Telecommunication networks: protocols, modeling and analysis
Telecommunication networks: protocols, modeling and analysis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computer Networks
A GA-based Multi-purpose Optimization Algorithm for QoS Routing
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Concepts of exact QoS routing algorithms
IEEE/ACM Transactions on Networking (TON)
Multiple path routing algorithm for IP networks
Computer Communications
Dynamic multi constraint multi path QoS routing algorithm (DMCMPRA)
WOC '08 Proceedings of the Eighth IASTED International Conference on Wireless and Optical Communications
Routing multimedia traffic with QoS guarantees
IEEE Transactions on Multimedia
QoS routing based on genetic algorithm
Computer Communications
TAMCRA: a tunable accuracy multiple constraints routing algorithm
Computer Communications
Quality-of-service routing for supporting multimedia applications
IEEE Journal on Selected Areas in Communications
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Real-time distributed multimedia applications have special requirements in terms of bandwidth, delay, delay jitter, etc. The current network, essentially being a connectionless network, provides only un-reliable, best-effort service. The data packets may follow different paths to the destination. The network resources, viz., switch buffer and link bandwidth, are fairly shared by packets from different sessions. This type of network is best suitable for applications such as email, ftp, etc., but not suitable for real time multimedia applications such as video-on-demand, video conferencing, Internet telephony, etc., as these applications require different QoS requirements from the underlying network. In order to make the network suitable for such applications many QoS routing algorithms have been proposed. The problem of finding a path with multiple constraints is a NP-complete problem. Hence, various heuristics have been proposed to find a path that satisfies more than one constraint. All these heuristics do not consider the queuing situation at a given node. An alternate path with less queue occupancy may be more convenient to use than the optimum path when a long queue is present in nodes along the path. To search all feasible paths in less time, many researchers have used the concept of Genetic Algorithm (GA), which is a new computational strategy inspired by natural processes. This GA approach has been used in the existing Dynamic Multi Constraint Multi Path QoS Routing Algorithm (DMCMPRA) [23] for IP networks to find k feasible paths satisfying the multi constraint requirement of the multi media applications. Computer simulations show that GA-DMCMPRA takes less time for path computation when compared to Dynamic Multi Constraint Multi Path QoS Routing Algorithm (DMCMPRA). Further it is also proved that the performance of GA-DMCMPRA with respect to percentage of Packets received and average Delay experienced by packets is at par with DMCMPRA.