Abstract
We propose a multi-objective Pareto-optimal technique using Genetic Algorithm (GA) for group communication, which determines a min-cost multicast tree satisfying end-to-end delay, jitter, packet loss rate and blocking probability constraints. The model incorporates a fuzzy-based selection technique for initialization of QoS parameter values at each instance of multicasting. The simulation results show that the proposed algorithm satisfies on-demand QoS requirements (like high availability, good load balancing and fault-tolerance) made by the hosts in varying topology and bursty data traffic in multimedia communication networks.
Highlights
Multicast services have been used for real-time multimedia applications to transport audio-video frames, among a group of users
The range of network parameters and Genetic Algorithm (GA) parameters considered for evaluating Quality of Service (QoS) parameters are represented in Table 1 and Table 2 respectively
We have proposed a multi-objective multicast model for wireless ad-hoc network using GA
Summary
Multicast services have been used for real-time multimedia applications to transport audio-video frames, among a group of users. During real-time communication the related audio-video frames are required to be delivered at the end nodes in a synchronous manner [1]. The frequent change of service types, session timings with QoS requirements by the group members increases the communication complexity of the network [2,3]. It is important to keep the network live with all possible satisfactions to the users during that period Such a scenario requires multi-objective optimizations with constraints satisfactions [4,5]. Development of multi-objective optimization algorithm for multi-rate traffic during multicasting is a challenge for efficient allocation of resources in a dynamically changing network [7,8]. A Pareto optimal algorithm can provide better results by fulfilling users’ requirement, irrespective of irrelevant transformation of parameters [4]
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