Multi-Constrained QoS Routing Optimization And Simulation

With the fast development of high-speed network technique and multimedia technique, people are more and more putting forward the comprehensive request of service including multimedia communication. The traditional packet switching network, such as Internet, is designed for non real time data communication, only provides "best effort" service, this means it can try its best to transmit user's datagram, but not provide any assurance at bandwidth and delay etc. aspect. Such service is very suitable for traditional application, however, for new emergence of real time and distributed multimedia application it is beyond all bearing. Providing high level quality of service assurance at new generation network has become main task of current network research.The research in these years shows that the algorithm of network routing plays an important part in providing quality of service guarantees. The study of QoS routing has become a very important study direction in research field of QoS. Considering that network model based multiple constraints can reflect QoS routing problem in practice more accurately, with the increase of quality of service demand and the enlargement of network scale, the research of QoS routing algorithm with multiple constraints is of very important research significance to achieve good network quality of service and high efficiency in resource utilization. In this paper, we mainly study QoS routing algorithms based multiple constraints and relative technologies.First, this paper deeply analyzes QoS routing, routing policy and algorithm , and introduces the research status of routing algorithms with multiple constraints. Then the routing problem and model with multiple constraints are discussed in the paper.The Ad Hoc network has attracted more and more attention with its good performance and special application. Generally, multi-constrained QoS routing is an NP-Complete problem. The drawbacks of the ad hoc network—poor quality of network links, frequent topology change and low capacity, make it more complicated for the QoS guarantee because the time and information needed for the algorithm to search path is relatively limited. Usually heuristic algorithms are taken to solve this problem. Among the available heuristic algorithms, the ant colony optimization (ACO) has been widely applied because of its good features of being robust, parallel, flexible, demanding no artificial interference and accurate rate of solution. However, when it is used in large-scale optimization programs, it converges slowly in the beginning, and is prone to fall into local optimization. This is the main drawback of the algorithm. Many researchers have been studying to modify the algorithm in order to overcome this drawback. But due to the complexity of network environment, most modifications are not suitable to solve multi-constrained QoS routing optimization problem.We propose a modified ant colony optimization based on the orientation factor to solve multi-constrained QoS routing problem by recurring to GPS location. This algorithm employs the orientation factor to adjust the search act of ants and update pheromones according to objective function values, thus guaranteeing the speed and efficiency of the search, and avoiding falling into local optimization. Simulation results indicate that the algorithm proposed in this paper has good performance in computing speed and reduction of control information amount, thus saves energy spending.Besides, this paper introduces cross entropy method to solve multi-constrained QoS routing problem. The cross-entropy method, initially proposed by Rubinstein, is used in the field of random simulation to estimate the probability of rare events, which evolves later as a powerful tool for combined optimization. Because of well global searching ability and systemic mathematics frame, it has gained more and more application. In this paper, we try to use the distributed cross-entropy method to solve multi-constrained Qos routing optimization. Simulation results in NS-2 environment indicate the feasibility and efficiency of it, enabling quick finding of global solution.