggregate flow on links. In computational experiments, the proposed algorithm determines solutions that are within a few percent of an optimal solution with 30 nodes in the QoS model. In terms of performance, our solution has more significant improvement than simple heuristics. The improvement on the total revenue can reach 12% on the average in the QoS model. The improvement of heuristics 2 in the QoS model on the total revenue can reach 8% on the average.

Tabu Search for Low-Cost Dynamic Multicast Tree Generation with Quality of Service Guarantees

2015 ◽  
Vol 24 (4) ◽  
pp. 479-489
Author(s):  
Muhammad Atif Tahir ◽  
Asif Jamshed ◽  
Habib-ur Rehman ◽  
Yassine Daadaa
Keyword(s):  
Quality Of Service ◽  
Tabu Search ◽  
Multicast Routing ◽  
Low Cost ◽  
Multicast Tree ◽  
Source Node ◽  
Delay Constraint ◽  
Routing Problem ◽  
Dynamic Multicast

AbstractIn a communication network with a source node, a multicast tree is defined as a tree rooted at the source node and all its leaves being recipients of the multicast originating at the source. The tree or bandwidth cost is normally measured by its utilization of tree links along with the quality of service (QoS) measures such as delay constraint and end-to-end delay. However, if nodes are allowed to join or leave the multicast group at any time during the lifetime of the multicast connection, then the problem is known as dynamic multicast routing problem. In this article, we combine a greedy approach with static multicast routing using Tabu Search to find a low-cost dynamic multicast tree with desirable QoS parameters. The proposed algorithm is then compared with several static multicast routing algorithms. The simulation results show that, on a large number of events, i.e., where nodes are leaving or joining, the proposed algorithm is able to find multicast trees of lower cost and more desirable QoS properties.

An IPv6 Based QoS Unicast Routing Mechanism

2014 ◽  
Vol 602-605 ◽  
pp. 3071-3075
Author(s):  
Hong Chao Duan ◽  
Xing Wei Wang ◽  
Min Huang
Keyword(s):  
Routing Protocols ◽  
Future Internet ◽  
Quality Of Service Routing ◽  
Network Applications ◽  
Link State ◽  
The Core ◽  
Unicast Routing ◽  
Routing Mechanism ◽  
Service Routing

IPv6 is the core protocol of Future Internet. Thus, IPv6 based QoS (Quality of Service) routing mechanisms need to be developed to support diverse QoS requirements of new network applications. In this paper, an IPv6 based QoS unicast routing mechanism is proposed based on OSPFv3 and BGP4 +. By introducing QoS state message and extending the link state database and link state advertisement, the collection, storage and release of intra-domain QoS information are realized. By introducing NLRI (Network Layer Reachability Information) which supports QoS, the release and revocation of inter-domain QoS routing information are realized. The mechanism not only provides QoS support, but also is compatible with the existing routing protocols. It can be quickly and effectively deployed in networks. A prototype implementation of the proposed mechanism is completed, which is tested under a video conference application over the CERNET2 topology. The results indicate that it is feasible and effective with good performance.

Solving Multi-Objective Multicast Routing Problem Using a New Hybrid Approach

2018 ◽  
Vol 9 (4) ◽  
pp. 22-36
Author(s):  
Mohammed Mahseur ◽  
Abdelmadjid Boukra ◽  
Yassine Meraihi
Keyword(s):  
Multicast Routing ◽  
Hybrid Approach ◽  
Bat Algorithm ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Routing Problem ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Quality Of Service Parameters

Multicast routing is the problem of finding the spanning tree of a set of destinations whose roots are the source node and its leaves are the set of destination nodes by optimizing a set of quality of service parameters and satisfying a set of transmission constraints. This article proposes a new hybrid multicast algorithm called Hybrid Multi-objective Multicast Algorithm (HMMA) based on the Strength Pareto Evolutionary Algorithm (SPEA) to evaluate and classify the population in dominated solutions and non-dominated solutions. Dominated solutions are evolved by the Bat Algorithm, and non-dominated solutions are evolved by the Firefly Algorithm. Old and weak solutions are replaced by new random solutions by a process of mutation. The simulation results demonstrate that the proposed algorithm is able to find good Pareto optimal solutions compared to other algorithms.

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