An Adaptive Load Balancing Strategy of Application Layer Multicasts Based on Score of Customer Satisfaction

An Adaptive Load Balancing Strategy of Application Layer Multicasts Based on Score of Customer Satisfaction

In P2P networks, due to the diversity of population density, there is an imbalance on users’ demands towards the internet resources. This paper proposes a method called “An Adaptive Load Balancing Strategy of Application Layer Based on Score of Customer Satisfaction”, ALBS-SCS for short. The method gives a score on the QoS of current networks periodically, and then gives feedback to the system, and eventually the system adjusts its services accordingly. This kind of method can truly improve user experience by providing better Multicast User Satisfaction (MUS) for the end systems of the hot spots and congestion areas, at the same time it also promotes the resource utilization rate of the non-hot spots, increases the network capacity and improves the performance of the system. The simulation experiments prove that the adaptive load balancing mechanism ALBS-SCS is better at reducing the average transmission delay and the average link pressure, thus increasing the system average MUS, and improving the overall performance and the user experience.