Computer networks exist to provide a communication medium for social networks, and information from social networks can help in estimating their communication needs. Despite this, current network management ignores the information from social networks. On the other hand, due to their limited and fluctuating bandwidth, mobile ad hoc networks are inherently resource-constrained. As traffic load increases, we need to decide when and how to throttle the traffic to maximize user satisfaction while keeping the network operational. The state-of-the-art for making these decisions is based on network measurements and so employs a reactive approach to deteriorating network state by reducing the amount of traffic admitted into the network. However, a better approach is to avoid congestion before it occurs, by (a) monitoring the network for early onset signals of congestive phase transition, and (b) predicting future network traffic using user and application information from the overlaying social network. We use machine learning methods to predict the amount of traffic load that can be admitted without transitioning the network to a congestive phase and to predict the source and destination of near future traffic load. These two predictions when fed into an admission control component ensure better management of constrained network resources while maximizing the quality of user experience.
['Akshay Vashist', 'Siun-Chuon Mau', 'Alexander Poylisher', 'Ritu Chadha', 'Abhrajit Ghosh']
Leveraging social network for predicting demand and estimating available resources for communication network management