Abstract: Loads for a wireless network having a plurality of end nodes are predicted by constructing a computer data set of end-to-end pairs of the end nodes included in the network using a computer model of the network; constructing a computerized set of observables from social information about users of the network; developing a computerized learned model of predicted traffic using at least the data set and the observables; and using the computerized learned model to predict future end-to-end network traffic.

Abstract: Systems and methods for managing network congestion through detecting the closeness to network congestion. The network includes a plurality of network nodes, where each node has at least one neighboring node and each node has a buffer for a queue of packets from other nodes. The system measures queue length at a node and the node's neighboring nodes, processes the measured queue lengths to obtain patterns of fluctuations for the measured queue length. The system determines if one or more of the measured nodes are in a transition-onset status toward a phase transition point based on the obtained patterns of fluctuation and generates congestion control signals based on the determination to route network traffic away. The phase transition point corresponds to a change from a non-congestive phase of the measured nodes to a congestive phase of the measured nodes.